Gordon Smyth
| Snapshot Date: 2018-10-15 16:45:08 -0400 (Mon, 15 Oct 2018) |
| URL: https://git.bioconductor.org/packages/limma |
| Branch: RELEASE_3_7 |
| Last Commit: 3148d1c |
| Last Changed Date: 2018-09-20 05:59:08 -0400 (Thu, 20 Sep 2018) |
| Back to Multiple platform build/check report for BioC 3.7 |
|
This page was generated on 2018-10-17 08:46:04 -0400 (Wed, 17 Oct 2018).
| Package 786/1561 | Hostname | OS / Arch | INSTALL | BUILD | CHECK | BUILD BIN | ||||||
| limma 3.36.5 Gordon Smyth
| malbec2 | Linux (Ubuntu 16.04.1 LTS) / x86_64 | OK | OK | OK | | ||||||
| tokay2 | Windows Server 2012 R2 Standard / x64 | OK | OK | OK | OK | | ||||||
| merida2 | OS X 10.11.6 El Capitan / x86_64 | OK | OK | [ OK ] | OK | |
| Package: limma |
| Version: 3.36.5 |
| Command: /Library/Frameworks/R.framework/Versions/Current/Resources/bin/R CMD check --install=check:limma.install-out.txt --library=/Library/Frameworks/R.framework/Versions/Current/Resources/library --no-vignettes --timings limma_3.36.5.tar.gz |
| StartedAt: 2018-10-16 22:15:24 -0400 (Tue, 16 Oct 2018) |
| EndedAt: 2018-10-16 22:17:00 -0400 (Tue, 16 Oct 2018) |
| EllapsedTime: 96.0 seconds |
| RetCode: 0 |
| Status: OK |
| CheckDir: limma.Rcheck |
| Warnings: 0 |
############################################################################## ############################################################################## ### ### Running command: ### ### /Library/Frameworks/R.framework/Versions/Current/Resources/bin/R CMD check --install=check:limma.install-out.txt --library=/Library/Frameworks/R.framework/Versions/Current/Resources/library --no-vignettes --timings limma_3.36.5.tar.gz ### ############################################################################## ############################################################################## * using log directory ‘/Users/biocbuild/bbs-3.7-bioc/meat/limma.Rcheck’ * using R version 3.5.1 Patched (2018-07-12 r74967) * using platform: x86_64-apple-darwin15.6.0 (64-bit) * using session charset: UTF-8 * using option ‘--no-vignettes’ * checking for file ‘limma/DESCRIPTION’ ... OK * this is package ‘limma’ version ‘3.36.5’ * checking package namespace information ... OK * checking package dependencies ... OK * checking if this is a source package ... OK * checking if there is a namespace ... OK * checking for hidden files and directories ... OK * checking for portable file names ... OK * checking for sufficient/correct file permissions ... OK * checking whether package ‘limma’ can be installed ... OK * checking installed package size ... OK * checking package directory ... OK * checking ‘build’ directory ... OK * checking DESCRIPTION meta-information ... OK * checking top-level files ... OK * checking for left-over files ... OK * checking index information ... OK * checking package subdirectories ... OK * checking R files for non-ASCII characters ... OK * checking R files for syntax errors ... OK * checking whether the package can be loaded ... OK * checking whether the package can be loaded with stated dependencies ... OK * checking whether the package can be unloaded cleanly ... OK * checking whether the namespace can be loaded with stated dependencies ... OK * checking whether the namespace can be unloaded cleanly ... OK * checking dependencies in R code ... OK * checking S3 generic/method consistency ... OK * checking replacement functions ... OK * checking foreign function calls ... OK * checking R code for possible problems ... OK * checking Rd files ... OK * checking Rd metadata ... OK * checking Rd cross-references ... OK * checking for missing documentation entries ... OK * checking for code/documentation mismatches ... OK * checking Rd \usage sections ... OK * checking Rd contents ... OK * checking for unstated dependencies in examples ... OK * checking line endings in C/C++/Fortran sources/headers ... OK * checking compiled code ... NOTE Note: information on .o files is not available * checking sizes of PDF files under ‘inst/doc’ ... OK * checking installed files from ‘inst/doc’ ... OK * checking files in ‘vignettes’ ... OK * checking examples ... OK * checking for unstated dependencies in ‘tests’ ... OK * checking tests ... Running ‘limma-Tests.R’ Comparing ‘limma-Tests.Rout’ to ‘limma-Tests.Rout.save’ ... OK OK * checking for unstated dependencies in vignettes ... OK * checking package vignettes in ‘inst/doc’ ... OK * checking running R code from vignettes ... SKIPPED * checking re-building of vignette outputs ... SKIPPED * checking PDF version of manual ... OK * DONE Status: 1 NOTE See ‘/Users/biocbuild/bbs-3.7-bioc/meat/limma.Rcheck/00check.log’ for details.
limma.Rcheck/00install.out
############################################################################## ############################################################################## ### ### Running command: ### ### /Library/Frameworks/R.framework/Versions/Current/Resources/bin/R CMD INSTALL limma ### ############################################################################## ############################################################################## * installing to library ‘/Library/Frameworks/R.framework/Versions/3.5/Resources/library’ * installing *source* package ‘limma’ ... ** libs clang -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG -I/usr/local/include -fPIC -Wall -g -O2 -c init.c -o init.o clang -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG -I/usr/local/include -fPIC -Wall -g -O2 -c normexp.c -o normexp.o clang -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG -I/usr/local/include -fPIC -Wall -g -O2 -c weighted_lowess.c -o weighted_lowess.o clang -dynamiclib -Wl,-headerpad_max_install_names -undefined dynamic_lookup -single_module -multiply_defined suppress -L/Library/Frameworks/R.framework/Resources/lib -L/usr/local/lib -o limma.so init.o normexp.o weighted_lowess.o -F/Library/Frameworks/R.framework/.. -framework R -Wl,-framework -Wl,CoreFoundation installing to /Library/Frameworks/R.framework/Versions/3.5/Resources/library/limma/libs ** R ** inst ** byte-compile and prepare package for lazy loading ** help *** installing help indices ** building package indices ** installing vignettes ‘intro.Rnw’ ** testing if installed package can be loaded * DONE (limma)
limma.Rcheck/tests/limma-Tests.Rout
R version 3.5.1 Patched (2018-07-12 r74967) -- "Feather Spray"
Copyright (C) 2018 The R Foundation for Statistical Computing
Platform: x86_64-apple-darwin15.6.0 (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(limma)
>
> set.seed(0); u <- runif(100)
>
> ### strsplit2
>
> x <- c("ab;cd;efg","abc;def","z","")
> strsplit2(x,split=";")
[,1] [,2] [,3]
[1,] "ab" "cd" "efg"
[2,] "abc" "def" ""
[3,] "z" "" ""
[4,] "" "" ""
>
> ### removeext
>
> removeExt(c("slide1.spot","slide.2.spot"))
[1] "slide1" "slide.2"
> removeExt(c("slide1.spot","slide"))
[1] "slide1.spot" "slide"
>
> ### printorder
>
> printorder(list(ngrid.r=4,ngrid.c=4,nspot.r=8,nspot.c=6),ndups=2,start="topright",npins=4)
$printorder
[1] 6 5 4 3 2 1 12 11 10 9 8 7 18 17 16 15 14 13
[19] 24 23 22 21 20 19 30 29 28 27 26 25 36 35 34 33 32 31
[37] 42 41 40 39 38 37 48 47 46 45 44 43 6 5 4 3 2 1
[55] 12 11 10 9 8 7 18 17 16 15 14 13 24 23 22 21 20 19
[73] 30 29 28 27 26 25 36 35 34 33 32 31 42 41 40 39 38 37
[91] 48 47 46 45 44 43 6 5 4 3 2 1 12 11 10 9 8 7
[109] 18 17 16 15 14 13 24 23 22 21 20 19 30 29 28 27 26 25
[127] 36 35 34 33 32 31 42 41 40 39 38 37 48 47 46 45 44 43
[145] 6 5 4 3 2 1 12 11 10 9 8 7 18 17 16 15 14 13
[163] 24 23 22 21 20 19 30 29 28 27 26 25 36 35 34 33 32 31
[181] 42 41 40 39 38 37 48 47 46 45 44 43 54 53 52 51 50 49
[199] 60 59 58 57 56 55 66 65 64 63 62 61 72 71 70 69 68 67
[217] 78 77 76 75 74 73 84 83 82 81 80 79 90 89 88 87 86 85
[235] 96 95 94 93 92 91 54 53 52 51 50 49 60 59 58 57 56 55
[253] 66 65 64 63 62 61 72 71 70 69 68 67 78 77 76 75 74 73
[271] 84 83 82 81 80 79 90 89 88 87 86 85 96 95 94 93 92 91
[289] 54 53 52 51 50 49 60 59 58 57 56 55 66 65 64 63 62 61
[307] 72 71 70 69 68 67 78 77 76 75 74 73 84 83 82 81 80 79
[325] 90 89 88 87 86 85 96 95 94 93 92 91 54 53 52 51 50 49
[343] 60 59 58 57 56 55 66 65 64 63 62 61 72 71 70 69 68 67
[361] 78 77 76 75 74 73 84 83 82 81 80 79 90 89 88 87 86 85
[379] 96 95 94 93 92 91 102 101 100 99 98 97 108 107 106 105 104 103
[397] 114 113 112 111 110 109 120 119 118 117 116 115 126 125 124 123 122 121
[415] 132 131 130 129 128 127 138 137 136 135 134 133 144 143 142 141 140 139
[433] 102 101 100 99 98 97 108 107 106 105 104 103 114 113 112 111 110 109
[451] 120 119 118 117 116 115 126 125 124 123 122 121 132 131 130 129 128 127
[469] 138 137 136 135 134 133 144 143 142 141 140 139 102 101 100 99 98 97
[487] 108 107 106 105 104 103 114 113 112 111 110 109 120 119 118 117 116 115
[505] 126 125 124 123 122 121 132 131 130 129 128 127 138 137 136 135 134 133
[523] 144 143 142 141 140 139 102 101 100 99 98 97 108 107 106 105 104 103
[541] 114 113 112 111 110 109 120 119 118 117 116 115 126 125 124 123 122 121
[559] 132 131 130 129 128 127 138 137 136 135 134 133 144 143 142 141 140 139
[577] 150 149 148 147 146 145 156 155 154 153 152 151 162 161 160 159 158 157
[595] 168 167 166 165 164 163 174 173 172 171 170 169 180 179 178 177 176 175
[613] 186 185 184 183 182 181 192 191 190 189 188 187 150 149 148 147 146 145
[631] 156 155 154 153 152 151 162 161 160 159 158 157 168 167 166 165 164 163
[649] 174 173 172 171 170 169 180 179 178 177 176 175 186 185 184 183 182 181
[667] 192 191 190 189 188 187 150 149 148 147 146 145 156 155 154 153 152 151
[685] 162 161 160 159 158 157 168 167 166 165 164 163 174 173 172 171 170 169
[703] 180 179 178 177 176 175 186 185 184 183 182 181 192 191 190 189 188 187
[721] 150 149 148 147 146 145 156 155 154 153 152 151 162 161 160 159 158 157
[739] 168 167 166 165 164 163 174 173 172 171 170 169 180 179 178 177 176 175
[757] 186 185 184 183 182 181 192 191 190 189 188 187
$plate
[1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[149] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[186] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[223] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[260] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[297] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[334] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[371] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[408] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[445] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[482] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[519] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[556] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[593] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[630] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[667] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[704] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[741] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
$plate.r
[1] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[26] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 3 3
[51] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[76] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2
[101] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[126] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1
[151] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[176] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 8 8 8 8 8 8 8 8
[201] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8
[226] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 7 7 7 7 7 7 7 7 7 7
[251] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
[276] 7 7 7 7 7 7 7 7 7 7 7 7 7 6 6 6 6 6 6 6 6 6 6 6 6
[301] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
[326] 6 6 6 6 6 6 6 6 6 6 6 5 5 5 5 5 5 5 5 5 5 5 5 5 5
[351] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
[376] 5 5 5 5 5 5 5 5 5 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12
[401] 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12
[426] 12 12 12 12 12 12 12 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
[451] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
[476] 11 11 11 11 11 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
[501] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
[526] 10 10 10 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
[551] 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
[576] 9 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16
[601] 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 15
[626] 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15
[651] 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 14 14 14
[676] 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14
[701] 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 13 13 13 13 13
[726] 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13
[751] 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13
$plate.c
[1] 3 3 2 2 1 1 6 6 5 5 4 4 9 9 8 8 7 7 12 12 11 11 10 10 15
[26] 15 14 14 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22 3 3
[51] 2 2 1 1 6 6 5 5 4 4 9 9 8 8 7 7 12 12 11 11 10 10 15 15 14
[76] 14 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22 3 3 2 2
[101] 1 1 6 6 5 5 4 4 9 9 8 8 7 7 12 12 11 11 10 10 15 15 14 14 13
[126] 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22 3 3 2 2 1 1
[151] 6 6 5 5 4 4 9 9 8 8 7 7 12 12 11 11 10 10 15 15 14 14 13 13 18
[176] 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22 3 3 2 2 1 1 6 6
[201] 5 5 4 4 9 9 8 8 7 7 12 12 11 11 10 10 15 15 14 14 13 13 18 18 17
[226] 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22 3 3 2 2 1 1 6 6 5 5
[251] 4 4 9 9 8 8 7 7 12 12 11 11 10 10 15 15 14 14 13 13 18 18 17 17 16
[276] 16 21 21 20 20 19 19 24 24 23 23 22 22 3 3 2 2 1 1 6 6 5 5 4 4
[301] 9 9 8 8 7 7 12 12 11 11 10 10 15 15 14 14 13 13 18 18 17 17 16 16 21
[326] 21 20 20 19 19 24 24 23 23 22 22 3 3 2 2 1 1 6 6 5 5 4 4 9 9
[351] 8 8 7 7 12 12 11 11 10 10 15 15 14 14 13 13 18 18 17 17 16 16 21 21 20
[376] 20 19 19 24 24 23 23 22 22 3 3 2 2 1 1 6 6 5 5 4 4 9 9 8 8
[401] 7 7 12 12 11 11 10 10 15 15 14 14 13 13 18 18 17 17 16 16 21 21 20 20 19
[426] 19 24 24 23 23 22 22 3 3 2 2 1 1 6 6 5 5 4 4 9 9 8 8 7 7
[451] 12 12 11 11 10 10 15 15 14 14 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24
[476] 24 23 23 22 22 3 3 2 2 1 1 6 6 5 5 4 4 9 9 8 8 7 7 12 12
[501] 11 11 10 10 15 15 14 14 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23
[526] 23 22 22 3 3 2 2 1 1 6 6 5 5 4 4 9 9 8 8 7 7 12 12 11 11
[551] 10 10 15 15 14 14 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22
[576] 22 3 3 2 2 1 1 6 6 5 5 4 4 9 9 8 8 7 7 12 12 11 11 10 10
[601] 15 15 14 14 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22 3
[626] 3 2 2 1 1 6 6 5 5 4 4 9 9 8 8 7 7 12 12 11 11 10 10 15 15
[651] 14 14 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22 3 3 2
[676] 2 1 1 6 6 5 5 4 4 9 9 8 8 7 7 12 12 11 11 10 10 15 15 14 14
[701] 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22 3 3 2 2 1
[726] 1 6 6 5 5 4 4 9 9 8 8 7 7 12 12 11 11 10 10 15 15 14 14 13 13
[751] 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22
$plateposition
[1] "p1D03" "p1D03" "p1D02" "p1D02" "p1D01" "p1D01" "p1D06" "p1D06" "p1D05"
[10] "p1D05" "p1D04" "p1D04" "p1D09" "p1D09" "p1D08" "p1D08" "p1D07" "p1D07"
[19] "p1D12" "p1D12" "p1D11" "p1D11" "p1D10" "p1D10" "p1D15" "p1D15" "p1D14"
[28] "p1D14" "p1D13" "p1D13" "p1D18" "p1D18" "p1D17" "p1D17" "p1D16" "p1D16"
[37] "p1D21" "p1D21" "p1D20" "p1D20" "p1D19" "p1D19" "p1D24" "p1D24" "p1D23"
[46] "p1D23" "p1D22" "p1D22" "p1C03" "p1C03" "p1C02" "p1C02" "p1C01" "p1C01"
[55] "p1C06" "p1C06" "p1C05" "p1C05" "p1C04" "p1C04" "p1C09" "p1C09" "p1C08"
[64] "p1C08" "p1C07" "p1C07" "p1C12" "p1C12" "p1C11" "p1C11" "p1C10" "p1C10"
[73] "p1C15" "p1C15" "p1C14" "p1C14" "p1C13" "p1C13" "p1C18" "p1C18" "p1C17"
[82] "p1C17" "p1C16" "p1C16" "p1C21" "p1C21" "p1C20" "p1C20" "p1C19" "p1C19"
[91] "p1C24" "p1C24" "p1C23" "p1C23" "p1C22" "p1C22" "p1B03" "p1B03" "p1B02"
[100] "p1B02" "p1B01" "p1B01" "p1B06" "p1B06" "p1B05" "p1B05" "p1B04" "p1B04"
[109] "p1B09" "p1B09" "p1B08" "p1B08" "p1B07" "p1B07" "p1B12" "p1B12" "p1B11"
[118] "p1B11" "p1B10" "p1B10" "p1B15" "p1B15" "p1B14" "p1B14" "p1B13" "p1B13"
[127] "p1B18" "p1B18" "p1B17" "p1B17" "p1B16" "p1B16" "p1B21" "p1B21" "p1B20"
[136] "p1B20" "p1B19" "p1B19" "p1B24" "p1B24" "p1B23" "p1B23" "p1B22" "p1B22"
[145] "p1A03" "p1A03" "p1A02" "p1A02" "p1A01" "p1A01" "p1A06" "p1A06" "p1A05"
[154] "p1A05" "p1A04" "p1A04" "p1A09" "p1A09" "p1A08" "p1A08" "p1A07" "p1A07"
[163] "p1A12" "p1A12" "p1A11" "p1A11" "p1A10" "p1A10" "p1A15" "p1A15" "p1A14"
[172] "p1A14" "p1A13" "p1A13" "p1A18" "p1A18" "p1A17" "p1A17" "p1A16" "p1A16"
[181] "p1A21" "p1A21" "p1A20" "p1A20" "p1A19" "p1A19" "p1A24" "p1A24" "p1A23"
[190] "p1A23" "p1A22" "p1A22" "p1H03" "p1H03" "p1H02" "p1H02" "p1H01" "p1H01"
[199] "p1H06" "p1H06" "p1H05" "p1H05" "p1H04" "p1H04" "p1H09" "p1H09" "p1H08"
[208] "p1H08" "p1H07" "p1H07" "p1H12" "p1H12" "p1H11" "p1H11" "p1H10" "p1H10"
[217] "p1H15" "p1H15" "p1H14" "p1H14" "p1H13" "p1H13" "p1H18" "p1H18" "p1H17"
[226] "p1H17" "p1H16" "p1H16" "p1H21" "p1H21" "p1H20" "p1H20" "p1H19" "p1H19"
[235] "p1H24" "p1H24" "p1H23" "p1H23" "p1H22" "p1H22" "p1G03" "p1G03" "p1G02"
[244] "p1G02" "p1G01" "p1G01" "p1G06" "p1G06" "p1G05" "p1G05" "p1G04" "p1G04"
[253] "p1G09" "p1G09" "p1G08" "p1G08" "p1G07" "p1G07" "p1G12" "p1G12" "p1G11"
[262] "p1G11" "p1G10" "p1G10" "p1G15" "p1G15" "p1G14" "p1G14" "p1G13" "p1G13"
[271] "p1G18" "p1G18" "p1G17" "p1G17" "p1G16" "p1G16" "p1G21" "p1G21" "p1G20"
[280] "p1G20" "p1G19" "p1G19" "p1G24" "p1G24" "p1G23" "p1G23" "p1G22" "p1G22"
[289] "p1F03" "p1F03" "p1F02" "p1F02" "p1F01" "p1F01" "p1F06" "p1F06" "p1F05"
[298] "p1F05" "p1F04" "p1F04" "p1F09" "p1F09" "p1F08" "p1F08" "p1F07" "p1F07"
[307] "p1F12" "p1F12" "p1F11" "p1F11" "p1F10" "p1F10" "p1F15" "p1F15" "p1F14"
[316] "p1F14" "p1F13" "p1F13" "p1F18" "p1F18" "p1F17" "p1F17" "p1F16" "p1F16"
[325] "p1F21" "p1F21" "p1F20" "p1F20" "p1F19" "p1F19" "p1F24" "p1F24" "p1F23"
[334] "p1F23" "p1F22" "p1F22" "p1E03" "p1E03" "p1E02" "p1E02" "p1E01" "p1E01"
[343] "p1E06" "p1E06" "p1E05" "p1E05" "p1E04" "p1E04" "p1E09" "p1E09" "p1E08"
[352] "p1E08" "p1E07" "p1E07" "p1E12" "p1E12" "p1E11" "p1E11" "p1E10" "p1E10"
[361] "p1E15" "p1E15" "p1E14" "p1E14" "p1E13" "p1E13" "p1E18" "p1E18" "p1E17"
[370] "p1E17" "p1E16" "p1E16" "p1E21" "p1E21" "p1E20" "p1E20" "p1E19" "p1E19"
[379] "p1E24" "p1E24" "p1E23" "p1E23" "p1E22" "p1E22" "p1L03" "p1L03" "p1L02"
[388] "p1L02" "p1L01" "p1L01" "p1L06" "p1L06" "p1L05" "p1L05" "p1L04" "p1L04"
[397] "p1L09" "p1L09" "p1L08" "p1L08" "p1L07" "p1L07" "p1L12" "p1L12" "p1L11"
[406] "p1L11" "p1L10" "p1L10" "p1L15" "p1L15" "p1L14" "p1L14" "p1L13" "p1L13"
[415] "p1L18" "p1L18" "p1L17" "p1L17" "p1L16" "p1L16" "p1L21" "p1L21" "p1L20"
[424] "p1L20" "p1L19" "p1L19" "p1L24" "p1L24" "p1L23" "p1L23" "p1L22" "p1L22"
[433] "p1K03" "p1K03" "p1K02" "p1K02" "p1K01" "p1K01" "p1K06" "p1K06" "p1K05"
[442] "p1K05" "p1K04" "p1K04" "p1K09" "p1K09" "p1K08" "p1K08" "p1K07" "p1K07"
[451] "p1K12" "p1K12" "p1K11" "p1K11" "p1K10" "p1K10" "p1K15" "p1K15" "p1K14"
[460] "p1K14" "p1K13" "p1K13" "p1K18" "p1K18" "p1K17" "p1K17" "p1K16" "p1K16"
[469] "p1K21" "p1K21" "p1K20" "p1K20" "p1K19" "p1K19" "p1K24" "p1K24" "p1K23"
[478] "p1K23" "p1K22" "p1K22" "p1J03" "p1J03" "p1J02" "p1J02" "p1J01" "p1J01"
[487] "p1J06" "p1J06" "p1J05" "p1J05" "p1J04" "p1J04" "p1J09" "p1J09" "p1J08"
[496] "p1J08" "p1J07" "p1J07" "p1J12" "p1J12" "p1J11" "p1J11" "p1J10" "p1J10"
[505] "p1J15" "p1J15" "p1J14" "p1J14" "p1J13" "p1J13" "p1J18" "p1J18" "p1J17"
[514] "p1J17" "p1J16" "p1J16" "p1J21" "p1J21" "p1J20" "p1J20" "p1J19" "p1J19"
[523] "p1J24" "p1J24" "p1J23" "p1J23" "p1J22" "p1J22" "p1I03" "p1I03" "p1I02"
[532] "p1I02" "p1I01" "p1I01" "p1I06" "p1I06" "p1I05" "p1I05" "p1I04" "p1I04"
[541] "p1I09" "p1I09" "p1I08" "p1I08" "p1I07" "p1I07" "p1I12" "p1I12" "p1I11"
[550] "p1I11" "p1I10" "p1I10" "p1I15" "p1I15" "p1I14" "p1I14" "p1I13" "p1I13"
[559] "p1I18" "p1I18" "p1I17" "p1I17" "p1I16" "p1I16" "p1I21" "p1I21" "p1I20"
[568] "p1I20" "p1I19" "p1I19" "p1I24" "p1I24" "p1I23" "p1I23" "p1I22" "p1I22"
[577] "p1P03" "p1P03" "p1P02" "p1P02" "p1P01" "p1P01" "p1P06" "p1P06" "p1P05"
[586] "p1P05" "p1P04" "p1P04" "p1P09" "p1P09" "p1P08" "p1P08" "p1P07" "p1P07"
[595] "p1P12" "p1P12" "p1P11" "p1P11" "p1P10" "p1P10" "p1P15" "p1P15" "p1P14"
[604] "p1P14" "p1P13" "p1P13" "p1P18" "p1P18" "p1P17" "p1P17" "p1P16" "p1P16"
[613] "p1P21" "p1P21" "p1P20" "p1P20" "p1P19" "p1P19" "p1P24" "p1P24" "p1P23"
[622] "p1P23" "p1P22" "p1P22" "p1O03" "p1O03" "p1O02" "p1O02" "p1O01" "p1O01"
[631] "p1O06" "p1O06" "p1O05" "p1O05" "p1O04" "p1O04" "p1O09" "p1O09" "p1O08"
[640] "p1O08" "p1O07" "p1O07" "p1O12" "p1O12" "p1O11" "p1O11" "p1O10" "p1O10"
[649] "p1O15" "p1O15" "p1O14" "p1O14" "p1O13" "p1O13" "p1O18" "p1O18" "p1O17"
[658] "p1O17" "p1O16" "p1O16" "p1O21" "p1O21" "p1O20" "p1O20" "p1O19" "p1O19"
[667] "p1O24" "p1O24" "p1O23" "p1O23" "p1O22" "p1O22" "p1N03" "p1N03" "p1N02"
[676] "p1N02" "p1N01" "p1N01" "p1N06" "p1N06" "p1N05" "p1N05" "p1N04" "p1N04"
[685] "p1N09" "p1N09" "p1N08" "p1N08" "p1N07" "p1N07" "p1N12" "p1N12" "p1N11"
[694] "p1N11" "p1N10" "p1N10" "p1N15" "p1N15" "p1N14" "p1N14" "p1N13" "p1N13"
[703] "p1N18" "p1N18" "p1N17" "p1N17" "p1N16" "p1N16" "p1N21" "p1N21" "p1N20"
[712] "p1N20" "p1N19" "p1N19" "p1N24" "p1N24" "p1N23" "p1N23" "p1N22" "p1N22"
[721] "p1M03" "p1M03" "p1M02" "p1M02" "p1M01" "p1M01" "p1M06" "p1M06" "p1M05"
[730] "p1M05" "p1M04" "p1M04" "p1M09" "p1M09" "p1M08" "p1M08" "p1M07" "p1M07"
[739] "p1M12" "p1M12" "p1M11" "p1M11" "p1M10" "p1M10" "p1M15" "p1M15" "p1M14"
[748] "p1M14" "p1M13" "p1M13" "p1M18" "p1M18" "p1M17" "p1M17" "p1M16" "p1M16"
[757] "p1M21" "p1M21" "p1M20" "p1M20" "p1M19" "p1M19" "p1M24" "p1M24" "p1M23"
[766] "p1M23" "p1M22" "p1M22"
> printorder(list(ngrid.r=4,ngrid.c=4,nspot.r=8,nspot.c=6))
$printorder
[1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
[26] 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 1 2
[51] 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27
[76] 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 1 2 3 4
[101] 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29
[126] 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 1 2 3 4 5 6
[151] 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31
[176] 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 1 2 3 4 5 6 7 8
[201] 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33
[226] 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 1 2 3 4 5 6 7 8 9 10
[251] 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35
[276] 36 37 38 39 40 41 42 43 44 45 46 47 48 1 2 3 4 5 6 7 8 9 10 11 12
[301] 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37
[326] 38 39 40 41 42 43 44 45 46 47 48 1 2 3 4 5 6 7 8 9 10 11 12 13 14
[351] 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39
[376] 40 41 42 43 44 45 46 47 48 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
[401] 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41
[426] 42 43 44 45 46 47 48 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
[451] 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43
[476] 44 45 46 47 48 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
[501] 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45
[526] 46 47 48 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22
[551] 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47
[576] 48 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
[601] 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 1
[626] 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26
[651] 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 1 2 3
[676] 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
[701] 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 1 2 3 4 5
[726] 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
[751] 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48
$plate
[1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2
[38] 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2
[75] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[112] 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1
[149] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[186] 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2
[223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[260] 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1
[297] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[334] 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2
[371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[408] 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1
[445] 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1
[482] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[519] 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2
[556] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[593] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1
[630] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[667] 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2
[704] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[741] 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
$plate.r
[1] 4 4 4 4 4 4 8 8 8 8 8 8 12 12 12 12 12 12 16 16 16 16 16 16 4
[26] 4 4 4 4 4 8 8 8 8 8 8 12 12 12 12 12 12 16 16 16 16 16 16 3 3
[51] 3 3 3 3 7 7 7 7 7 7 11 11 11 11 11 11 15 15 15 15 15 15 3 3 3
[76] 3 3 3 7 7 7 7 7 7 11 11 11 11 11 11 15 15 15 15 15 15 2 2 2 2
[101] 2 2 6 6 6 6 6 6 10 10 10 10 10 10 14 14 14 14 14 14 2 2 2 2 2
[126] 2 6 6 6 6 6 6 10 10 10 10 10 10 14 14 14 14 14 14 1 1 1 1 1 1
[151] 5 5 5 5 5 5 9 9 9 9 9 9 13 13 13 13 13 13 1 1 1 1 1 1 5
[176] 5 5 5 5 5 9 9 9 9 9 9 13 13 13 13 13 13 4 4 4 4 4 4 8 8
[201] 8 8 8 8 12 12 12 12 12 12 16 16 16 16 16 16 4 4 4 4 4 4 8 8 8
[226] 8 8 8 12 12 12 12 12 12 16 16 16 16 16 16 3 3 3 3 3 3 7 7 7 7
[251] 7 7 11 11 11 11 11 11 15 15 15 15 15 15 3 3 3 3 3 3 7 7 7 7 7
[276] 7 11 11 11 11 11 11 15 15 15 15 15 15 2 2 2 2 2 2 6 6 6 6 6 6
[301] 10 10 10 10 10 10 14 14 14 14 14 14 2 2 2 2 2 2 6 6 6 6 6 6 10
[326] 10 10 10 10 10 14 14 14 14 14 14 1 1 1 1 1 1 5 5 5 5 5 5 9 9
[351] 9 9 9 9 13 13 13 13 13 13 1 1 1 1 1 1 5 5 5 5 5 5 9 9 9
[376] 9 9 9 13 13 13 13 13 13 4 4 4 4 4 4 8 8 8 8 8 8 12 12 12 12
[401] 12 12 16 16 16 16 16 16 4 4 4 4 4 4 8 8 8 8 8 8 12 12 12 12 12
[426] 12 16 16 16 16 16 16 3 3 3 3 3 3 7 7 7 7 7 7 11 11 11 11 11 11
[451] 15 15 15 15 15 15 3 3 3 3 3 3 7 7 7 7 7 7 11 11 11 11 11 11 15
[476] 15 15 15 15 15 2 2 2 2 2 2 6 6 6 6 6 6 10 10 10 10 10 10 14 14
[501] 14 14 14 14 2 2 2 2 2 2 6 6 6 6 6 6 10 10 10 10 10 10 14 14 14
[526] 14 14 14 1 1 1 1 1 1 5 5 5 5 5 5 9 9 9 9 9 9 13 13 13 13
[551] 13 13 1 1 1 1 1 1 5 5 5 5 5 5 9 9 9 9 9 9 13 13 13 13 13
[576] 13 4 4 4 4 4 4 8 8 8 8 8 8 12 12 12 12 12 12 16 16 16 16 16 16
[601] 4 4 4 4 4 4 8 8 8 8 8 8 12 12 12 12 12 12 16 16 16 16 16 16 3
[626] 3 3 3 3 3 7 7 7 7 7 7 11 11 11 11 11 11 15 15 15 15 15 15 3 3
[651] 3 3 3 3 7 7 7 7 7 7 11 11 11 11 11 11 15 15 15 15 15 15 2 2 2
[676] 2 2 2 6 6 6 6 6 6 10 10 10 10 10 10 14 14 14 14 14 14 2 2 2 2
[701] 2 2 6 6 6 6 6 6 10 10 10 10 10 10 14 14 14 14 14 14 1 1 1 1 1
[726] 1 5 5 5 5 5 5 9 9 9 9 9 9 13 13 13 13 13 13 1 1 1 1 1 1
[751] 5 5 5 5 5 5 9 9 9 9 9 9 13 13 13 13 13 13
$plate.c
[1] 1 5 9 13 17 21 1 5 9 13 17 21 1 5 9 13 17 21 1 5 9 13 17 21 1
[26] 5 9 13 17 21 1 5 9 13 17 21 1 5 9 13 17 21 1 5 9 13 17 21 1 5
[51] 9 13 17 21 1 5 9 13 17 21 1 5 9 13 17 21 1 5 9 13 17 21 1 5 9
[76] 13 17 21 1 5 9 13 17 21 1 5 9 13 17 21 1 5 9 13 17 21 1 5 9 13
[101] 17 21 1 5 9 13 17 21 1 5 9 13 17 21 1 5 9 13 17 21 1 5 9 13 17
[126] 21 1 5 9 13 17 21 1 5 9 13 17 21 1 5 9 13 17 21 1 5 9 13 17 21
[151] 1 5 9 13 17 21 1 5 9 13 17 21 1 5 9 13 17 21 1 5 9 13 17 21 1
[176] 5 9 13 17 21 1 5 9 13 17 21 1 5 9 13 17 21 2 6 10 14 18 22 2 6
[201] 10 14 18 22 2 6 10 14 18 22 2 6 10 14 18 22 2 6 10 14 18 22 2 6 10
[226] 14 18 22 2 6 10 14 18 22 2 6 10 14 18 22 2 6 10 14 18 22 2 6 10 14
[251] 18 22 2 6 10 14 18 22 2 6 10 14 18 22 2 6 10 14 18 22 2 6 10 14 18
[276] 22 2 6 10 14 18 22 2 6 10 14 18 22 2 6 10 14 18 22 2 6 10 14 18 22
[301] 2 6 10 14 18 22 2 6 10 14 18 22 2 6 10 14 18 22 2 6 10 14 18 22 2
[326] 6 10 14 18 22 2 6 10 14 18 22 2 6 10 14 18 22 2 6 10 14 18 22 2 6
[351] 10 14 18 22 2 6 10 14 18 22 2 6 10 14 18 22 2 6 10 14 18 22 2 6 10
[376] 14 18 22 2 6 10 14 18 22 3 7 11 15 19 23 3 7 11 15 19 23 3 7 11 15
[401] 19 23 3 7 11 15 19 23 3 7 11 15 19 23 3 7 11 15 19 23 3 7 11 15 19
[426] 23 3 7 11 15 19 23 3 7 11 15 19 23 3 7 11 15 19 23 3 7 11 15 19 23
[451] 3 7 11 15 19 23 3 7 11 15 19 23 3 7 11 15 19 23 3 7 11 15 19 23 3
[476] 7 11 15 19 23 3 7 11 15 19 23 3 7 11 15 19 23 3 7 11 15 19 23 3 7
[501] 11 15 19 23 3 7 11 15 19 23 3 7 11 15 19 23 3 7 11 15 19 23 3 7 11
[526] 15 19 23 3 7 11 15 19 23 3 7 11 15 19 23 3 7 11 15 19 23 3 7 11 15
[551] 19 23 3 7 11 15 19 23 3 7 11 15 19 23 3 7 11 15 19 23 3 7 11 15 19
[576] 23 4 8 12 16 20 24 4 8 12 16 20 24 4 8 12 16 20 24 4 8 12 16 20 24
[601] 4 8 12 16 20 24 4 8 12 16 20 24 4 8 12 16 20 24 4 8 12 16 20 24 4
[626] 8 12 16 20 24 4 8 12 16 20 24 4 8 12 16 20 24 4 8 12 16 20 24 4 8
[651] 12 16 20 24 4 8 12 16 20 24 4 8 12 16 20 24 4 8 12 16 20 24 4 8 12
[676] 16 20 24 4 8 12 16 20 24 4 8 12 16 20 24 4 8 12 16 20 24 4 8 12 16
[701] 20 24 4 8 12 16 20 24 4 8 12 16 20 24 4 8 12 16 20 24 4 8 12 16 20
[726] 24 4 8 12 16 20 24 4 8 12 16 20 24 4 8 12 16 20 24 4 8 12 16 20 24
[751] 4 8 12 16 20 24 4 8 12 16 20 24 4 8 12 16 20 24
$plateposition
[1] "p1D01" "p1D05" "p1D09" "p1D13" "p1D17" "p1D21" "p1H01" "p1H05" "p1H09"
[10] "p1H13" "p1H17" "p1H21" "p1L01" "p1L05" "p1L09" "p1L13" "p1L17" "p1L21"
[19] "p1P01" "p1P05" "p1P09" "p1P13" "p1P17" "p1P21" "p2D01" "p2D05" "p2D09"
[28] "p2D13" "p2D17" "p2D21" "p2H01" "p2H05" "p2H09" "p2H13" "p2H17" "p2H21"
[37] "p2L01" "p2L05" "p2L09" "p2L13" "p2L17" "p2L21" "p2P01" "p2P05" "p2P09"
[46] "p2P13" "p2P17" "p2P21" "p1C01" "p1C05" "p1C09" "p1C13" "p1C17" "p1C21"
[55] "p1G01" "p1G05" "p1G09" "p1G13" "p1G17" "p1G21" "p1K01" "p1K05" "p1K09"
[64] "p1K13" "p1K17" "p1K21" "p1O01" "p1O05" "p1O09" "p1O13" "p1O17" "p1O21"
[73] "p2C01" "p2C05" "p2C09" "p2C13" "p2C17" "p2C21" "p2G01" "p2G05" "p2G09"
[82] "p2G13" "p2G17" "p2G21" "p2K01" "p2K05" "p2K09" "p2K13" "p2K17" "p2K21"
[91] "p2O01" "p2O05" "p2O09" "p2O13" "p2O17" "p2O21" "p1B01" "p1B05" "p1B09"
[100] "p1B13" "p1B17" "p1B21" "p1F01" "p1F05" "p1F09" "p1F13" "p1F17" "p1F21"
[109] "p1J01" "p1J05" "p1J09" "p1J13" "p1J17" "p1J21" "p1N01" "p1N05" "p1N09"
[118] "p1N13" "p1N17" "p1N21" "p2B01" "p2B05" "p2B09" "p2B13" "p2B17" "p2B21"
[127] "p2F01" "p2F05" "p2F09" "p2F13" "p2F17" "p2F21" "p2J01" "p2J05" "p2J09"
[136] "p2J13" "p2J17" "p2J21" "p2N01" "p2N05" "p2N09" "p2N13" "p2N17" "p2N21"
[145] "p1A01" "p1A05" "p1A09" "p1A13" "p1A17" "p1A21" "p1E01" "p1E05" "p1E09"
[154] "p1E13" "p1E17" "p1E21" "p1I01" "p1I05" "p1I09" "p1I13" "p1I17" "p1I21"
[163] "p1M01" "p1M05" "p1M09" "p1M13" "p1M17" "p1M21" "p2A01" "p2A05" "p2A09"
[172] "p2A13" "p2A17" "p2A21" "p2E01" "p2E05" "p2E09" "p2E13" "p2E17" "p2E21"
[181] "p2I01" "p2I05" "p2I09" "p2I13" "p2I17" "p2I21" "p2M01" "p2M05" "p2M09"
[190] "p2M13" "p2M17" "p2M21" "p1D02" "p1D06" "p1D10" "p1D14" "p1D18" "p1D22"
[199] "p1H02" "p1H06" "p1H10" "p1H14" "p1H18" "p1H22" "p1L02" "p1L06" "p1L10"
[208] "p1L14" "p1L18" "p1L22" "p1P02" "p1P06" "p1P10" "p1P14" "p1P18" "p1P22"
[217] "p2D02" "p2D06" "p2D10" "p2D14" "p2D18" "p2D22" "p2H02" "p2H06" "p2H10"
[226] "p2H14" "p2H18" "p2H22" "p2L02" "p2L06" "p2L10" "p2L14" "p2L18" "p2L22"
[235] "p2P02" "p2P06" "p2P10" "p2P14" "p2P18" "p2P22" "p1C02" "p1C06" "p1C10"
[244] "p1C14" "p1C18" "p1C22" "p1G02" "p1G06" "p1G10" "p1G14" "p1G18" "p1G22"
[253] "p1K02" "p1K06" "p1K10" "p1K14" "p1K18" "p1K22" "p1O02" "p1O06" "p1O10"
[262] "p1O14" "p1O18" "p1O22" "p2C02" "p2C06" "p2C10" "p2C14" "p2C18" "p2C22"
[271] "p2G02" "p2G06" "p2G10" "p2G14" "p2G18" "p2G22" "p2K02" "p2K06" "p2K10"
[280] "p2K14" "p2K18" "p2K22" "p2O02" "p2O06" "p2O10" "p2O14" "p2O18" "p2O22"
[289] "p1B02" "p1B06" "p1B10" "p1B14" "p1B18" "p1B22" "p1F02" "p1F06" "p1F10"
[298] "p1F14" "p1F18" "p1F22" "p1J02" "p1J06" "p1J10" "p1J14" "p1J18" "p1J22"
[307] "p1N02" "p1N06" "p1N10" "p1N14" "p1N18" "p1N22" "p2B02" "p2B06" "p2B10"
[316] "p2B14" "p2B18" "p2B22" "p2F02" "p2F06" "p2F10" "p2F14" "p2F18" "p2F22"
[325] "p2J02" "p2J06" "p2J10" "p2J14" "p2J18" "p2J22" "p2N02" "p2N06" "p2N10"
[334] "p2N14" "p2N18" "p2N22" "p1A02" "p1A06" "p1A10" "p1A14" "p1A18" "p1A22"
[343] "p1E02" "p1E06" "p1E10" "p1E14" "p1E18" "p1E22" "p1I02" "p1I06" "p1I10"
[352] "p1I14" "p1I18" "p1I22" "p1M02" "p1M06" "p1M10" "p1M14" "p1M18" "p1M22"
[361] "p2A02" "p2A06" "p2A10" "p2A14" "p2A18" "p2A22" "p2E02" "p2E06" "p2E10"
[370] "p2E14" "p2E18" "p2E22" "p2I02" "p2I06" "p2I10" "p2I14" "p2I18" "p2I22"
[379] "p2M02" "p2M06" "p2M10" "p2M14" "p2M18" "p2M22" "p1D03" "p1D07" "p1D11"
[388] "p1D15" "p1D19" "p1D23" "p1H03" "p1H07" "p1H11" "p1H15" "p1H19" "p1H23"
[397] "p1L03" "p1L07" "p1L11" "p1L15" "p1L19" "p1L23" "p1P03" "p1P07" "p1P11"
[406] "p1P15" "p1P19" "p1P23" "p2D03" "p2D07" "p2D11" "p2D15" "p2D19" "p2D23"
[415] "p2H03" "p2H07" "p2H11" "p2H15" "p2H19" "p2H23" "p2L03" "p2L07" "p2L11"
[424] "p2L15" "p2L19" "p2L23" "p2P03" "p2P07" "p2P11" "p2P15" "p2P19" "p2P23"
[433] "p1C03" "p1C07" "p1C11" "p1C15" "p1C19" "p1C23" "p1G03" "p1G07" "p1G11"
[442] "p1G15" "p1G19" "p1G23" "p1K03" "p1K07" "p1K11" "p1K15" "p1K19" "p1K23"
[451] "p1O03" "p1O07" "p1O11" "p1O15" "p1O19" "p1O23" "p2C03" "p2C07" "p2C11"
[460] "p2C15" "p2C19" "p2C23" "p2G03" "p2G07" "p2G11" "p2G15" "p2G19" "p2G23"
[469] "p2K03" "p2K07" "p2K11" "p2K15" "p2K19" "p2K23" "p2O03" "p2O07" "p2O11"
[478] "p2O15" "p2O19" "p2O23" "p1B03" "p1B07" "p1B11" "p1B15" "p1B19" "p1B23"
[487] "p1F03" "p1F07" "p1F11" "p1F15" "p1F19" "p1F23" "p1J03" "p1J07" "p1J11"
[496] "p1J15" "p1J19" "p1J23" "p1N03" "p1N07" "p1N11" "p1N15" "p1N19" "p1N23"
[505] "p2B03" "p2B07" "p2B11" "p2B15" "p2B19" "p2B23" "p2F03" "p2F07" "p2F11"
[514] "p2F15" "p2F19" "p2F23" "p2J03" "p2J07" "p2J11" "p2J15" "p2J19" "p2J23"
[523] "p2N03" "p2N07" "p2N11" "p2N15" "p2N19" "p2N23" "p1A03" "p1A07" "p1A11"
[532] "p1A15" "p1A19" "p1A23" "p1E03" "p1E07" "p1E11" "p1E15" "p1E19" "p1E23"
[541] "p1I03" "p1I07" "p1I11" "p1I15" "p1I19" "p1I23" "p1M03" "p1M07" "p1M11"
[550] "p1M15" "p1M19" "p1M23" "p2A03" "p2A07" "p2A11" "p2A15" "p2A19" "p2A23"
[559] "p2E03" "p2E07" "p2E11" "p2E15" "p2E19" "p2E23" "p2I03" "p2I07" "p2I11"
[568] "p2I15" "p2I19" "p2I23" "p2M03" "p2M07" "p2M11" "p2M15" "p2M19" "p2M23"
[577] "p1D04" "p1D08" "p1D12" "p1D16" "p1D20" "p1D24" "p1H04" "p1H08" "p1H12"
[586] "p1H16" "p1H20" "p1H24" "p1L04" "p1L08" "p1L12" "p1L16" "p1L20" "p1L24"
[595] "p1P04" "p1P08" "p1P12" "p1P16" "p1P20" "p1P24" "p2D04" "p2D08" "p2D12"
[604] "p2D16" "p2D20" "p2D24" "p2H04" "p2H08" "p2H12" "p2H16" "p2H20" "p2H24"
[613] "p2L04" "p2L08" "p2L12" "p2L16" "p2L20" "p2L24" "p2P04" "p2P08" "p2P12"
[622] "p2P16" "p2P20" "p2P24" "p1C04" "p1C08" "p1C12" "p1C16" "p1C20" "p1C24"
[631] "p1G04" "p1G08" "p1G12" "p1G16" "p1G20" "p1G24" "p1K04" "p1K08" "p1K12"
[640] "p1K16" "p1K20" "p1K24" "p1O04" "p1O08" "p1O12" "p1O16" "p1O20" "p1O24"
[649] "p2C04" "p2C08" "p2C12" "p2C16" "p2C20" "p2C24" "p2G04" "p2G08" "p2G12"
[658] "p2G16" "p2G20" "p2G24" "p2K04" "p2K08" "p2K12" "p2K16" "p2K20" "p2K24"
[667] "p2O04" "p2O08" "p2O12" "p2O16" "p2O20" "p2O24" "p1B04" "p1B08" "p1B12"
[676] "p1B16" "p1B20" "p1B24" "p1F04" "p1F08" "p1F12" "p1F16" "p1F20" "p1F24"
[685] "p1J04" "p1J08" "p1J12" "p1J16" "p1J20" "p1J24" "p1N04" "p1N08" "p1N12"
[694] "p1N16" "p1N20" "p1N24" "p2B04" "p2B08" "p2B12" "p2B16" "p2B20" "p2B24"
[703] "p2F04" "p2F08" "p2F12" "p2F16" "p2F20" "p2F24" "p2J04" "p2J08" "p2J12"
[712] "p2J16" "p2J20" "p2J24" "p2N04" "p2N08" "p2N12" "p2N16" "p2N20" "p2N24"
[721] "p1A04" "p1A08" "p1A12" "p1A16" "p1A20" "p1A24" "p1E04" "p1E08" "p1E12"
[730] "p1E16" "p1E20" "p1E24" "p1I04" "p1I08" "p1I12" "p1I16" "p1I20" "p1I24"
[739] "p1M04" "p1M08" "p1M12" "p1M16" "p1M20" "p1M24" "p2A04" "p2A08" "p2A12"
[748] "p2A16" "p2A20" "p2A24" "p2E04" "p2E08" "p2E12" "p2E16" "p2E20" "p2E24"
[757] "p2I04" "p2I08" "p2I12" "p2I16" "p2I20" "p2I24" "p2M04" "p2M08" "p2M12"
[766] "p2M16" "p2M20" "p2M24"
>
> ### merge.rglist
>
> R <- G <- matrix(11:14,4,2)
> rownames(R) <- rownames(G) <- c("a","a","b","c")
> RG1 <- new("RGList",list(R=R,G=G))
> R <- G <- matrix(21:24,4,2)
> rownames(R) <- rownames(G) <- c("b","a","a","c")
> RG2 <- new("RGList",list(R=R,G=G))
> merge(RG1,RG2)
An object of class "RGList"
$R
[,1] [,2] [,3] [,4]
a 11 11 22 22
a 12 12 23 23
b 13 13 21 21
c 14 14 24 24
$G
[,1] [,2] [,3] [,4]
a 11 11 22 22
a 12 12 23 23
b 13 13 21 21
c 14 14 24 24
> merge(RG2,RG1)
An object of class "RGList"
$R
[,1] [,2] [,3] [,4]
b 21 21 13 13
a 22 22 11 11
a 23 23 12 12
c 24 24 14 14
$G
[,1] [,2] [,3] [,4]
b 21 21 13 13
a 22 22 11 11
a 23 23 12 12
c 24 24 14 14
>
> ### background correction
>
> RG <- new("RGList", list(R=c(1,2,3,4),G=c(1,2,3,4),Rb=c(2,2,2,2),Gb=c(2,2,2,2)))
> backgroundCorrect(RG)
An object of class "RGList"
$R
[,1]
[1,] -1
[2,] 0
[3,] 1
[4,] 2
$G
[,1]
[1,] -1
[2,] 0
[3,] 1
[4,] 2
> backgroundCorrect(RG, method="half")
An object of class "RGList"
$R
[,1]
[1,] 0.5
[2,] 0.5
[3,] 1.0
[4,] 2.0
$G
[,1]
[1,] 0.5
[2,] 0.5
[3,] 1.0
[4,] 2.0
> backgroundCorrect(RG, method="minimum")
An object of class "RGList"
$R
[,1]
[1,] 0.5
[2,] 0.5
[3,] 1.0
[4,] 2.0
$G
[,1]
[1,] 0.5
[2,] 0.5
[3,] 1.0
[4,] 2.0
> backgroundCorrect(RG, offset=5)
An object of class "RGList"
$R
[,1]
[1,] 4
[2,] 5
[3,] 6
[4,] 7
$G
[,1]
[1,] 4
[2,] 5
[3,] 6
[4,] 7
>
> ### loessFit
>
> x <- 1:100
> y <- rnorm(100)
> out <- loessFit(y,x)
> f1 <- quantile(out$fitted)
> r1 <- quantile(out$residual)
> w <- rep(1,100)
> w[1:50] <- 0.5
> out <- loessFit(y,x,weights=w,method="weightedLowess")
> f2 <- quantile(out$fitted)
> r2 <- quantile(out$residual)
> out <- loessFit(y,x,weights=w,method="locfit")
> f3 <- quantile(out$fitted)
> r3 <- quantile(out$residual)
> out <- loessFit(y,x,weights=w,method="loess")
> f4 <- quantile(out$fitted)
> r4 <- quantile(out$residual)
> w <- rep(1,100)
> w[2*(1:50)] <- 0
> out <- loessFit(y,x,weights=w,method="weightedLowess")
> f5 <- quantile(out$fitted)
> r5 <- quantile(out$residual)
> data.frame(f1,f2,f3,f4,f5)
f1 f2 f3 f4 f5
0% -0.78835384 -0.687432210 -0.78957137 -0.76756060 -0.63778292
25% -0.18340154 -0.179683572 -0.18979269 -0.16773223 -0.38064318
50% -0.11492924 -0.114796040 -0.12087983 -0.07185314 -0.15971879
75% 0.01507921 -0.008145125 -0.01857508 0.04030634 0.07839396
100% 0.21653837 0.145106033 0.19214597 0.21417361 0.51836274
> data.frame(r1,r2,r3,r4,r5)
r1 r2 r3 r4 r5
0% -2.04434053 -2.05132680 -2.02404318 -2.101242874 -2.22280633
25% -0.59321065 -0.57200209 -0.58975649 -0.577887481 -0.71037756
50% 0.05874864 0.04514326 0.08335198 -0.001769806 0.06785517
75% 0.56010750 0.55124530 0.57618740 0.561454370 0.65383830
100% 2.57936026 2.64549799 2.57549257 2.402324533 2.28648835
>
> ### normalizeWithinArrays
>
> RG <- new("RGList",list())
> RG$R <- matrix(rexp(100*2),100,2)
> RG$G <- matrix(rexp(100*2),100,2)
> RG$Rb <- matrix(rnorm(100*2,sd=0.02),100,2)
> RG$Gb <- matrix(rnorm(100*2,sd=0.02),100,2)
> RGb <- backgroundCorrect(RG,method="normexp",normexp.method="saddle")
Array 1 corrected
Array 2 corrected
Array 1 corrected
Array 2 corrected
> summary(cbind(RGb$R,RGb$G))
V1 V2 V3 V4
Min. :0.01626 Min. :0.01213 Min. :0.0000 Min. :0.0000
1st Qu.:0.35497 1st Qu.:0.29133 1st Qu.:0.2745 1st Qu.:0.3953
Median :0.71793 Median :0.70294 Median :0.6339 Median :0.8223
Mean :0.90184 Mean :1.00122 Mean :0.9454 Mean :1.1324
3rd Qu.:1.16891 3rd Qu.:1.33139 3rd Qu.:1.4059 3rd Qu.:1.4221
Max. :4.56267 Max. :6.37947 Max. :5.0486 Max. :6.6295
> RGb <- backgroundCorrect(RG,method="normexp",normexp.method="mle")
Array 1 corrected
Array 2 corrected
Array 1 corrected
Array 2 corrected
> summary(cbind(RGb$R,RGb$G))
V1 V2 V3 V4
Min. :0.01701 Min. :0.01255 Min. :0.0000 Min. :0.0000
1st Qu.:0.35423 1st Qu.:0.29118 1st Qu.:0.2745 1st Qu.:0.3953
Median :0.71719 Median :0.70280 Median :0.6339 Median :0.8223
Mean :0.90118 Mean :1.00110 Mean :0.9454 Mean :1.1324
3rd Qu.:1.16817 3rd Qu.:1.33124 3rd Qu.:1.4059 3rd Qu.:1.4221
Max. :4.56193 Max. :6.37932 Max. :5.0486 Max. :6.6295
> MA <- normalizeWithinArrays(RGb,method="loess")
> summary(MA$M)
V1 V2
Min. :-5.88044 Min. :-5.66985
1st Qu.:-1.18483 1st Qu.:-1.57014
Median :-0.21632 Median : 0.04823
Mean : 0.03487 Mean :-0.05481
3rd Qu.: 1.49669 3rd Qu.: 1.45113
Max. : 7.07324 Max. : 6.19744
> #MA <- normalizeWithinArrays(RG[,1:2], mouse.setup, method="robustspline")
> #MA$M[1:5,]
> #MA <- normalizeWithinArrays(mouse.data, mouse.setup)
> #MA$M[1:5,]
>
> ### normalizeBetweenArrays
>
> MA2 <- normalizeBetweenArrays(MA,method="scale")
> MA$M[1:5,]
[,1] [,2]
[1,] -1.1689588 4.5558123
[2,] 0.8971363 0.3296544
[3,] 2.8247439 1.4249960
[4,] -1.8533240 0.4804851
[5,] 1.9158459 -5.5087631
> MA$A[1:5,]
[,1] [,2]
[1,] -2.48465011 -2.4041550
[2,] -0.79230447 -0.9002250
[3,] -0.76237200 0.2071043
[4,] 0.09281027 -1.3880965
[5,] 0.22385828 -3.0855818
> MA2 <- normalizeBetweenArrays(MA,method="quantile")
> MA$M[1:5,]
[,1] [,2]
[1,] -1.1689588 4.5558123
[2,] 0.8971363 0.3296544
[3,] 2.8247439 1.4249960
[4,] -1.8533240 0.4804851
[5,] 1.9158459 -5.5087631
> MA$A[1:5,]
[,1] [,2]
[1,] -2.48465011 -2.4041550
[2,] -0.79230447 -0.9002250
[3,] -0.76237200 0.2071043
[4,] 0.09281027 -1.3880965
[5,] 0.22385828 -3.0855818
>
> ### unwrapdups
>
> M <- matrix(1:12,6,2)
> unwrapdups(M,ndups=1)
[,1] [,2]
[1,] 1 7
[2,] 2 8
[3,] 3 9
[4,] 4 10
[5,] 5 11
[6,] 6 12
> unwrapdups(M,ndups=2)
[,1] [,2] [,3] [,4]
[1,] 1 2 7 8
[2,] 3 4 9 10
[3,] 5 6 11 12
> unwrapdups(M,ndups=3)
[,1] [,2] [,3] [,4] [,5] [,6]
[1,] 1 2 3 7 8 9
[2,] 4 5 6 10 11 12
> unwrapdups(M,ndups=2,spacing=3)
[,1] [,2] [,3] [,4]
[1,] 1 4 7 10
[2,] 2 5 8 11
[3,] 3 6 9 12
>
> ### trigammaInverse
>
> trigammaInverse(c(1e-6,NA,5,1e6))
[1] 1.000000e+06 NA 4.961687e-01 1.000001e-03
>
> ### lmFit, eBayes, topTable
>
> M <- matrix(rnorm(10*6,sd=0.3),10,6)
> rownames(M) <- LETTERS[1:10]
> M[1,1:3] <- M[1,1:3] + 2
> design <- cbind(First3Arrays=c(1,1,1,0,0,0),Last3Arrays=c(0,0,0,1,1,1))
> contrast.matrix <- cbind(First3=c(1,0),Last3=c(0,1),"Last3-First3"=c(-1,1))
> fit <- lmFit(M,design)
> fit2 <- eBayes(contrasts.fit(fit,contrasts=contrast.matrix))
> topTable(fit2)
First3 Last3 Last3.First3 AveExpr F P.Value
A 1.77602021 0.06025114 -1.71576906 0.918135675 50.91471061 7.727200e-23
D -0.05454069 0.39127869 0.44581938 0.168369004 2.51638838 8.075072e-02
F -0.16249607 -0.33009728 -0.16760121 -0.246296671 2.18256779 1.127516e-01
G 0.30852468 -0.06873462 -0.37725930 0.119895035 1.61088775 1.997102e-01
H -0.16942269 0.20578118 0.37520387 0.018179245 1.14554368 3.180510e-01
J 0.21417623 0.07074940 -0.14342683 0.142462814 0.82029274 4.403027e-01
C -0.12236781 0.15095948 0.27332729 0.014295836 0.60885003 5.439761e-01
B -0.11982833 0.13529287 0.25512120 0.007732271 0.52662792 5.905931e-01
E 0.01897934 0.10434934 0.08536999 0.061664340 0.18136849 8.341279e-01
I -0.04720963 0.03996397 0.08717360 -0.003622829 0.06168476 9.401792e-01
adj.P.Val
A 7.727200e-22
D 3.758388e-01
F 3.758388e-01
G 4.992756e-01
H 6.361019e-01
J 7.338379e-01
C 7.382414e-01
B 7.382414e-01
E 9.268088e-01
I 9.401792e-01
> topTable(fit2,coef=3,resort.by="logFC")
logFC AveExpr t P.Value adj.P.Val B
D 0.44581938 0.168369004 1.7901232 8.100587e-02 3.494414e-01 -5.323150
H 0.37520387 0.018179245 1.5065768 1.397766e-01 3.494414e-01 -5.785971
C 0.27332729 0.014295836 1.0975061 2.789833e-01 5.196681e-01 -6.313399
B 0.25512120 0.007732271 1.0244023 3.118009e-01 5.196681e-01 -6.390202
I 0.08717360 -0.003622829 0.3500330 7.281504e-01 7.335508e-01 -6.849117
E 0.08536999 0.061664340 0.3427908 7.335508e-01 7.335508e-01 -6.851601
J -0.14342683 0.142462814 -0.5759097 5.679026e-01 7.098782e-01 -6.745563
F -0.16760121 -0.246296671 -0.6729784 5.048308e-01 7.098782e-01 -6.685541
G -0.37725930 0.119895035 -1.5148301 1.376783e-01 3.494414e-01 -5.773625
A -1.71576906 0.918135675 -6.8894222 2.674199e-08 2.674199e-07 16.590631
> topTable(fit2,coef=3,resort.by="p")
logFC AveExpr t P.Value adj.P.Val B
A -1.71576906 0.918135675 -6.8894222 2.674199e-08 2.674199e-07 16.590631
D 0.44581938 0.168369004 1.7901232 8.100587e-02 3.494414e-01 -5.323150
G -0.37725930 0.119895035 -1.5148301 1.376783e-01 3.494414e-01 -5.773625
H 0.37520387 0.018179245 1.5065768 1.397766e-01 3.494414e-01 -5.785971
C 0.27332729 0.014295836 1.0975061 2.789833e-01 5.196681e-01 -6.313399
B 0.25512120 0.007732271 1.0244023 3.118009e-01 5.196681e-01 -6.390202
F -0.16760121 -0.246296671 -0.6729784 5.048308e-01 7.098782e-01 -6.685541
J -0.14342683 0.142462814 -0.5759097 5.679026e-01 7.098782e-01 -6.745563
I 0.08717360 -0.003622829 0.3500330 7.281504e-01 7.335508e-01 -6.849117
E 0.08536999 0.061664340 0.3427908 7.335508e-01 7.335508e-01 -6.851601
> topTable(fit2,coef=3,sort="logFC",resort.by="t")
logFC AveExpr t P.Value adj.P.Val B
D 0.44581938 0.168369004 1.7901232 8.100587e-02 3.494414e-01 -5.323150
H 0.37520387 0.018179245 1.5065768 1.397766e-01 3.494414e-01 -5.785971
C 0.27332729 0.014295836 1.0975061 2.789833e-01 5.196681e-01 -6.313399
B 0.25512120 0.007732271 1.0244023 3.118009e-01 5.196681e-01 -6.390202
I 0.08717360 -0.003622829 0.3500330 7.281504e-01 7.335508e-01 -6.849117
E 0.08536999 0.061664340 0.3427908 7.335508e-01 7.335508e-01 -6.851601
J -0.14342683 0.142462814 -0.5759097 5.679026e-01 7.098782e-01 -6.745563
F -0.16760121 -0.246296671 -0.6729784 5.048308e-01 7.098782e-01 -6.685541
G -0.37725930 0.119895035 -1.5148301 1.376783e-01 3.494414e-01 -5.773625
A -1.71576906 0.918135675 -6.8894222 2.674199e-08 2.674199e-07 16.590631
> topTable(fit2,coef=3,resort.by="B")
logFC AveExpr t P.Value adj.P.Val B
A -1.71576906 0.918135675 -6.8894222 2.674199e-08 2.674199e-07 16.590631
D 0.44581938 0.168369004 1.7901232 8.100587e-02 3.494414e-01 -5.323150
G -0.37725930 0.119895035 -1.5148301 1.376783e-01 3.494414e-01 -5.773625
H 0.37520387 0.018179245 1.5065768 1.397766e-01 3.494414e-01 -5.785971
C 0.27332729 0.014295836 1.0975061 2.789833e-01 5.196681e-01 -6.313399
B 0.25512120 0.007732271 1.0244023 3.118009e-01 5.196681e-01 -6.390202
F -0.16760121 -0.246296671 -0.6729784 5.048308e-01 7.098782e-01 -6.685541
J -0.14342683 0.142462814 -0.5759097 5.679026e-01 7.098782e-01 -6.745563
I 0.08717360 -0.003622829 0.3500330 7.281504e-01 7.335508e-01 -6.849117
E 0.08536999 0.061664340 0.3427908 7.335508e-01 7.335508e-01 -6.851601
> topTable(fit2,coef=3,lfc=1)
logFC AveExpr t P.Value adj.P.Val B
A -1.715769 0.9181357 -6.889422 2.674199e-08 2.674199e-07 16.59063
> topTable(fit2,coef=3,p=0.2)
logFC AveExpr t P.Value adj.P.Val B
A -1.715769 0.9181357 -6.889422 2.674199e-08 2.674199e-07 16.59063
> topTable(fit2,coef=3,p=0.2,lfc=0.5)
logFC AveExpr t P.Value adj.P.Val B
A -1.715769 0.9181357 -6.889422 2.674199e-08 2.674199e-07 16.59063
> topTable(fit2,coef=3,p=0.2,lfc=0.5,sort="none")
logFC AveExpr t P.Value adj.P.Val B
A -1.715769 0.9181357 -6.889422 2.674199e-08 2.674199e-07 16.59063
>
> designlist <- list(Null=matrix(1,6,1),Two=design,Three=cbind(1,c(0,0,1,1,0,0),c(0,0,0,0,1,1)))
> out <- selectModel(M,designlist)
> table(out$pref)
Null Two Three
5 3 2
>
> ### marray object
>
> #suppressMessages(suppressWarnings(gotmarray <- require(marray,quietly=TRUE)))
> #if(gotmarray) {
> # data(swirl)
> # snorm = maNorm(swirl)
> # fit <- lmFit(snorm, design = c(1,-1,-1,1))
> # fit <- eBayes(fit)
> # topTable(fit,resort.by="AveExpr")
> #}
>
> ### duplicateCorrelation
>
> cor.out <- duplicateCorrelation(M)
> cor.out$consensus.correlation
[1] -0.09290714
> cor.out$atanh.correlations
[1] -0.4419130 0.4088967 -0.1964978 -0.6093769 0.3730118
>
> ### gls.series
>
> fit <- gls.series(M,design,correlation=cor.out$cor)
> fit$coefficients
First3Arrays Last3Arrays
[1,] 0.82809594 0.09777201
[2,] -0.08845425 0.27111909
[3,] -0.07175836 -0.11287397
[4,] 0.06955100 0.06852328
[5,] 0.08348330 0.05535668
> fit$stdev.unscaled
First3Arrays Last3Arrays
[1,] 0.3888215 0.3888215
[2,] 0.3888215 0.3888215
[3,] 0.3888215 0.3888215
[4,] 0.3888215 0.3888215
[5,] 0.3888215 0.3888215
> fit$sigma
[1] 0.7630059 0.2152728 0.3350370 0.3227781 0.3405473
> fit$df.residual
[1] 10 10 10 10 10
>
> ### mrlm
>
> fit <- mrlm(M,design)
Warning message:
In rlm.default(x = X, y = y, weights = w, ...) :
'rlm' failed to converge in 20 steps
> fit$coef
First3Arrays Last3Arrays
A 1.75138894 0.06025114
B -0.11982833 0.10322039
C -0.09302502 0.15095948
D -0.05454069 0.33700045
E 0.07927938 0.10434934
F -0.16249607 -0.34010852
G 0.30852468 -0.06873462
H -0.16942269 0.24392984
I -0.04720963 0.03996397
J 0.21417623 -0.05679272
> fit$stdev.unscaled
First3Arrays Last3Arrays
A 0.5933418 0.5773503
B 0.5773503 0.6096497
C 0.6017444 0.5773503
D 0.5773503 0.6266021
E 0.6307703 0.5773503
F 0.5773503 0.5846707
G 0.5773503 0.5773503
H 0.5773503 0.6544564
I 0.5773503 0.5773503
J 0.5773503 0.6689776
> fit$sigma
[1] 0.2894294 0.2679396 0.2090236 0.1461395 0.2309018 0.2827476 0.2285945
[8] 0.2267556 0.3537469 0.2172409
> fit$df.residual
[1] 4 4 4 4 4 4 4 4 4 4
>
> # Similar to Mette Langaas 19 May 2004
> set.seed(123)
> narrays <- 9
> ngenes <- 5
> mu <- 0
> alpha <- 2
> beta <- -2
> epsilon <- matrix(rnorm(narrays*ngenes,0,1),ncol=narrays)
> X <- cbind(rep(1,9),c(0,0,0,1,1,1,0,0,0),c(0,0,0,0,0,0,1,1,1))
> dimnames(X) <- list(1:9,c("mu","alpha","beta"))
> yvec <- mu*X[,1]+alpha*X[,2]+beta*X[,3]
> ymat <- matrix(rep(yvec,ngenes),ncol=narrays,byrow=T)+epsilon
> ymat[5,1:2] <- NA
> fit <- lmFit(ymat,design=X)
> test.contr <- cbind(c(0,1,-1),c(1,1,0),c(1,0,1))
> dimnames(test.contr) <- list(c("mu","alpha","beta"),c("alpha-beta","mu+alpha","mu+beta"))
> fit2 <- contrasts.fit(fit,contrasts=test.contr)
> eBayes(fit2)
An object of class "MArrayLM"
$coefficients
alpha-beta mu+alpha mu+beta
[1,] 3.537333 1.677465 -1.859868
[2,] 4.355578 2.372554 -1.983024
[3,] 3.197645 1.053584 -2.144061
[4,] 2.697734 1.611443 -1.086291
[5,] 3.502304 2.051995 -1.450309
$stdev.unscaled
alpha-beta mu+alpha mu+beta
[1,] 0.8164966 0.5773503 0.5773503
[2,] 0.8164966 0.5773503 0.5773503
[3,] 0.8164966 0.5773503 0.5773503
[4,] 0.8164966 0.5773503 0.5773503
[5,] 1.1547005 0.8368633 0.8368633
$sigma
[1] 1.3425032 0.4647155 1.1993444 0.9428569 0.9421509
$df.residual
[1] 6 6 6 6 4
$cov.coefficients
alpha-beta mu+alpha mu+beta
alpha-beta 0.6666667 3.333333e-01 -3.333333e-01
mu+alpha 0.3333333 3.333333e-01 5.551115e-17
mu+beta -0.3333333 5.551115e-17 3.333333e-01
$rank
[1] 3
$Amean
[1] 0.2034961 0.1954604 -0.2863347 0.1188659 0.1784593
$method
[1] "ls"
$design
mu alpha beta
1 1 0 0
2 1 0 0
3 1 0 0
4 1 1 0
5 1 1 0
6 1 1 0
7 1 0 1
8 1 0 1
9 1 0 1
$contrasts
alpha-beta mu+alpha mu+beta
mu 0 1 1
alpha 1 1 0
beta -1 0 1
$df.prior
[1] 9.306153
$s2.prior
[1] 0.923179
$var.prior
[1] 17.33142 17.33142 12.26855
$proportion
[1] 0.01
$s2.post
[1] 1.2677996 0.6459499 1.1251558 0.9097727 0.9124980
$t
alpha-beta mu+alpha mu+beta
[1,] 3.847656 2.580411 -2.860996
[2,] 6.637308 5.113018 -4.273553
[3,] 3.692066 1.720376 -3.500994
[4,] 3.464003 2.926234 -1.972606
[5,] 3.175181 2.566881 -1.814221
$df.total
[1] 15.30615 15.30615 15.30615 15.30615 13.30615
$p.value
alpha-beta mu+alpha mu+beta
[1,] 1.529450e-03 0.0206493481 0.0117123495
[2,] 7.144893e-06 0.0001195844 0.0006385076
[3,] 2.109270e-03 0.1055117477 0.0031325769
[4,] 3.381970e-03 0.0102514264 0.0668844448
[5,] 7.124839e-03 0.0230888584 0.0922478630
$lods
alpha-beta mu+alpha mu+beta
[1,] -1.013417 -3.702133 -3.0332393
[2,] 3.981496 1.283349 -0.2615911
[3,] -1.315036 -5.168621 -1.7864101
[4,] -1.757103 -3.043209 -4.6191869
[5,] -2.257358 -3.478267 -4.5683738
$F
[1] 7.421911 22.203107 7.608327 6.227010 5.060579
$F.p.value
[1] 5.581800e-03 2.988923e-05 5.080726e-03 1.050148e-02 2.320274e-02
>
> ### uniquegenelist
>
> uniquegenelist(letters[1:8],ndups=2)
[1] "a" "c" "e" "g"
> uniquegenelist(letters[1:8],ndups=2,spacing=2)
[1] "a" "b" "e" "f"
>
> ### classifyTests
>
> tstat <- matrix(c(0,5,0, 0,2.5,0, -2,-2,2, 1,1,1), 4, 3, byrow=TRUE)
> classifyTestsF(tstat)
TestResults matrix
[,1] [,2] [,3]
[1,] 0 1 0
[2,] 0 0 0
[3,] -1 -1 1
[4,] 0 0 0
> FStat(tstat)
[1] 8.333333 2.083333 4.000000 1.000000
attr(,"df1")
[1] 3
attr(,"df2")
[1] Inf
> classifyTestsT(tstat)
TestResults matrix
[,1] [,2] [,3]
[1,] 0 1 0
[2,] 0 0 0
[3,] 0 0 0
[4,] 0 0 0
> classifyTestsP(tstat)
TestResults matrix
[,1] [,2] [,3]
[1,] 0 1 0
[2,] 0 1 0
[3,] 0 0 0
[4,] 0 0 0
>
> ### avereps
>
> x <- matrix(rnorm(8*3),8,3)
> colnames(x) <- c("S1","S2","S3")
> rownames(x) <- c("b","a","a","c","c","b","b","b")
> avereps(x)
S1 S2 S3
b -0.2353018 0.5220094 0.2302895
a -0.4347701 0.6453498 -0.6758914
c 0.3482980 -0.4820695 -0.3841313
>
> ### roast
>
> y <- matrix(rnorm(100*4),100,4)
> sigma <- sqrt(2/rchisq(100,df=7))
> y <- y*sigma
> design <- cbind(Intercept=1,Group=c(0,0,1,1))
> iset1 <- 1:5
> y[iset1,3:4] <- y[iset1,3:4]+3
> iset2 <- 6:10
> roast(y=y,iset1,design,contrast=2)
Active.Prop P.Value
Down 0 0.996498249
Up 1 0.004002001
UpOrDown 1 0.008000000
Mixed 1 0.008000000
> roast(y=y,iset1,design,contrast=2,array.weights=c(0.5,1,0.5,1))
Active.Prop P.Value
Down 0 0.99899950
Up 1 0.00150075
UpOrDown 1 0.00300000
Mixed 1 0.00300000
> w <- matrix(runif(100*4),100,4)
> roast(y=y,iset1,design,contrast=2,weights=w)
Active.Prop P.Value
Down 0 0.9994997
Up 1 0.0010005
UpOrDown 1 0.0020000
Mixed 1 0.0020000
> mroast(y=y,list(set1=iset1,set2=iset2),design,contrast=2,gene.weights=runif(100))
NGenes PropDown PropUp Direction PValue FDR PValue.Mixed FDR.Mixed
set1 5 0 1 Up 0.008 0.015 0.008 0.015
set2 5 0 0 Up 0.959 0.959 0.687 0.687
> mroast(y=y,list(set1=iset1,set2=iset2),design,contrast=2,array.weights=c(0.5,1,0.5,1))
NGenes PropDown PropUp Direction PValue FDR PValue.Mixed FDR.Mixed
set1 5 0 1 Up 0.004 0.007 0.004 0.007
set2 5 0 0 Up 0.679 0.679 0.658 0.658
> mroast(y=y,list(set1=iset1,set2=iset2),design,contrast=2,weights=w)
NGenes PropDown PropUp Direction PValue FDR PValue.Mixed FDR.Mixed
set1 5 0.0 1 Up 0.003 0.005 0.003 0.005
set2 5 0.2 0 Down 0.950 0.950 0.250 0.250
> mroast(y=y,list(set1=iset1,set2=iset2),design,contrast=2,weights=w,array.weights=c(0.5,1,0.5,1))
NGenes PropDown PropUp Direction PValue FDR PValue.Mixed FDR.Mixed
set1 5 0 1 Up 0.001 0.001 0.001 0.001
set2 5 0 0 Down 0.791 0.791 0.146 0.146
> fry(y=y,list(set1=iset1,set2=iset2),design,contrast=2,weights=w,array.weights=c(0.5,1,0.5,1))
NGenes Direction PValue FDR PValue.Mixed FDR.Mixed
set1 5 Up 0.0007432594 0.001486519 1.820548e-05 3.641096e-05
set2 5 Down 0.8208140511 0.820814051 2.211837e-01 2.211837e-01
> rownames(y) <- paste0("Gene",1:100)
> iset1A <- rownames(y)[1:5]
> fry(y=y,index=iset1A,design,contrast=2,weights=w,array.weights=c(0.5,1,0.5,1))
NGenes Direction PValue PValue.Mixed
set1 5 Up 0.0007432594 1.820548e-05
>
> ### camera
>
> camera(y=y,iset1,design,contrast=2,weights=c(0.5,1,0.5,1),allow.neg.cor=TRUE,inter.gene.cor=NA)
NGenes Correlation Direction PValue
set1 5 -0.2481655 Up 0.001050253
> camera(y=y,list(set1=iset1,set2=iset2),design,contrast=2,allow.neg.cor=TRUE,inter.gene.cor=NA)
NGenes Correlation Direction PValue FDR
set1 5 -0.2481655 Up 0.0009047749 0.00180955
set2 5 0.1719094 Down 0.9068364378 0.90683644
> camera(y=y,iset1,design,contrast=2,weights=c(0.5,1,0.5,1))
NGenes Direction PValue
set1 5 Up 1.105329e-10
> camera(y=y,list(set1=iset1,set2=iset2),design,contrast=2)
NGenes Direction PValue FDR
set1 5 Up 7.334400e-12 1.466880e-11
set2 5 Down 8.677115e-01 8.677115e-01
> camera(y=y,iset1A,design,contrast=2)
NGenes Direction PValue
set1 5 Up 7.3344e-12
>
> ### with EList arg
>
> y <- new("EList",list(E=y))
> roast(y=y,iset1,design,contrast=2)
Active.Prop P.Value
Down 0 0.997498749
Up 1 0.003001501
UpOrDown 1 0.006000000
Mixed 1 0.006000000
> camera(y=y,iset1,design,contrast=2,allow.neg.cor=TRUE,inter.gene.cor=NA)
NGenes Correlation Direction PValue
set1 5 -0.2481655 Up 0.0009047749
> camera(y=y,iset1,design,contrast=2)
NGenes Direction PValue
set1 5 Up 7.3344e-12
>
> ### eBayes with trend
>
> fit <- lmFit(y,design)
> fit <- eBayes(fit,trend=TRUE)
> topTable(fit,coef=2)
logFC AveExpr t P.Value adj.P.Val B
Gene2 3.729512 1.73488969 4.865697 0.0004854886 0.02902331 0.1596831
Gene3 3.488703 1.03931081 4.754954 0.0005804663 0.02902331 -0.0144071
Gene4 2.696676 1.74060725 3.356468 0.0063282637 0.21094212 -2.3434702
Gene1 2.391846 1.72305203 3.107124 0.0098781268 0.24695317 -2.7738874
Gene33 -1.492317 -0.07525287 -2.783817 0.0176475742 0.29965463 -3.3300835
Gene5 2.387967 1.63066783 2.773444 0.0179792778 0.29965463 -3.3478204
Gene80 -1.839760 -0.32802306 -2.503584 0.0291489863 0.37972679 -3.8049642
Gene39 1.366141 -0.27360750 2.451133 0.0320042242 0.37972679 -3.8925860
Gene95 -1.907074 1.26297763 -2.414217 0.0341754107 0.37972679 -3.9539571
Gene50 1.034777 0.01608433 2.054690 0.0642289403 0.59978803 -4.5350317
> fit$df.prior
[1] 9.098442
> fit$s2.prior
Gene1 Gene2 Gene3 Gene4 Gene5 Gene6 Gene7 Gene8
0.6901845 0.6977354 0.3860494 0.7014122 0.6341068 0.2926337 0.3077620 0.3058098
Gene9 Gene10 Gene11 Gene12 Gene13 Gene14 Gene15 Gene16
0.2985145 0.2832520 0.3232434 0.3279710 0.2816081 0.2943502 0.3127994 0.2894802
Gene17 Gene18 Gene19 Gene20 Gene21 Gene22 Gene23 Gene24
0.2812758 0.2840051 0.2839124 0.2954261 0.2838592 0.2812704 0.3157029 0.2844541
Gene25 Gene26 Gene27 Gene28 Gene29 Gene30 Gene31 Gene32
0.4778832 0.2818242 0.2930360 0.2940957 0.2941862 0.3234399 0.3164779 0.2853510
Gene33 Gene34 Gene35 Gene36 Gene37 Gene38 Gene39 Gene40
0.2988244 0.3450090 0.3048596 0.3089086 0.3104534 0.4551549 0.3220008 0.2813286
Gene41 Gene42 Gene43 Gene44 Gene45 Gene46 Gene47 Gene48
0.2826027 0.2822504 0.2823330 0.3170673 0.3146173 0.3146793 0.2916540 0.2975003
Gene49 Gene50 Gene51 Gene52 Gene53 Gene54 Gene55 Gene56
0.3538946 0.2907240 0.3199596 0.2816641 0.2814293 0.2996822 0.2812885 0.2896157
Gene57 Gene58 Gene59 Gene60 Gene61 Gene62 Gene63 Gene64
0.2955317 0.2815907 0.2919420 0.2849675 0.3540805 0.3491713 0.2975019 0.2939325
Gene65 Gene66 Gene67 Gene68 Gene69 Gene70 Gene71 Gene72
0.2986943 0.3265466 0.3402343 0.3394927 0.2813283 0.2814440 0.3089669 0.3030850
Gene73 Gene74 Gene75 Gene76 Gene77 Gene78 Gene79 Gene80
0.2859286 0.2813216 0.3475231 0.3334419 0.2949550 0.3108702 0.2959688 0.3295294
Gene81 Gene82 Gene83 Gene84 Gene85 Gene86 Gene87 Gene88
0.3413700 0.2946268 0.3029565 0.2920284 0.2926205 0.2818046 0.3425116 0.2882936
Gene89 Gene90 Gene91 Gene92 Gene93 Gene94 Gene95 Gene96
0.2945459 0.3077919 0.2892134 0.2823787 0.3048049 0.2961408 0.4590012 0.2812784
Gene97 Gene98 Gene99 Gene100
0.2846345 0.2819651 0.3137551 0.2856081
> summary(fit$s2.post)
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.2335 0.2603 0.2997 0.3375 0.3655 0.7812
>
> y$E[1,1] <- NA
> y$E[1,3] <- NA
> fit <- lmFit(y,design)
> fit <- eBayes(fit,trend=TRUE)
> topTable(fit,coef=2)
logFC AveExpr t P.Value adj.P.Val B
Gene3 3.488703 1.03931081 4.604490 0.0007644061 0.07644061 -0.2333915
Gene2 3.729512 1.73488969 4.158038 0.0016033158 0.08016579 -0.9438583
Gene4 2.696676 1.74060725 2.898102 0.0145292666 0.44537707 -3.0530813
Gene33 -1.492317 -0.07525287 -2.784004 0.0178150826 0.44537707 -3.2456324
Gene5 2.387967 1.63066783 2.495395 0.0297982959 0.46902627 -3.7272957
Gene80 -1.839760 -0.32802306 -2.491115 0.0300256116 0.46902627 -3.7343584
Gene39 1.366141 -0.27360750 2.440729 0.0328318388 0.46902627 -3.8172597
Gene1 2.638272 1.47993643 2.227507 0.0530016060 0.58890673 -3.9537576
Gene95 -1.907074 1.26297763 -2.288870 0.0429197808 0.53649726 -4.0642439
Gene50 1.034777 0.01608433 2.063663 0.0635275235 0.60439978 -4.4204731
> fit$df.residual[1]
[1] 0
> fit$df.prior
[1] 8.971891
> fit$s2.prior
[1] 0.7014084 0.9646561 0.4276287 0.9716476 0.8458852 0.2910492 0.3097052
[8] 0.3074225 0.2985517 0.2786374 0.3267121 0.3316013 0.2766404 0.2932679
[15] 0.3154347 0.2869186 0.2761395 0.2799884 0.2795119 0.2946468 0.2794412
[22] 0.2761282 0.3186442 0.2806092 0.4596465 0.2767847 0.2924541 0.2939204
[29] 0.2930568 0.3269177 0.3194905 0.2814293 0.2989389 0.3483845 0.3062977
[36] 0.3110287 0.3127934 0.4418052 0.3254067 0.2761732 0.2780422 0.2773311
[43] 0.2776653 0.3201314 0.3174515 0.3175199 0.2897731 0.2972785 0.3567262
[50] 0.2885556 0.3232426 0.2767207 0.2762915 0.3000062 0.2761306 0.2870975
[57] 0.2947817 0.2766152 0.2901489 0.2813183 0.3568982 0.3724440 0.2972804
[64] 0.2927300 0.2987764 0.3301406 0.3437962 0.3430762 0.2761729 0.2763094
[71] 0.3110958 0.3041715 0.2822004 0.2761654 0.3507694 0.3371214 0.2940441
[78] 0.3132660 0.2953388 0.3331880 0.3448949 0.2946558 0.3040162 0.2902616
[85] 0.2910320 0.2769211 0.3459946 0.2859057 0.2935193 0.3097398 0.2865663
[92] 0.2774968 0.3062327 0.2955576 0.5425422 0.2761214 0.2808585 0.2771484
[99] 0.3164981 0.2817725
> summary(fit$s2.post)
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.2296 0.2581 0.3003 0.3453 0.3652 0.9158
>
> ### voom
>
> y <- matrix(rpois(100*4,lambda=20),100,4)
> design <- cbind(Int=1,x=c(0,0,1,1))
> v <- voom(y,design)
> names(v)
[1] "E" "weights" "design" "targets"
> summary(v$E)
V1 V2 V3 V4
Min. :12.25 Min. :12.58 Min. :12.19 Min. :12.24
1st Qu.:13.13 1st Qu.:13.07 1st Qu.:13.15 1st Qu.:13.03
Median :13.29 Median :13.30 Median :13.30 Median :13.27
Mean :13.28 Mean :13.29 Mean :13.29 Mean :13.28
3rd Qu.:13.49 3rd Qu.:13.51 3rd Qu.:13.50 3rd Qu.:13.50
Max. :14.23 Max. :14.28 Max. :13.97 Max. :13.96
> summary(v$weights)
V1 V2 V3 V4
Min. : 5.935 Min. : 5.935 Min. : 5.935 Min. : 5.935
1st Qu.: 6.788 1st Qu.: 7.049 1st Qu.: 7.207 1st Qu.: 6.825
Median :11.066 Median :10.443 Median :10.606 Median :10.414
Mean :10.421 Mean :10.485 Mean :10.571 Mean :10.532
3rd Qu.:13.485 3rd Qu.:14.155 3rd Qu.:13.859 3rd Qu.:14.121
Max. :15.083 Max. :15.101 Max. :15.095 Max. :15.063
>
> ### goana
>
> EB <- c("133746","1339","134","1340","134083","134111","134147","134187","134218","134266",
+ "134353","134359","134391","134429","134430","1345","134510","134526","134549","1346",
+ "134637","1347","134701","134728","1348","134829","134860","134864","1349","134957",
+ "135","1350","1351","135112","135114","135138","135152","135154","1352","135228",
+ "135250","135293","135295","1353","135458","1355","1356","135644","135656","1357",
+ "1358","135892","1359","135924","135935","135941","135946","135948","136","1360",
+ "136051","1361","1362","136227","136242","136259","1363","136306","136319","136332",
+ "136371","1364","1365","136541","1366","136647","1368","136853","1369","136991",
+ "1370","137075","1371","137209","1373","137362","1374","137492","1375","1376",
+ "137682","137695","137735","1378","137814","137868","137872","137886","137902","137964")
> go <- goana(fit,FDR=0.8,geneid=EB)
> topGO(go,n=10,truncate.term=30)
Term Ont N Up Down P.Up P.Down
GO:0055082 cellular chemical homeostas... BP 2 0 2 1.000000000 0.009090909
GO:0006915 apoptotic process BP 5 4 1 0.009503355 0.416247633
GO:0098609 cell-cell adhesion BP 5 4 0 0.009503355 1.000000000
GO:0040011 locomotion BP 5 4 0 0.009503355 1.000000000
GO:0012501 programmed cell death BP 5 4 1 0.009503355 0.416247633
GO:0042981 regulation of apoptotic pro... BP 5 4 1 0.009503355 0.416247633
GO:0043067 regulation of programmed ce... BP 5 4 1 0.009503355 0.416247633
GO:0097190 apoptotic signaling pathway BP 3 3 0 0.010952381 1.000000000
GO:0031252 cell leading edge CC 3 3 0 0.010952381 1.000000000
GO:0006897 endocytosis BP 3 3 0 0.010952381 1.000000000
> topGO(go,n=10,truncate.term=30,sort="down")
Term Ont N Up Down P.Up P.Down
GO:0055082 cellular chemical homeostas... BP 2 0 2 1.0000000 0.009090909
GO:0032502 developmental process BP 25 4 6 0.8946593 0.014492712
GO:0009887 animal organ morphogenesis BP 3 0 2 1.0000000 0.025788497
GO:0019725 cellular homeostasis BP 3 0 2 1.0000000 0.025788497
GO:0072359 circulatory system developm... BP 3 0 2 1.0000000 0.025788497
GO:0007507 heart development BP 3 0 2 1.0000000 0.025788497
GO:0048232 male gamete generation BP 3 0 2 1.0000000 0.025788497
GO:0007283 spermatogenesis BP 3 0 2 1.0000000 0.025788497
GO:0070062 extracellular exosome CC 14 3 4 0.6749330 0.031604687
GO:0043230 extracellular organelle CC 14 3 4 0.6749330 0.031604687
>
> proc.time()
user system elapsed
4.256 0.326 4.582
limma.Rcheck/tests/limma-Tests.Rout.save
R version 3.5.0 (2018-04-23) -- "Joy in Playing"
Copyright (C) 2018 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(limma)
>
> set.seed(0); u <- runif(100)
>
> ### strsplit2
>
> x <- c("ab;cd;efg","abc;def","z","")
> strsplit2(x,split=";")
[,1] [,2] [,3]
[1,] "ab" "cd" "efg"
[2,] "abc" "def" ""
[3,] "z" "" ""
[4,] "" "" ""
>
> ### removeext
>
> removeExt(c("slide1.spot","slide.2.spot"))
[1] "slide1" "slide.2"
> removeExt(c("slide1.spot","slide"))
[1] "slide1.spot" "slide"
>
> ### printorder
>
> printorder(list(ngrid.r=4,ngrid.c=4,nspot.r=8,nspot.c=6),ndups=2,start="topright",npins=4)
$printorder
[1] 6 5 4 3 2 1 12 11 10 9 8 7 18 17 16 15 14 13
[19] 24 23 22 21 20 19 30 29 28 27 26 25 36 35 34 33 32 31
[37] 42 41 40 39 38 37 48 47 46 45 44 43 6 5 4 3 2 1
[55] 12 11 10 9 8 7 18 17 16 15 14 13 24 23 22 21 20 19
[73] 30 29 28 27 26 25 36 35 34 33 32 31 42 41 40 39 38 37
[91] 48 47 46 45 44 43 6 5 4 3 2 1 12 11 10 9 8 7
[109] 18 17 16 15 14 13 24 23 22 21 20 19 30 29 28 27 26 25
[127] 36 35 34 33 32 31 42 41 40 39 38 37 48 47 46 45 44 43
[145] 6 5 4 3 2 1 12 11 10 9 8 7 18 17 16 15 14 13
[163] 24 23 22 21 20 19 30 29 28 27 26 25 36 35 34 33 32 31
[181] 42 41 40 39 38 37 48 47 46 45 44 43 54 53 52 51 50 49
[199] 60 59 58 57 56 55 66 65 64 63 62 61 72 71 70 69 68 67
[217] 78 77 76 75 74 73 84 83 82 81 80 79 90 89 88 87 86 85
[235] 96 95 94 93 92 91 54 53 52 51 50 49 60 59 58 57 56 55
[253] 66 65 64 63 62 61 72 71 70 69 68 67 78 77 76 75 74 73
[271] 84 83 82 81 80 79 90 89 88 87 86 85 96 95 94 93 92 91
[289] 54 53 52 51 50 49 60 59 58 57 56 55 66 65 64 63 62 61
[307] 72 71 70 69 68 67 78 77 76 75 74 73 84 83 82 81 80 79
[325] 90 89 88 87 86 85 96 95 94 93 92 91 54 53 52 51 50 49
[343] 60 59 58 57 56 55 66 65 64 63 62 61 72 71 70 69 68 67
[361] 78 77 76 75 74 73 84 83 82 81 80 79 90 89 88 87 86 85
[379] 96 95 94 93 92 91 102 101 100 99 98 97 108 107 106 105 104 103
[397] 114 113 112 111 110 109 120 119 118 117 116 115 126 125 124 123 122 121
[415] 132 131 130 129 128 127 138 137 136 135 134 133 144 143 142 141 140 139
[433] 102 101 100 99 98 97 108 107 106 105 104 103 114 113 112 111 110 109
[451] 120 119 118 117 116 115 126 125 124 123 122 121 132 131 130 129 128 127
[469] 138 137 136 135 134 133 144 143 142 141 140 139 102 101 100 99 98 97
[487] 108 107 106 105 104 103 114 113 112 111 110 109 120 119 118 117 116 115
[505] 126 125 124 123 122 121 132 131 130 129 128 127 138 137 136 135 134 133
[523] 144 143 142 141 140 139 102 101 100 99 98 97 108 107 106 105 104 103
[541] 114 113 112 111 110 109 120 119 118 117 116 115 126 125 124 123 122 121
[559] 132 131 130 129 128 127 138 137 136 135 134 133 144 143 142 141 140 139
[577] 150 149 148 147 146 145 156 155 154 153 152 151 162 161 160 159 158 157
[595] 168 167 166 165 164 163 174 173 172 171 170 169 180 179 178 177 176 175
[613] 186 185 184 183 182 181 192 191 190 189 188 187 150 149 148 147 146 145
[631] 156 155 154 153 152 151 162 161 160 159 158 157 168 167 166 165 164 163
[649] 174 173 172 171 170 169 180 179 178 177 176 175 186 185 184 183 182 181
[667] 192 191 190 189 188 187 150 149 148 147 146 145 156 155 154 153 152 151
[685] 162 161 160 159 158 157 168 167 166 165 164 163 174 173 172 171 170 169
[703] 180 179 178 177 176 175 186 185 184 183 182 181 192 191 190 189 188 187
[721] 150 149 148 147 146 145 156 155 154 153 152 151 162 161 160 159 158 157
[739] 168 167 166 165 164 163 174 173 172 171 170 169 180 179 178 177 176 175
[757] 186 185 184 183 182 181 192 191 190 189 188 187
$plate
[1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[149] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[186] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[223] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[260] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[297] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[334] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[371] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[408] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[445] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[482] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[519] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[556] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[593] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[630] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[667] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[704] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[741] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
$plate.r
[1] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[26] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 3 3
[51] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[76] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2
[101] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[126] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1
[151] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[176] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 8 8 8 8 8 8 8 8
[201] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8
[226] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 7 7 7 7 7 7 7 7 7 7
[251] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
[276] 7 7 7 7 7 7 7 7 7 7 7 7 7 6 6 6 6 6 6 6 6 6 6 6 6
[301] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
[326] 6 6 6 6 6 6 6 6 6 6 6 5 5 5 5 5 5 5 5 5 5 5 5 5 5
[351] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
[376] 5 5 5 5 5 5 5 5 5 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12
[401] 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12
[426] 12 12 12 12 12 12 12 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
[451] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
[476] 11 11 11 11 11 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
[501] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
[526] 10 10 10 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
[551] 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
[576] 9 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16
[601] 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 15
[626] 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15
[651] 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 14 14 14
[676] 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14
[701] 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 13 13 13 13 13
[726] 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13
[751] 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13
$plate.c
[1] 3 3 2 2 1 1 6 6 5 5 4 4 9 9 8 8 7 7 12 12 11 11 10 10 15
[26] 15 14 14 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22 3 3
[51] 2 2 1 1 6 6 5 5 4 4 9 9 8 8 7 7 12 12 11 11 10 10 15 15 14
[76] 14 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22 3 3 2 2
[101] 1 1 6 6 5 5 4 4 9 9 8 8 7 7 12 12 11 11 10 10 15 15 14 14 13
[126] 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22 3 3 2 2 1 1
[151] 6 6 5 5 4 4 9 9 8 8 7 7 12 12 11 11 10 10 15 15 14 14 13 13 18
[176] 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22 3 3 2 2 1 1 6 6
[201] 5 5 4 4 9 9 8 8 7 7 12 12 11 11 10 10 15 15 14 14 13 13 18 18 17
[226] 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22 3 3 2 2 1 1 6 6 5 5
[251] 4 4 9 9 8 8 7 7 12 12 11 11 10 10 15 15 14 14 13 13 18 18 17 17 16
[276] 16 21 21 20 20 19 19 24 24 23 23 22 22 3 3 2 2 1 1 6 6 5 5 4 4
[301] 9 9 8 8 7 7 12 12 11 11 10 10 15 15 14 14 13 13 18 18 17 17 16 16 21
[326] 21 20 20 19 19 24 24 23 23 22 22 3 3 2 2 1 1 6 6 5 5 4 4 9 9
[351] 8 8 7 7 12 12 11 11 10 10 15 15 14 14 13 13 18 18 17 17 16 16 21 21 20
[376] 20 19 19 24 24 23 23 22 22 3 3 2 2 1 1 6 6 5 5 4 4 9 9 8 8
[401] 7 7 12 12 11 11 10 10 15 15 14 14 13 13 18 18 17 17 16 16 21 21 20 20 19
[426] 19 24 24 23 23 22 22 3 3 2 2 1 1 6 6 5 5 4 4 9 9 8 8 7 7
[451] 12 12 11 11 10 10 15 15 14 14 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24
[476] 24 23 23 22 22 3 3 2 2 1 1 6 6 5 5 4 4 9 9 8 8 7 7 12 12
[501] 11 11 10 10 15 15 14 14 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23
[526] 23 22 22 3 3 2 2 1 1 6 6 5 5 4 4 9 9 8 8 7 7 12 12 11 11
[551] 10 10 15 15 14 14 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22
[576] 22 3 3 2 2 1 1 6 6 5 5 4 4 9 9 8 8 7 7 12 12 11 11 10 10
[601] 15 15 14 14 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22 3
[626] 3 2 2 1 1 6 6 5 5 4 4 9 9 8 8 7 7 12 12 11 11 10 10 15 15
[651] 14 14 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22 3 3 2
[676] 2 1 1 6 6 5 5 4 4 9 9 8 8 7 7 12 12 11 11 10 10 15 15 14 14
[701] 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22 3 3 2 2 1
[726] 1 6 6 5 5 4 4 9 9 8 8 7 7 12 12 11 11 10 10 15 15 14 14 13 13
[751] 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22
$plateposition
[1] "p1D03" "p1D03" "p1D02" "p1D02" "p1D01" "p1D01" "p1D06" "p1D06" "p1D05"
[10] "p1D05" "p1D04" "p1D04" "p1D09" "p1D09" "p1D08" "p1D08" "p1D07" "p1D07"
[19] "p1D12" "p1D12" "p1D11" "p1D11" "p1D10" "p1D10" "p1D15" "p1D15" "p1D14"
[28] "p1D14" "p1D13" "p1D13" "p1D18" "p1D18" "p1D17" "p1D17" "p1D16" "p1D16"
[37] "p1D21" "p1D21" "p1D20" "p1D20" "p1D19" "p1D19" "p1D24" "p1D24" "p1D23"
[46] "p1D23" "p1D22" "p1D22" "p1C03" "p1C03" "p1C02" "p1C02" "p1C01" "p1C01"
[55] "p1C06" "p1C06" "p1C05" "p1C05" "p1C04" "p1C04" "p1C09" "p1C09" "p1C08"
[64] "p1C08" "p1C07" "p1C07" "p1C12" "p1C12" "p1C11" "p1C11" "p1C10" "p1C10"
[73] "p1C15" "p1C15" "p1C14" "p1C14" "p1C13" "p1C13" "p1C18" "p1C18" "p1C17"
[82] "p1C17" "p1C16" "p1C16" "p1C21" "p1C21" "p1C20" "p1C20" "p1C19" "p1C19"
[91] "p1C24" "p1C24" "p1C23" "p1C23" "p1C22" "p1C22" "p1B03" "p1B03" "p1B02"
[100] "p1B02" "p1B01" "p1B01" "p1B06" "p1B06" "p1B05" "p1B05" "p1B04" "p1B04"
[109] "p1B09" "p1B09" "p1B08" "p1B08" "p1B07" "p1B07" "p1B12" "p1B12" "p1B11"
[118] "p1B11" "p1B10" "p1B10" "p1B15" "p1B15" "p1B14" "p1B14" "p1B13" "p1B13"
[127] "p1B18" "p1B18" "p1B17" "p1B17" "p1B16" "p1B16" "p1B21" "p1B21" "p1B20"
[136] "p1B20" "p1B19" "p1B19" "p1B24" "p1B24" "p1B23" "p1B23" "p1B22" "p1B22"
[145] "p1A03" "p1A03" "p1A02" "p1A02" "p1A01" "p1A01" "p1A06" "p1A06" "p1A05"
[154] "p1A05" "p1A04" "p1A04" "p1A09" "p1A09" "p1A08" "p1A08" "p1A07" "p1A07"
[163] "p1A12" "p1A12" "p1A11" "p1A11" "p1A10" "p1A10" "p1A15" "p1A15" "p1A14"
[172] "p1A14" "p1A13" "p1A13" "p1A18" "p1A18" "p1A17" "p1A17" "p1A16" "p1A16"
[181] "p1A21" "p1A21" "p1A20" "p1A20" "p1A19" "p1A19" "p1A24" "p1A24" "p1A23"
[190] "p1A23" "p1A22" "p1A22" "p1H03" "p1H03" "p1H02" "p1H02" "p1H01" "p1H01"
[199] "p1H06" "p1H06" "p1H05" "p1H05" "p1H04" "p1H04" "p1H09" "p1H09" "p1H08"
[208] "p1H08" "p1H07" "p1H07" "p1H12" "p1H12" "p1H11" "p1H11" "p1H10" "p1H10"
[217] "p1H15" "p1H15" "p1H14" "p1H14" "p1H13" "p1H13" "p1H18" "p1H18" "p1H17"
[226] "p1H17" "p1H16" "p1H16" "p1H21" "p1H21" "p1H20" "p1H20" "p1H19" "p1H19"
[235] "p1H24" "p1H24" "p1H23" "p1H23" "p1H22" "p1H22" "p1G03" "p1G03" "p1G02"
[244] "p1G02" "p1G01" "p1G01" "p1G06" "p1G06" "p1G05" "p1G05" "p1G04" "p1G04"
[253] "p1G09" "p1G09" "p1G08" "p1G08" "p1G07" "p1G07" "p1G12" "p1G12" "p1G11"
[262] "p1G11" "p1G10" "p1G10" "p1G15" "p1G15" "p1G14" "p1G14" "p1G13" "p1G13"
[271] "p1G18" "p1G18" "p1G17" "p1G17" "p1G16" "p1G16" "p1G21" "p1G21" "p1G20"
[280] "p1G20" "p1G19" "p1G19" "p1G24" "p1G24" "p1G23" "p1G23" "p1G22" "p1G22"
[289] "p1F03" "p1F03" "p1F02" "p1F02" "p1F01" "p1F01" "p1F06" "p1F06" "p1F05"
[298] "p1F05" "p1F04" "p1F04" "p1F09" "p1F09" "p1F08" "p1F08" "p1F07" "p1F07"
[307] "p1F12" "p1F12" "p1F11" "p1F11" "p1F10" "p1F10" "p1F15" "p1F15" "p1F14"
[316] "p1F14" "p1F13" "p1F13" "p1F18" "p1F18" "p1F17" "p1F17" "p1F16" "p1F16"
[325] "p1F21" "p1F21" "p1F20" "p1F20" "p1F19" "p1F19" "p1F24" "p1F24" "p1F23"
[334] "p1F23" "p1F22" "p1F22" "p1E03" "p1E03" "p1E02" "p1E02" "p1E01" "p1E01"
[343] "p1E06" "p1E06" "p1E05" "p1E05" "p1E04" "p1E04" "p1E09" "p1E09" "p1E08"
[352] "p1E08" "p1E07" "p1E07" "p1E12" "p1E12" "p1E11" "p1E11" "p1E10" "p1E10"
[361] "p1E15" "p1E15" "p1E14" "p1E14" "p1E13" "p1E13" "p1E18" "p1E18" "p1E17"
[370] "p1E17" "p1E16" "p1E16" "p1E21" "p1E21" "p1E20" "p1E20" "p1E19" "p1E19"
[379] "p1E24" "p1E24" "p1E23" "p1E23" "p1E22" "p1E22" "p1L03" "p1L03" "p1L02"
[388] "p1L02" "p1L01" "p1L01" "p1L06" "p1L06" "p1L05" "p1L05" "p1L04" "p1L04"
[397] "p1L09" "p1L09" "p1L08" "p1L08" "p1L07" "p1L07" "p1L12" "p1L12" "p1L11"
[406] "p1L11" "p1L10" "p1L10" "p1L15" "p1L15" "p1L14" "p1L14" "p1L13" "p1L13"
[415] "p1L18" "p1L18" "p1L17" "p1L17" "p1L16" "p1L16" "p1L21" "p1L21" "p1L20"
[424] "p1L20" "p1L19" "p1L19" "p1L24" "p1L24" "p1L23" "p1L23" "p1L22" "p1L22"
[433] "p1K03" "p1K03" "p1K02" "p1K02" "p1K01" "p1K01" "p1K06" "p1K06" "p1K05"
[442] "p1K05" "p1K04" "p1K04" "p1K09" "p1K09" "p1K08" "p1K08" "p1K07" "p1K07"
[451] "p1K12" "p1K12" "p1K11" "p1K11" "p1K10" "p1K10" "p1K15" "p1K15" "p1K14"
[460] "p1K14" "p1K13" "p1K13" "p1K18" "p1K18" "p1K17" "p1K17" "p1K16" "p1K16"
[469] "p1K21" "p1K21" "p1K20" "p1K20" "p1K19" "p1K19" "p1K24" "p1K24" "p1K23"
[478] "p1K23" "p1K22" "p1K22" "p1J03" "p1J03" "p1J02" "p1J02" "p1J01" "p1J01"
[487] "p1J06" "p1J06" "p1J05" "p1J05" "p1J04" "p1J04" "p1J09" "p1J09" "p1J08"
[496] "p1J08" "p1J07" "p1J07" "p1J12" "p1J12" "p1J11" "p1J11" "p1J10" "p1J10"
[505] "p1J15" "p1J15" "p1J14" "p1J14" "p1J13" "p1J13" "p1J18" "p1J18" "p1J17"
[514] "p1J17" "p1J16" "p1J16" "p1J21" "p1J21" "p1J20" "p1J20" "p1J19" "p1J19"
[523] "p1J24" "p1J24" "p1J23" "p1J23" "p1J22" "p1J22" "p1I03" "p1I03" "p1I02"
[532] "p1I02" "p1I01" "p1I01" "p1I06" "p1I06" "p1I05" "p1I05" "p1I04" "p1I04"
[541] "p1I09" "p1I09" "p1I08" "p1I08" "p1I07" "p1I07" "p1I12" "p1I12" "p1I11"
[550] "p1I11" "p1I10" "p1I10" "p1I15" "p1I15" "p1I14" "p1I14" "p1I13" "p1I13"
[559] "p1I18" "p1I18" "p1I17" "p1I17" "p1I16" "p1I16" "p1I21" "p1I21" "p1I20"
[568] "p1I20" "p1I19" "p1I19" "p1I24" "p1I24" "p1I23" "p1I23" "p1I22" "p1I22"
[577] "p1P03" "p1P03" "p1P02" "p1P02" "p1P01" "p1P01" "p1P06" "p1P06" "p1P05"
[586] "p1P05" "p1P04" "p1P04" "p1P09" "p1P09" "p1P08" "p1P08" "p1P07" "p1P07"
[595] "p1P12" "p1P12" "p1P11" "p1P11" "p1P10" "p1P10" "p1P15" "p1P15" "p1P14"
[604] "p1P14" "p1P13" "p1P13" "p1P18" "p1P18" "p1P17" "p1P17" "p1P16" "p1P16"
[613] "p1P21" "p1P21" "p1P20" "p1P20" "p1P19" "p1P19" "p1P24" "p1P24" "p1P23"
[622] "p1P23" "p1P22" "p1P22" "p1O03" "p1O03" "p1O02" "p1O02" "p1O01" "p1O01"
[631] "p1O06" "p1O06" "p1O05" "p1O05" "p1O04" "p1O04" "p1O09" "p1O09" "p1O08"
[640] "p1O08" "p1O07" "p1O07" "p1O12" "p1O12" "p1O11" "p1O11" "p1O10" "p1O10"
[649] "p1O15" "p1O15" "p1O14" "p1O14" "p1O13" "p1O13" "p1O18" "p1O18" "p1O17"
[658] "p1O17" "p1O16" "p1O16" "p1O21" "p1O21" "p1O20" "p1O20" "p1O19" "p1O19"
[667] "p1O24" "p1O24" "p1O23" "p1O23" "p1O22" "p1O22" "p1N03" "p1N03" "p1N02"
[676] "p1N02" "p1N01" "p1N01" "p1N06" "p1N06" "p1N05" "p1N05" "p1N04" "p1N04"
[685] "p1N09" "p1N09" "p1N08" "p1N08" "p1N07" "p1N07" "p1N12" "p1N12" "p1N11"
[694] "p1N11" "p1N10" "p1N10" "p1N15" "p1N15" "p1N14" "p1N14" "p1N13" "p1N13"
[703] "p1N18" "p1N18" "p1N17" "p1N17" "p1N16" "p1N16" "p1N21" "p1N21" "p1N20"
[712] "p1N20" "p1N19" "p1N19" "p1N24" "p1N24" "p1N23" "p1N23" "p1N22" "p1N22"
[721] "p1M03" "p1M03" "p1M02" "p1M02" "p1M01" "p1M01" "p1M06" "p1M06" "p1M05"
[730] "p1M05" "p1M04" "p1M04" "p1M09" "p1M09" "p1M08" "p1M08" "p1M07" "p1M07"
[739] "p1M12" "p1M12" "p1M11" "p1M11" "p1M10" "p1M10" "p1M15" "p1M15" "p1M14"
[748] "p1M14" "p1M13" "p1M13" "p1M18" "p1M18" "p1M17" "p1M17" "p1M16" "p1M16"
[757] "p1M21" "p1M21" "p1M20" "p1M20" "p1M19" "p1M19" "p1M24" "p1M24" "p1M23"
[766] "p1M23" "p1M22" "p1M22"
> printorder(list(ngrid.r=4,ngrid.c=4,nspot.r=8,nspot.c=6))
$printorder
[1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
[26] 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 1 2
[51] 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27
[76] 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 1 2 3 4
[101] 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29
[126] 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 1 2 3 4 5 6
[151] 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31
[176] 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 1 2 3 4 5 6 7 8
[201] 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33
[226] 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 1 2 3 4 5 6 7 8 9 10
[251] 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35
[276] 36 37 38 39 40 41 42 43 44 45 46 47 48 1 2 3 4 5 6 7 8 9 10 11 12
[301] 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37
[326] 38 39 40 41 42 43 44 45 46 47 48 1 2 3 4 5 6 7 8 9 10 11 12 13 14
[351] 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39
[376] 40 41 42 43 44 45 46 47 48 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
[401] 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41
[426] 42 43 44 45 46 47 48 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
[451] 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43
[476] 44 45 46 47 48 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
[501] 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45
[526] 46 47 48 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22
[551] 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47
[576] 48 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
[601] 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 1
[626] 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26
[651] 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 1 2 3
[676] 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
[701] 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 1 2 3 4 5
[726] 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
[751] 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48
$plate
[1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2
[38] 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2
[75] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[112] 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1
[149] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[186] 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2
[223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[260] 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1
[297] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[334] 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2
[371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[408] 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1
[445] 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1
[482] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[519] 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2
[556] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[593] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1
[630] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[667] 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2
[704] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[741] 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
$plate.r
[1] 4 4 4 4 4 4 8 8 8 8 8 8 12 12 12 12 12 12 16 16 16 16 16 16 4
[26] 4 4 4 4 4 8 8 8 8 8 8 12 12 12 12 12 12 16 16 16 16 16 16 3 3
[51] 3 3 3 3 7 7 7 7 7 7 11 11 11 11 11 11 15 15 15 15 15 15 3 3 3
[76] 3 3 3 7 7 7 7 7 7 11 11 11 11 11 11 15 15 15 15 15 15 2 2 2 2
[101] 2 2 6 6 6 6 6 6 10 10 10 10 10 10 14 14 14 14 14 14 2 2 2 2 2
[126] 2 6 6 6 6 6 6 10 10 10 10 10 10 14 14 14 14 14 14 1 1 1 1 1 1
[151] 5 5 5 5 5 5 9 9 9 9 9 9 13 13 13 13 13 13 1 1 1 1 1 1 5
[176] 5 5 5 5 5 9 9 9 9 9 9 13 13 13 13 13 13 4 4 4 4 4 4 8 8
[201] 8 8 8 8 12 12 12 12 12 12 16 16 16 16 16 16 4 4 4 4 4 4 8 8 8
[226] 8 8 8 12 12 12 12 12 12 16 16 16 16 16 16 3 3 3 3 3 3 7 7 7 7
[251] 7 7 11 11 11 11 11 11 15 15 15 15 15 15 3 3 3 3 3 3 7 7 7 7 7
[276] 7 11 11 11 11 11 11 15 15 15 15 15 15 2 2 2 2 2 2 6 6 6 6 6 6
[301] 10 10 10 10 10 10 14 14 14 14 14 14 2 2 2 2 2 2 6 6 6 6 6 6 10
[326] 10 10 10 10 10 14 14 14 14 14 14 1 1 1 1 1 1 5 5 5 5 5 5 9 9
[351] 9 9 9 9 13 13 13 13 13 13 1 1 1 1 1 1 5 5 5 5 5 5 9 9 9
[376] 9 9 9 13 13 13 13 13 13 4 4 4 4 4 4 8 8 8 8 8 8 12 12 12 12
[401] 12 12 16 16 16 16 16 16 4 4 4 4 4 4 8 8 8 8 8 8 12 12 12 12 12
[426] 12 16 16 16 16 16 16 3 3 3 3 3 3 7 7 7 7 7 7 11 11 11 11 11 11
[451] 15 15 15 15 15 15 3 3 3 3 3 3 7 7 7 7 7 7 11 11 11 11 11 11 15
[476] 15 15 15 15 15 2 2 2 2 2 2 6 6 6 6 6 6 10 10 10 10 10 10 14 14
[501] 14 14 14 14 2 2 2 2 2 2 6 6 6 6 6 6 10 10 10 10 10 10 14 14 14
[526] 14 14 14 1 1 1 1 1 1 5 5 5 5 5 5 9 9 9 9 9 9 13 13 13 13
[551] 13 13 1 1 1 1 1 1 5 5 5 5 5 5 9 9 9 9 9 9 13 13 13 13 13
[576] 13 4 4 4 4 4 4 8 8 8 8 8 8 12 12 12 12 12 12 16 16 16 16 16 16
[601] 4 4 4 4 4 4 8 8 8 8 8 8 12 12 12 12 12 12 16 16 16 16 16 16 3
[626] 3 3 3 3 3 7 7 7 7 7 7 11 11 11 11 11 11 15 15 15 15 15 15 3 3
[651] 3 3 3 3 7 7 7 7 7 7 11 11 11 11 11 11 15 15 15 15 15 15 2 2 2
[676] 2 2 2 6 6 6 6 6 6 10 10 10 10 10 10 14 14 14 14 14 14 2 2 2 2
[701] 2 2 6 6 6 6 6 6 10 10 10 10 10 10 14 14 14 14 14 14 1 1 1 1 1
[726] 1 5 5 5 5 5 5 9 9 9 9 9 9 13 13 13 13 13 13 1 1 1 1 1 1
[751] 5 5 5 5 5 5 9 9 9 9 9 9 13 13 13 13 13 13
$plate.c
[1] 1 5 9 13 17 21 1 5 9 13 17 21 1 5 9 13 17 21 1 5 9 13 17 21 1
[26] 5 9 13 17 21 1 5 9 13 17 21 1 5 9 13 17 21 1 5 9 13 17 21 1 5
[51] 9 13 17 21 1 5 9 13 17 21 1 5 9 13 17 21 1 5 9 13 17 21 1 5 9
[76] 13 17 21 1 5 9 13 17 21 1 5 9 13 17 21 1 5 9 13 17 21 1 5 9 13
[101] 17 21 1 5 9 13 17 21 1 5 9 13 17 21 1 5 9 13 17 21 1 5 9 13 17
[126] 21 1 5 9 13 17 21 1 5 9 13 17 21 1 5 9 13 17 21 1 5 9 13 17 21
[151] 1 5 9 13 17 21 1 5 9 13 17 21 1 5 9 13 17 21 1 5 9 13 17 21 1
[176] 5 9 13 17 21 1 5 9 13 17 21 1 5 9 13 17 21 2 6 10 14 18 22 2 6
[201] 10 14 18 22 2 6 10 14 18 22 2 6 10 14 18 22 2 6 10 14 18 22 2 6 10
[226] 14 18 22 2 6 10 14 18 22 2 6 10 14 18 22 2 6 10 14 18 22 2 6 10 14
[251] 18 22 2 6 10 14 18 22 2 6 10 14 18 22 2 6 10 14 18 22 2 6 10 14 18
[276] 22 2 6 10 14 18 22 2 6 10 14 18 22 2 6 10 14 18 22 2 6 10 14 18 22
[301] 2 6 10 14 18 22 2 6 10 14 18 22 2 6 10 14 18 22 2 6 10 14 18 22 2
[326] 6 10 14 18 22 2 6 10 14 18 22 2 6 10 14 18 22 2 6 10 14 18 22 2 6
[351] 10 14 18 22 2 6 10 14 18 22 2 6 10 14 18 22 2 6 10 14 18 22 2 6 10
[376] 14 18 22 2 6 10 14 18 22 3 7 11 15 19 23 3 7 11 15 19 23 3 7 11 15
[401] 19 23 3 7 11 15 19 23 3 7 11 15 19 23 3 7 11 15 19 23 3 7 11 15 19
[426] 23 3 7 11 15 19 23 3 7 11 15 19 23 3 7 11 15 19 23 3 7 11 15 19 23
[451] 3 7 11 15 19 23 3 7 11 15 19 23 3 7 11 15 19 23 3 7 11 15 19 23 3
[476] 7 11 15 19 23 3 7 11 15 19 23 3 7 11 15 19 23 3 7 11 15 19 23 3 7
[501] 11 15 19 23 3 7 11 15 19 23 3 7 11 15 19 23 3 7 11 15 19 23 3 7 11
[526] 15 19 23 3 7 11 15 19 23 3 7 11 15 19 23 3 7 11 15 19 23 3 7 11 15
[551] 19 23 3 7 11 15 19 23 3 7 11 15 19 23 3 7 11 15 19 23 3 7 11 15 19
[576] 23 4 8 12 16 20 24 4 8 12 16 20 24 4 8 12 16 20 24 4 8 12 16 20 24
[601] 4 8 12 16 20 24 4 8 12 16 20 24 4 8 12 16 20 24 4 8 12 16 20 24 4
[626] 8 12 16 20 24 4 8 12 16 20 24 4 8 12 16 20 24 4 8 12 16 20 24 4 8
[651] 12 16 20 24 4 8 12 16 20 24 4 8 12 16 20 24 4 8 12 16 20 24 4 8 12
[676] 16 20 24 4 8 12 16 20 24 4 8 12 16 20 24 4 8 12 16 20 24 4 8 12 16
[701] 20 24 4 8 12 16 20 24 4 8 12 16 20 24 4 8 12 16 20 24 4 8 12 16 20
[726] 24 4 8 12 16 20 24 4 8 12 16 20 24 4 8 12 16 20 24 4 8 12 16 20 24
[751] 4 8 12 16 20 24 4 8 12 16 20 24 4 8 12 16 20 24
$plateposition
[1] "p1D01" "p1D05" "p1D09" "p1D13" "p1D17" "p1D21" "p1H01" "p1H05" "p1H09"
[10] "p1H13" "p1H17" "p1H21" "p1L01" "p1L05" "p1L09" "p1L13" "p1L17" "p1L21"
[19] "p1P01" "p1P05" "p1P09" "p1P13" "p1P17" "p1P21" "p2D01" "p2D05" "p2D09"
[28] "p2D13" "p2D17" "p2D21" "p2H01" "p2H05" "p2H09" "p2H13" "p2H17" "p2H21"
[37] "p2L01" "p2L05" "p2L09" "p2L13" "p2L17" "p2L21" "p2P01" "p2P05" "p2P09"
[46] "p2P13" "p2P17" "p2P21" "p1C01" "p1C05" "p1C09" "p1C13" "p1C17" "p1C21"
[55] "p1G01" "p1G05" "p1G09" "p1G13" "p1G17" "p1G21" "p1K01" "p1K05" "p1K09"
[64] "p1K13" "p1K17" "p1K21" "p1O01" "p1O05" "p1O09" "p1O13" "p1O17" "p1O21"
[73] "p2C01" "p2C05" "p2C09" "p2C13" "p2C17" "p2C21" "p2G01" "p2G05" "p2G09"
[82] "p2G13" "p2G17" "p2G21" "p2K01" "p2K05" "p2K09" "p2K13" "p2K17" "p2K21"
[91] "p2O01" "p2O05" "p2O09" "p2O13" "p2O17" "p2O21" "p1B01" "p1B05" "p1B09"
[100] "p1B13" "p1B17" "p1B21" "p1F01" "p1F05" "p1F09" "p1F13" "p1F17" "p1F21"
[109] "p1J01" "p1J05" "p1J09" "p1J13" "p1J17" "p1J21" "p1N01" "p1N05" "p1N09"
[118] "p1N13" "p1N17" "p1N21" "p2B01" "p2B05" "p2B09" "p2B13" "p2B17" "p2B21"
[127] "p2F01" "p2F05" "p2F09" "p2F13" "p2F17" "p2F21" "p2J01" "p2J05" "p2J09"
[136] "p2J13" "p2J17" "p2J21" "p2N01" "p2N05" "p2N09" "p2N13" "p2N17" "p2N21"
[145] "p1A01" "p1A05" "p1A09" "p1A13" "p1A17" "p1A21" "p1E01" "p1E05" "p1E09"
[154] "p1E13" "p1E17" "p1E21" "p1I01" "p1I05" "p1I09" "p1I13" "p1I17" "p1I21"
[163] "p1M01" "p1M05" "p1M09" "p1M13" "p1M17" "p1M21" "p2A01" "p2A05" "p2A09"
[172] "p2A13" "p2A17" "p2A21" "p2E01" "p2E05" "p2E09" "p2E13" "p2E17" "p2E21"
[181] "p2I01" "p2I05" "p2I09" "p2I13" "p2I17" "p2I21" "p2M01" "p2M05" "p2M09"
[190] "p2M13" "p2M17" "p2M21" "p1D02" "p1D06" "p1D10" "p1D14" "p1D18" "p1D22"
[199] "p1H02" "p1H06" "p1H10" "p1H14" "p1H18" "p1H22" "p1L02" "p1L06" "p1L10"
[208] "p1L14" "p1L18" "p1L22" "p1P02" "p1P06" "p1P10" "p1P14" "p1P18" "p1P22"
[217] "p2D02" "p2D06" "p2D10" "p2D14" "p2D18" "p2D22" "p2H02" "p2H06" "p2H10"
[226] "p2H14" "p2H18" "p2H22" "p2L02" "p2L06" "p2L10" "p2L14" "p2L18" "p2L22"
[235] "p2P02" "p2P06" "p2P10" "p2P14" "p2P18" "p2P22" "p1C02" "p1C06" "p1C10"
[244] "p1C14" "p1C18" "p1C22" "p1G02" "p1G06" "p1G10" "p1G14" "p1G18" "p1G22"
[253] "p1K02" "p1K06" "p1K10" "p1K14" "p1K18" "p1K22" "p1O02" "p1O06" "p1O10"
[262] "p1O14" "p1O18" "p1O22" "p2C02" "p2C06" "p2C10" "p2C14" "p2C18" "p2C22"
[271] "p2G02" "p2G06" "p2G10" "p2G14" "p2G18" "p2G22" "p2K02" "p2K06" "p2K10"
[280] "p2K14" "p2K18" "p2K22" "p2O02" "p2O06" "p2O10" "p2O14" "p2O18" "p2O22"
[289] "p1B02" "p1B06" "p1B10" "p1B14" "p1B18" "p1B22" "p1F02" "p1F06" "p1F10"
[298] "p1F14" "p1F18" "p1F22" "p1J02" "p1J06" "p1J10" "p1J14" "p1J18" "p1J22"
[307] "p1N02" "p1N06" "p1N10" "p1N14" "p1N18" "p1N22" "p2B02" "p2B06" "p2B10"
[316] "p2B14" "p2B18" "p2B22" "p2F02" "p2F06" "p2F10" "p2F14" "p2F18" "p2F22"
[325] "p2J02" "p2J06" "p2J10" "p2J14" "p2J18" "p2J22" "p2N02" "p2N06" "p2N10"
[334] "p2N14" "p2N18" "p2N22" "p1A02" "p1A06" "p1A10" "p1A14" "p1A18" "p1A22"
[343] "p1E02" "p1E06" "p1E10" "p1E14" "p1E18" "p1E22" "p1I02" "p1I06" "p1I10"
[352] "p1I14" "p1I18" "p1I22" "p1M02" "p1M06" "p1M10" "p1M14" "p1M18" "p1M22"
[361] "p2A02" "p2A06" "p2A10" "p2A14" "p2A18" "p2A22" "p2E02" "p2E06" "p2E10"
[370] "p2E14" "p2E18" "p2E22" "p2I02" "p2I06" "p2I10" "p2I14" "p2I18" "p2I22"
[379] "p2M02" "p2M06" "p2M10" "p2M14" "p2M18" "p2M22" "p1D03" "p1D07" "p1D11"
[388] "p1D15" "p1D19" "p1D23" "p1H03" "p1H07" "p1H11" "p1H15" "p1H19" "p1H23"
[397] "p1L03" "p1L07" "p1L11" "p1L15" "p1L19" "p1L23" "p1P03" "p1P07" "p1P11"
[406] "p1P15" "p1P19" "p1P23" "p2D03" "p2D07" "p2D11" "p2D15" "p2D19" "p2D23"
[415] "p2H03" "p2H07" "p2H11" "p2H15" "p2H19" "p2H23" "p2L03" "p2L07" "p2L11"
[424] "p2L15" "p2L19" "p2L23" "p2P03" "p2P07" "p2P11" "p2P15" "p2P19" "p2P23"
[433] "p1C03" "p1C07" "p1C11" "p1C15" "p1C19" "p1C23" "p1G03" "p1G07" "p1G11"
[442] "p1G15" "p1G19" "p1G23" "p1K03" "p1K07" "p1K11" "p1K15" "p1K19" "p1K23"
[451] "p1O03" "p1O07" "p1O11" "p1O15" "p1O19" "p1O23" "p2C03" "p2C07" "p2C11"
[460] "p2C15" "p2C19" "p2C23" "p2G03" "p2G07" "p2G11" "p2G15" "p2G19" "p2G23"
[469] "p2K03" "p2K07" "p2K11" "p2K15" "p2K19" "p2K23" "p2O03" "p2O07" "p2O11"
[478] "p2O15" "p2O19" "p2O23" "p1B03" "p1B07" "p1B11" "p1B15" "p1B19" "p1B23"
[487] "p1F03" "p1F07" "p1F11" "p1F15" "p1F19" "p1F23" "p1J03" "p1J07" "p1J11"
[496] "p1J15" "p1J19" "p1J23" "p1N03" "p1N07" "p1N11" "p1N15" "p1N19" "p1N23"
[505] "p2B03" "p2B07" "p2B11" "p2B15" "p2B19" "p2B23" "p2F03" "p2F07" "p2F11"
[514] "p2F15" "p2F19" "p2F23" "p2J03" "p2J07" "p2J11" "p2J15" "p2J19" "p2J23"
[523] "p2N03" "p2N07" "p2N11" "p2N15" "p2N19" "p2N23" "p1A03" "p1A07" "p1A11"
[532] "p1A15" "p1A19" "p1A23" "p1E03" "p1E07" "p1E11" "p1E15" "p1E19" "p1E23"
[541] "p1I03" "p1I07" "p1I11" "p1I15" "p1I19" "p1I23" "p1M03" "p1M07" "p1M11"
[550] "p1M15" "p1M19" "p1M23" "p2A03" "p2A07" "p2A11" "p2A15" "p2A19" "p2A23"
[559] "p2E03" "p2E07" "p2E11" "p2E15" "p2E19" "p2E23" "p2I03" "p2I07" "p2I11"
[568] "p2I15" "p2I19" "p2I23" "p2M03" "p2M07" "p2M11" "p2M15" "p2M19" "p2M23"
[577] "p1D04" "p1D08" "p1D12" "p1D16" "p1D20" "p1D24" "p1H04" "p1H08" "p1H12"
[586] "p1H16" "p1H20" "p1H24" "p1L04" "p1L08" "p1L12" "p1L16" "p1L20" "p1L24"
[595] "p1P04" "p1P08" "p1P12" "p1P16" "p1P20" "p1P24" "p2D04" "p2D08" "p2D12"
[604] "p2D16" "p2D20" "p2D24" "p2H04" "p2H08" "p2H12" "p2H16" "p2H20" "p2H24"
[613] "p2L04" "p2L08" "p2L12" "p2L16" "p2L20" "p2L24" "p2P04" "p2P08" "p2P12"
[622] "p2P16" "p2P20" "p2P24" "p1C04" "p1C08" "p1C12" "p1C16" "p1C20" "p1C24"
[631] "p1G04" "p1G08" "p1G12" "p1G16" "p1G20" "p1G24" "p1K04" "p1K08" "p1K12"
[640] "p1K16" "p1K20" "p1K24" "p1O04" "p1O08" "p1O12" "p1O16" "p1O20" "p1O24"
[649] "p2C04" "p2C08" "p2C12" "p2C16" "p2C20" "p2C24" "p2G04" "p2G08" "p2G12"
[658] "p2G16" "p2G20" "p2G24" "p2K04" "p2K08" "p2K12" "p2K16" "p2K20" "p2K24"
[667] "p2O04" "p2O08" "p2O12" "p2O16" "p2O20" "p2O24" "p1B04" "p1B08" "p1B12"
[676] "p1B16" "p1B20" "p1B24" "p1F04" "p1F08" "p1F12" "p1F16" "p1F20" "p1F24"
[685] "p1J04" "p1J08" "p1J12" "p1J16" "p1J20" "p1J24" "p1N04" "p1N08" "p1N12"
[694] "p1N16" "p1N20" "p1N24" "p2B04" "p2B08" "p2B12" "p2B16" "p2B20" "p2B24"
[703] "p2F04" "p2F08" "p2F12" "p2F16" "p2F20" "p2F24" "p2J04" "p2J08" "p2J12"
[712] "p2J16" "p2J20" "p2J24" "p2N04" "p2N08" "p2N12" "p2N16" "p2N20" "p2N24"
[721] "p1A04" "p1A08" "p1A12" "p1A16" "p1A20" "p1A24" "p1E04" "p1E08" "p1E12"
[730] "p1E16" "p1E20" "p1E24" "p1I04" "p1I08" "p1I12" "p1I16" "p1I20" "p1I24"
[739] "p1M04" "p1M08" "p1M12" "p1M16" "p1M20" "p1M24" "p2A04" "p2A08" "p2A12"
[748] "p2A16" "p2A20" "p2A24" "p2E04" "p2E08" "p2E12" "p2E16" "p2E20" "p2E24"
[757] "p2I04" "p2I08" "p2I12" "p2I16" "p2I20" "p2I24" "p2M04" "p2M08" "p2M12"
[766] "p2M16" "p2M20" "p2M24"
>
> ### merge.rglist
>
> R <- G <- matrix(11:14,4,2)
> rownames(R) <- rownames(G) <- c("a","a","b","c")
> RG1 <- new("RGList",list(R=R,G=G))
> R <- G <- matrix(21:24,4,2)
> rownames(R) <- rownames(G) <- c("b","a","a","c")
> RG2 <- new("RGList",list(R=R,G=G))
> merge(RG1,RG2)
An object of class "RGList"
$R
[,1] [,2] [,3] [,4]
a 11 11 22 22
a 12 12 23 23
b 13 13 21 21
c 14 14 24 24
$G
[,1] [,2] [,3] [,4]
a 11 11 22 22
a 12 12 23 23
b 13 13 21 21
c 14 14 24 24
> merge(RG2,RG1)
An object of class "RGList"
$R
[,1] [,2] [,3] [,4]
b 21 21 13 13
a 22 22 11 11
a 23 23 12 12
c 24 24 14 14
$G
[,1] [,2] [,3] [,4]
b 21 21 13 13
a 22 22 11 11
a 23 23 12 12
c 24 24 14 14
>
> ### background correction
>
> RG <- new("RGList", list(R=c(1,2,3,4),G=c(1,2,3,4),Rb=c(2,2,2,2),Gb=c(2,2,2,2)))
> backgroundCorrect(RG)
An object of class "RGList"
$R
[,1]
[1,] -1
[2,] 0
[3,] 1
[4,] 2
$G
[,1]
[1,] -1
[2,] 0
[3,] 1
[4,] 2
> backgroundCorrect(RG, method="half")
An object of class "RGList"
$R
[,1]
[1,] 0.5
[2,] 0.5
[3,] 1.0
[4,] 2.0
$G
[,1]
[1,] 0.5
[2,] 0.5
[3,] 1.0
[4,] 2.0
> backgroundCorrect(RG, method="minimum")
An object of class "RGList"
$R
[,1]
[1,] 0.5
[2,] 0.5
[3,] 1.0
[4,] 2.0
$G
[,1]
[1,] 0.5
[2,] 0.5
[3,] 1.0
[4,] 2.0
> backgroundCorrect(RG, offset=5)
An object of class "RGList"
$R
[,1]
[1,] 4
[2,] 5
[3,] 6
[4,] 7
$G
[,1]
[1,] 4
[2,] 5
[3,] 6
[4,] 7
>
> ### loessFit
>
> x <- 1:100
> y <- rnorm(100)
> out <- loessFit(y,x)
> f1 <- quantile(out$fitted)
> r1 <- quantile(out$residual)
> w <- rep(1,100)
> w[1:50] <- 0.5
> out <- loessFit(y,x,weights=w,method="weightedLowess")
> f2 <- quantile(out$fitted)
> r2 <- quantile(out$residual)
> out <- loessFit(y,x,weights=w,method="locfit")
> f3 <- quantile(out$fitted)
> r3 <- quantile(out$residual)
> out <- loessFit(y,x,weights=w,method="loess")
> f4 <- quantile(out$fitted)
> r4 <- quantile(out$residual)
> w <- rep(1,100)
> w[2*(1:50)] <- 0
> out <- loessFit(y,x,weights=w,method="weightedLowess")
> f5 <- quantile(out$fitted)
> r5 <- quantile(out$residual)
> data.frame(f1,f2,f3,f4,f5)
f1 f2 f3 f4 f5
0% -0.78835384 -0.687432210 -0.78957137 -0.76756060 -0.63778292
25% -0.18340154 -0.179683572 -0.18979269 -0.16773223 -0.38064318
50% -0.11492924 -0.114796040 -0.12087983 -0.07185314 -0.15971879
75% 0.01507921 -0.008145125 -0.01857508 0.04030634 0.07839396
100% 0.21653837 0.145106033 0.19214597 0.21417361 0.51836274
> data.frame(r1,r2,r3,r4,r5)
r1 r2 r3 r4 r5
0% -2.04434053 -2.05132680 -2.02404318 -2.101242874 -2.22280633
25% -0.59321065 -0.57200209 -0.58975649 -0.577887481 -0.71037756
50% 0.05874864 0.04514326 0.08335198 -0.001769806 0.06785517
75% 0.56010750 0.55124530 0.57618740 0.561454370 0.65383830
100% 2.57936026 2.64549799 2.57549257 2.402324533 2.28648835
>
> ### normalizeWithinArrays
>
> RG <- new("RGList",list())
> RG$R <- matrix(rexp(100*2),100,2)
> RG$G <- matrix(rexp(100*2),100,2)
> RG$Rb <- matrix(rnorm(100*2,sd=0.02),100,2)
> RG$Gb <- matrix(rnorm(100*2,sd=0.02),100,2)
> RGb <- backgroundCorrect(RG,method="normexp",normexp.method="saddle")
Array 1 corrected
Array 2 corrected
Array 1 corrected
Array 2 corrected
> summary(cbind(RGb$R,RGb$G))
V1 V2 V3 V4
Min. :0.01626 Min. :0.01213 Min. :0.0000 Min. :0.0000
1st Qu.:0.35497 1st Qu.:0.29133 1st Qu.:0.2745 1st Qu.:0.3953
Median :0.71793 Median :0.70294 Median :0.6339 Median :0.8223
Mean :0.90184 Mean :1.00122 Mean :0.9454 Mean :1.1324
3rd Qu.:1.16891 3rd Qu.:1.33139 3rd Qu.:1.4059 3rd Qu.:1.4221
Max. :4.56267 Max. :6.37947 Max. :5.0486 Max. :6.6295
> RGb <- backgroundCorrect(RG,method="normexp",normexp.method="mle")
Array 1 corrected
Array 2 corrected
Array 1 corrected
Array 2 corrected
> summary(cbind(RGb$R,RGb$G))
V1 V2 V3 V4
Min. :0.01701 Min. :0.01255 Min. :0.0000 Min. :0.0000
1st Qu.:0.35423 1st Qu.:0.29118 1st Qu.:0.2745 1st Qu.:0.3953
Median :0.71719 Median :0.70280 Median :0.6339 Median :0.8223
Mean :0.90118 Mean :1.00110 Mean :0.9454 Mean :1.1324
3rd Qu.:1.16817 3rd Qu.:1.33124 3rd Qu.:1.4059 3rd Qu.:1.4221
Max. :4.56193 Max. :6.37932 Max. :5.0486 Max. :6.6295
> MA <- normalizeWithinArrays(RGb,method="loess")
> summary(MA$M)
V1 V2
Min. :-5.88044 Min. :-5.66985
1st Qu.:-1.18483 1st Qu.:-1.57014
Median :-0.21632 Median : 0.04823
Mean : 0.03487 Mean :-0.05481
3rd Qu.: 1.49669 3rd Qu.: 1.45113
Max. : 7.07324 Max. : 6.19744
> #MA <- normalizeWithinArrays(RG[,1:2], mouse.setup, method="robustspline")
> #MA$M[1:5,]
> #MA <- normalizeWithinArrays(mouse.data, mouse.setup)
> #MA$M[1:5,]
>
> ### normalizeBetweenArrays
>
> MA2 <- normalizeBetweenArrays(MA,method="scale")
> MA$M[1:5,]
[,1] [,2]
[1,] -1.1689588 4.5558123
[2,] 0.8971363 0.3296544
[3,] 2.8247439 1.4249960
[4,] -1.8533240 0.4804851
[5,] 1.9158459 -5.5087631
> MA$A[1:5,]
[,1] [,2]
[1,] -2.48465011 -2.4041550
[2,] -0.79230447 -0.9002250
[3,] -0.76237200 0.2071043
[4,] 0.09281027 -1.3880965
[5,] 0.22385828 -3.0855818
> MA2 <- normalizeBetweenArrays(MA,method="quantile")
> MA$M[1:5,]
[,1] [,2]
[1,] -1.1689588 4.5558123
[2,] 0.8971363 0.3296544
[3,] 2.8247439 1.4249960
[4,] -1.8533240 0.4804851
[5,] 1.9158459 -5.5087631
> MA$A[1:5,]
[,1] [,2]
[1,] -2.48465011 -2.4041550
[2,] -0.79230447 -0.9002250
[3,] -0.76237200 0.2071043
[4,] 0.09281027 -1.3880965
[5,] 0.22385828 -3.0855818
>
> ### unwrapdups
>
> M <- matrix(1:12,6,2)
> unwrapdups(M,ndups=1)
[,1] [,2]
[1,] 1 7
[2,] 2 8
[3,] 3 9
[4,] 4 10
[5,] 5 11
[6,] 6 12
> unwrapdups(M,ndups=2)
[,1] [,2] [,3] [,4]
[1,] 1 2 7 8
[2,] 3 4 9 10
[3,] 5 6 11 12
> unwrapdups(M,ndups=3)
[,1] [,2] [,3] [,4] [,5] [,6]
[1,] 1 2 3 7 8 9
[2,] 4 5 6 10 11 12
> unwrapdups(M,ndups=2,spacing=3)
[,1] [,2] [,3] [,4]
[1,] 1 4 7 10
[2,] 2 5 8 11
[3,] 3 6 9 12
>
> ### trigammaInverse
>
> trigammaInverse(c(1e-6,NA,5,1e6))
[1] 1.000000e+06 NA 4.961687e-01 1.000001e-03
>
> ### lmFit, eBayes, topTable
>
> M <- matrix(rnorm(10*6,sd=0.3),10,6)
> rownames(M) <- LETTERS[1:10]
> M[1,1:3] <- M[1,1:3] + 2
> design <- cbind(First3Arrays=c(1,1,1,0,0,0),Last3Arrays=c(0,0,0,1,1,1))
> contrast.matrix <- cbind(First3=c(1,0),Last3=c(0,1),"Last3-First3"=c(-1,1))
> fit <- lmFit(M,design)
> fit2 <- eBayes(contrasts.fit(fit,contrasts=contrast.matrix))
> topTable(fit2)
First3 Last3 Last3.First3 AveExpr F P.Value
A 1.77602021 0.06025114 -1.71576906 0.918135675 50.91471061 7.727200e-23
D -0.05454069 0.39127869 0.44581938 0.168369004 2.51638838 8.075072e-02
F -0.16249607 -0.33009728 -0.16760121 -0.246296671 2.18256779 1.127516e-01
G 0.30852468 -0.06873462 -0.37725930 0.119895035 1.61088775 1.997102e-01
H -0.16942269 0.20578118 0.37520387 0.018179245 1.14554368 3.180510e-01
J 0.21417623 0.07074940 -0.14342683 0.142462814 0.82029274 4.403027e-01
C -0.12236781 0.15095948 0.27332729 0.014295836 0.60885003 5.439761e-01
B -0.11982833 0.13529287 0.25512120 0.007732271 0.52662792 5.905931e-01
E 0.01897934 0.10434934 0.08536999 0.061664340 0.18136849 8.341279e-01
I -0.04720963 0.03996397 0.08717360 -0.003622829 0.06168476 9.401792e-01
adj.P.Val
A 7.727200e-22
D 3.758388e-01
F 3.758388e-01
G 4.992756e-01
H 6.361019e-01
J 7.338379e-01
C 7.382414e-01
B 7.382414e-01
E 9.268088e-01
I 9.401792e-01
> topTable(fit2,coef=3,resort.by="logFC")
logFC AveExpr t P.Value adj.P.Val B
D 0.44581938 0.168369004 1.7901232 8.100587e-02 3.494414e-01 -5.323150
H 0.37520387 0.018179245 1.5065768 1.397766e-01 3.494414e-01 -5.785971
C 0.27332729 0.014295836 1.0975061 2.789833e-01 5.196681e-01 -6.313399
B 0.25512120 0.007732271 1.0244023 3.118009e-01 5.196681e-01 -6.390202
I 0.08717360 -0.003622829 0.3500330 7.281504e-01 7.335508e-01 -6.849117
E 0.08536999 0.061664340 0.3427908 7.335508e-01 7.335508e-01 -6.851601
J -0.14342683 0.142462814 -0.5759097 5.679026e-01 7.098782e-01 -6.745563
F -0.16760121 -0.246296671 -0.6729784 5.048308e-01 7.098782e-01 -6.685541
G -0.37725930 0.119895035 -1.5148301 1.376783e-01 3.494414e-01 -5.773625
A -1.71576906 0.918135675 -6.8894222 2.674199e-08 2.674199e-07 16.590631
> topTable(fit2,coef=3,resort.by="p")
logFC AveExpr t P.Value adj.P.Val B
A -1.71576906 0.918135675 -6.8894222 2.674199e-08 2.674199e-07 16.590631
D 0.44581938 0.168369004 1.7901232 8.100587e-02 3.494414e-01 -5.323150
G -0.37725930 0.119895035 -1.5148301 1.376783e-01 3.494414e-01 -5.773625
H 0.37520387 0.018179245 1.5065768 1.397766e-01 3.494414e-01 -5.785971
C 0.27332729 0.014295836 1.0975061 2.789833e-01 5.196681e-01 -6.313399
B 0.25512120 0.007732271 1.0244023 3.118009e-01 5.196681e-01 -6.390202
F -0.16760121 -0.246296671 -0.6729784 5.048308e-01 7.098782e-01 -6.685541
J -0.14342683 0.142462814 -0.5759097 5.679026e-01 7.098782e-01 -6.745563
I 0.08717360 -0.003622829 0.3500330 7.281504e-01 7.335508e-01 -6.849117
E 0.08536999 0.061664340 0.3427908 7.335508e-01 7.335508e-01 -6.851601
> topTable(fit2,coef=3,sort="logFC",resort.by="t")
logFC AveExpr t P.Value adj.P.Val B
D 0.44581938 0.168369004 1.7901232 8.100587e-02 3.494414e-01 -5.323150
H 0.37520387 0.018179245 1.5065768 1.397766e-01 3.494414e-01 -5.785971
C 0.27332729 0.014295836 1.0975061 2.789833e-01 5.196681e-01 -6.313399
B 0.25512120 0.007732271 1.0244023 3.118009e-01 5.196681e-01 -6.390202
I 0.08717360 -0.003622829 0.3500330 7.281504e-01 7.335508e-01 -6.849117
E 0.08536999 0.061664340 0.3427908 7.335508e-01 7.335508e-01 -6.851601
J -0.14342683 0.142462814 -0.5759097 5.679026e-01 7.098782e-01 -6.745563
F -0.16760121 -0.246296671 -0.6729784 5.048308e-01 7.098782e-01 -6.685541
G -0.37725930 0.119895035 -1.5148301 1.376783e-01 3.494414e-01 -5.773625
A -1.71576906 0.918135675 -6.8894222 2.674199e-08 2.674199e-07 16.590631
> topTable(fit2,coef=3,resort.by="B")
logFC AveExpr t P.Value adj.P.Val B
A -1.71576906 0.918135675 -6.8894222 2.674199e-08 2.674199e-07 16.590631
D 0.44581938 0.168369004 1.7901232 8.100587e-02 3.494414e-01 -5.323150
G -0.37725930 0.119895035 -1.5148301 1.376783e-01 3.494414e-01 -5.773625
H 0.37520387 0.018179245 1.5065768 1.397766e-01 3.494414e-01 -5.785971
C 0.27332729 0.014295836 1.0975061 2.789833e-01 5.196681e-01 -6.313399
B 0.25512120 0.007732271 1.0244023 3.118009e-01 5.196681e-01 -6.390202
F -0.16760121 -0.246296671 -0.6729784 5.048308e-01 7.098782e-01 -6.685541
J -0.14342683 0.142462814 -0.5759097 5.679026e-01 7.098782e-01 -6.745563
I 0.08717360 -0.003622829 0.3500330 7.281504e-01 7.335508e-01 -6.849117
E 0.08536999 0.061664340 0.3427908 7.335508e-01 7.335508e-01 -6.851601
> topTable(fit2,coef=3,lfc=1)
logFC AveExpr t P.Value adj.P.Val B
A -1.715769 0.9181357 -6.889422 2.674199e-08 2.674199e-07 16.59063
> topTable(fit2,coef=3,p=0.2)
logFC AveExpr t P.Value adj.P.Val B
A -1.715769 0.9181357 -6.889422 2.674199e-08 2.674199e-07 16.59063
> topTable(fit2,coef=3,p=0.2,lfc=0.5)
logFC AveExpr t P.Value adj.P.Val B
A -1.715769 0.9181357 -6.889422 2.674199e-08 2.674199e-07 16.59063
> topTable(fit2,coef=3,p=0.2,lfc=0.5,sort="none")
logFC AveExpr t P.Value adj.P.Val B
A -1.715769 0.9181357 -6.889422 2.674199e-08 2.674199e-07 16.59063
>
> designlist <- list(Null=matrix(1,6,1),Two=design,Three=cbind(1,c(0,0,1,1,0,0),c(0,0,0,0,1,1)))
> out <- selectModel(M,designlist)
> table(out$pref)
Null Two Three
5 3 2
>
> ### marray object
>
> #suppressMessages(suppressWarnings(gotmarray <- require(marray,quietly=TRUE)))
> #if(gotmarray) {
> # data(swirl)
> # snorm = maNorm(swirl)
> # fit <- lmFit(snorm, design = c(1,-1,-1,1))
> # fit <- eBayes(fit)
> # topTable(fit,resort.by="AveExpr")
> #}
>
> ### duplicateCorrelation
>
> cor.out <- duplicateCorrelation(M)
> cor.out$consensus.correlation
[1] -0.09290714
> cor.out$atanh.correlations
[1] -0.4419130 0.4088967 -0.1964978 -0.6093769 0.3730118
>
> ### gls.series
>
> fit <- gls.series(M,design,correlation=cor.out$cor)
> fit$coefficients
First3Arrays Last3Arrays
[1,] 0.82809594 0.09777201
[2,] -0.08845425 0.27111909
[3,] -0.07175836 -0.11287397
[4,] 0.06955100 0.06852328
[5,] 0.08348330 0.05535668
> fit$stdev.unscaled
First3Arrays Last3Arrays
[1,] 0.3888215 0.3888215
[2,] 0.3888215 0.3888215
[3,] 0.3888215 0.3888215
[4,] 0.3888215 0.3888215
[5,] 0.3888215 0.3888215
> fit$sigma
[1] 0.7630059 0.2152728 0.3350370 0.3227781 0.3405473
> fit$df.residual
[1] 10 10 10 10 10
>
> ### mrlm
>
> fit <- mrlm(M,design)
Warning message:
In rlm.default(x = X, y = y, weights = w, ...) :
'rlm' failed to converge in 20 steps
> fit$coef
First3Arrays Last3Arrays
A 1.75138894 0.06025114
B -0.11982833 0.10322039
C -0.09302502 0.15095948
D -0.05454069 0.33700045
E 0.07927938 0.10434934
F -0.16249607 -0.34010852
G 0.30852468 -0.06873462
H -0.16942269 0.24392984
I -0.04720963 0.03996397
J 0.21417623 -0.05679272
> fit$stdev.unscaled
First3Arrays Last3Arrays
A 0.5933418 0.5773503
B 0.5773503 0.6096497
C 0.6017444 0.5773503
D 0.5773503 0.6266021
E 0.6307703 0.5773503
F 0.5773503 0.5846707
G 0.5773503 0.5773503
H 0.5773503 0.6544564
I 0.5773503 0.5773503
J 0.5773503 0.6689776
> fit$sigma
[1] 0.2894294 0.2679396 0.2090236 0.1461395 0.2309018 0.2827476 0.2285945
[8] 0.2267556 0.3537469 0.2172409
> fit$df.residual
[1] 4 4 4 4 4 4 4 4 4 4
>
> # Similar to Mette Langaas 19 May 2004
> set.seed(123)
> narrays <- 9
> ngenes <- 5
> mu <- 0
> alpha <- 2
> beta <- -2
> epsilon <- matrix(rnorm(narrays*ngenes,0,1),ncol=narrays)
> X <- cbind(rep(1,9),c(0,0,0,1,1,1,0,0,0),c(0,0,0,0,0,0,1,1,1))
> dimnames(X) <- list(1:9,c("mu","alpha","beta"))
> yvec <- mu*X[,1]+alpha*X[,2]+beta*X[,3]
> ymat <- matrix(rep(yvec,ngenes),ncol=narrays,byrow=T)+epsilon
> ymat[5,1:2] <- NA
> fit <- lmFit(ymat,design=X)
> test.contr <- cbind(c(0,1,-1),c(1,1,0),c(1,0,1))
> dimnames(test.contr) <- list(c("mu","alpha","beta"),c("alpha-beta","mu+alpha","mu+beta"))
> fit2 <- contrasts.fit(fit,contrasts=test.contr)
> eBayes(fit2)
An object of class "MArrayLM"
$coefficients
alpha-beta mu+alpha mu+beta
[1,] 3.537333 1.677465 -1.859868
[2,] 4.355578 2.372554 -1.983024
[3,] 3.197645 1.053584 -2.144061
[4,] 2.697734 1.611443 -1.086291
[5,] 3.502304 2.051995 -1.450309
$stdev.unscaled
alpha-beta mu+alpha mu+beta
[1,] 0.8164966 0.5773503 0.5773503
[2,] 0.8164966 0.5773503 0.5773503
[3,] 0.8164966 0.5773503 0.5773503
[4,] 0.8164966 0.5773503 0.5773503
[5,] 1.1547005 0.8368633 0.8368633
$sigma
[1] 1.3425032 0.4647155 1.1993444 0.9428569 0.9421509
$df.residual
[1] 6 6 6 6 4
$cov.coefficients
alpha-beta mu+alpha mu+beta
alpha-beta 0.6666667 3.333333e-01 -3.333333e-01
mu+alpha 0.3333333 3.333333e-01 5.551115e-17
mu+beta -0.3333333 5.551115e-17 3.333333e-01
$rank
[1] 3
$Amean
[1] 0.2034961 0.1954604 -0.2863347 0.1188659 0.1784593
$method
[1] "ls"
$design
mu alpha beta
1 1 0 0
2 1 0 0
3 1 0 0
4 1 1 0
5 1 1 0
6 1 1 0
7 1 0 1
8 1 0 1
9 1 0 1
$contrasts
alpha-beta mu+alpha mu+beta
mu 0 1 1
alpha 1 1 0
beta -1 0 1
$df.prior
[1] 9.306153
$s2.prior
[1] 0.923179
$var.prior
[1] 17.33142 17.33142 12.26855
$proportion
[1] 0.01
$s2.post
[1] 1.2677996 0.6459499 1.1251558 0.9097727 0.9124980
$t
alpha-beta mu+alpha mu+beta
[1,] 3.847656 2.580411 -2.860996
[2,] 6.637308 5.113018 -4.273553
[3,] 3.692066 1.720376 -3.500994
[4,] 3.464003 2.926234 -1.972606
[5,] 3.175181 2.566881 -1.814221
$df.total
[1] 15.30615 15.30615 15.30615 15.30615 13.30615
$p.value
alpha-beta mu+alpha mu+beta
[1,] 1.529450e-03 0.0206493481 0.0117123495
[2,] 7.144893e-06 0.0001195844 0.0006385076
[3,] 2.109270e-03 0.1055117477 0.0031325769
[4,] 3.381970e-03 0.0102514264 0.0668844448
[5,] 7.124839e-03 0.0230888584 0.0922478630
$lods
alpha-beta mu+alpha mu+beta
[1,] -1.013417 -3.702133 -3.0332393
[2,] 3.981496 1.283349 -0.2615911
[3,] -1.315036 -5.168621 -1.7864101
[4,] -1.757103 -3.043209 -4.6191869
[5,] -2.257358 -3.478267 -4.5683738
$F
[1] 7.421911 22.203107 7.608327 6.227010 5.060579
$F.p.value
[1] 5.581800e-03 2.988923e-05 5.080726e-03 1.050148e-02 2.320274e-02
>
> ### uniquegenelist
>
> uniquegenelist(letters[1:8],ndups=2)
[1] "a" "c" "e" "g"
> uniquegenelist(letters[1:8],ndups=2,spacing=2)
[1] "a" "b" "e" "f"
>
> ### classifyTests
>
> tstat <- matrix(c(0,5,0, 0,2.5,0, -2,-2,2, 1,1,1), 4, 3, byrow=TRUE)
> classifyTestsF(tstat)
TestResults matrix
[,1] [,2] [,3]
[1,] 0 1 0
[2,] 0 0 0
[3,] -1 -1 1
[4,] 0 0 0
> FStat(tstat)
[1] 8.333333 2.083333 4.000000 1.000000
attr(,"df1")
[1] 3
attr(,"df2")
[1] Inf
> classifyTestsT(tstat)
TestResults matrix
[,1] [,2] [,3]
[1,] 0 1 0
[2,] 0 0 0
[3,] 0 0 0
[4,] 0 0 0
> classifyTestsP(tstat)
TestResults matrix
[,1] [,2] [,3]
[1,] 0 1 0
[2,] 0 1 0
[3,] 0 0 0
[4,] 0 0 0
>
> ### avereps
>
> x <- matrix(rnorm(8*3),8,3)
> colnames(x) <- c("S1","S2","S3")
> rownames(x) <- c("b","a","a","c","c","b","b","b")
> avereps(x)
S1 S2 S3
b -0.2353018 0.5220094 0.2302895
a -0.4347701 0.6453498 -0.6758914
c 0.3482980 -0.4820695 -0.3841313
>
> ### roast
>
> y <- matrix(rnorm(100*4),100,4)
> sigma <- sqrt(2/rchisq(100,df=7))
> y <- y*sigma
> design <- cbind(Intercept=1,Group=c(0,0,1,1))
> iset1 <- 1:5
> y[iset1,3:4] <- y[iset1,3:4]+3
> iset2 <- 6:10
> roast(y=y,iset1,design,contrast=2)
Active.Prop P.Value
Down 0 0.996498249
Up 1 0.004002001
UpOrDown 1 0.008000000
Mixed 1 0.008000000
> roast(y=y,iset1,design,contrast=2,array.weights=c(0.5,1,0.5,1))
Active.Prop P.Value
Down 0 0.99899950
Up 1 0.00150075
UpOrDown 1 0.00300000
Mixed 1 0.00300000
> w <- matrix(runif(100*4),100,4)
> roast(y=y,iset1,design,contrast=2,weights=w)
Active.Prop P.Value
Down 0 0.9994997
Up 1 0.0010005
UpOrDown 1 0.0020000
Mixed 1 0.0020000
> mroast(y=y,list(set1=iset1,set2=iset2),design,contrast=2,gene.weights=runif(100))
NGenes PropDown PropUp Direction PValue FDR PValue.Mixed FDR.Mixed
set1 5 0 1 Up 0.008 0.015 0.008 0.015
set2 5 0 0 Up 0.959 0.959 0.687 0.687
> mroast(y=y,list(set1=iset1,set2=iset2),design,contrast=2,array.weights=c(0.5,1,0.5,1))
NGenes PropDown PropUp Direction PValue FDR PValue.Mixed FDR.Mixed
set1 5 0 1 Up 0.004 0.007 0.004 0.007
set2 5 0 0 Up 0.679 0.679 0.658 0.658
> mroast(y=y,list(set1=iset1,set2=iset2),design,contrast=2,weights=w)
NGenes PropDown PropUp Direction PValue FDR PValue.Mixed FDR.Mixed
set1 5 0.0 1 Up 0.003 0.005 0.003 0.005
set2 5 0.2 0 Down 0.950 0.950 0.250 0.250
> mroast(y=y,list(set1=iset1,set2=iset2),design,contrast=2,weights=w,array.weights=c(0.5,1,0.5,1))
NGenes PropDown PropUp Direction PValue FDR PValue.Mixed FDR.Mixed
set1 5 0 1 Up 0.001 0.001 0.001 0.001
set2 5 0 0 Down 0.791 0.791 0.146 0.146
> fry(y=y,list(set1=iset1,set2=iset2),design,contrast=2,weights=w,array.weights=c(0.5,1,0.5,1))
NGenes Direction PValue FDR PValue.Mixed FDR.Mixed
set1 5 Up 0.0007432594 0.001486519 1.820548e-05 3.641096e-05
set2 5 Down 0.8208140511 0.820814051 2.211837e-01 2.211837e-01
> rownames(y) <- paste0("Gene",1:100)
> iset1A <- rownames(y)[1:5]
> fry(y=y,index=iset1A,design,contrast=2,weights=w,array.weights=c(0.5,1,0.5,1))
NGenes Direction PValue PValue.Mixed
set1 5 Up 0.0007432594 1.820548e-05
>
> ### camera
>
> camera(y=y,iset1,design,contrast=2,weights=c(0.5,1,0.5,1),allow.neg.cor=TRUE,inter.gene.cor=NA)
NGenes Correlation Direction PValue
set1 5 -0.2481655 Up 0.001050253
> camera(y=y,list(set1=iset1,set2=iset2),design,contrast=2,allow.neg.cor=TRUE,inter.gene.cor=NA)
NGenes Correlation Direction PValue FDR
set1 5 -0.2481655 Up 0.0009047749 0.00180955
set2 5 0.1719094 Down 0.9068364378 0.90683644
> camera(y=y,iset1,design,contrast=2,weights=c(0.5,1,0.5,1))
NGenes Direction PValue
set1 5 Up 1.105329e-10
> camera(y=y,list(set1=iset1,set2=iset2),design,contrast=2)
NGenes Direction PValue FDR
set1 5 Up 7.334400e-12 1.466880e-11
set2 5 Down 8.677115e-01 8.677115e-01
> camera(y=y,iset1A,design,contrast=2)
NGenes Direction PValue
set1 5 Up 7.3344e-12
>
> ### with EList arg
>
> y <- new("EList",list(E=y))
> roast(y=y,iset1,design,contrast=2)
Active.Prop P.Value
Down 0 0.997498749
Up 1 0.003001501
UpOrDown 1 0.006000000
Mixed 1 0.006000000
> camera(y=y,iset1,design,contrast=2,allow.neg.cor=TRUE,inter.gene.cor=NA)
NGenes Correlation Direction PValue
set1 5 -0.2481655 Up 0.0009047749
> camera(y=y,iset1,design,contrast=2)
NGenes Direction PValue
set1 5 Up 7.3344e-12
>
> ### eBayes with trend
>
> fit <- lmFit(y,design)
> fit <- eBayes(fit,trend=TRUE)
> topTable(fit,coef=2)
logFC AveExpr t P.Value adj.P.Val B
Gene2 3.729512 1.73488969 4.865697 0.0004854886 0.02902331 0.1596831
Gene3 3.488703 1.03931081 4.754954 0.0005804663 0.02902331 -0.0144071
Gene4 2.696676 1.74060725 3.356468 0.0063282637 0.21094212 -2.3434702
Gene1 2.391846 1.72305203 3.107124 0.0098781268 0.24695317 -2.7738874
Gene33 -1.492317 -0.07525287 -2.783817 0.0176475742 0.29965463 -3.3300835
Gene5 2.387967 1.63066783 2.773444 0.0179792778 0.29965463 -3.3478204
Gene80 -1.839760 -0.32802306 -2.503584 0.0291489863 0.37972679 -3.8049642
Gene39 1.366141 -0.27360750 2.451133 0.0320042242 0.37972679 -3.8925860
Gene95 -1.907074 1.26297763 -2.414217 0.0341754107 0.37972679 -3.9539571
Gene50 1.034777 0.01608433 2.054690 0.0642289403 0.59978803 -4.5350317
> fit$df.prior
[1] 9.098442
> fit$s2.prior
Gene1 Gene2 Gene3 Gene4 Gene5 Gene6 Gene7 Gene8
0.6901845 0.6977354 0.3860494 0.7014122 0.6341068 0.2926337 0.3077620 0.3058098
Gene9 Gene10 Gene11 Gene12 Gene13 Gene14 Gene15 Gene16
0.2985145 0.2832520 0.3232434 0.3279710 0.2816081 0.2943502 0.3127994 0.2894802
Gene17 Gene18 Gene19 Gene20 Gene21 Gene22 Gene23 Gene24
0.2812758 0.2840051 0.2839124 0.2954261 0.2838592 0.2812704 0.3157029 0.2844541
Gene25 Gene26 Gene27 Gene28 Gene29 Gene30 Gene31 Gene32
0.4778832 0.2818242 0.2930360 0.2940957 0.2941862 0.3234399 0.3164779 0.2853510
Gene33 Gene34 Gene35 Gene36 Gene37 Gene38 Gene39 Gene40
0.2988244 0.3450090 0.3048596 0.3089086 0.3104534 0.4551549 0.3220008 0.2813286
Gene41 Gene42 Gene43 Gene44 Gene45 Gene46 Gene47 Gene48
0.2826027 0.2822504 0.2823330 0.3170673 0.3146173 0.3146793 0.2916540 0.2975003
Gene49 Gene50 Gene51 Gene52 Gene53 Gene54 Gene55 Gene56
0.3538946 0.2907240 0.3199596 0.2816641 0.2814293 0.2996822 0.2812885 0.2896157
Gene57 Gene58 Gene59 Gene60 Gene61 Gene62 Gene63 Gene64
0.2955317 0.2815907 0.2919420 0.2849675 0.3540805 0.3491713 0.2975019 0.2939325
Gene65 Gene66 Gene67 Gene68 Gene69 Gene70 Gene71 Gene72
0.2986943 0.3265466 0.3402343 0.3394927 0.2813283 0.2814440 0.3089669 0.3030850
Gene73 Gene74 Gene75 Gene76 Gene77 Gene78 Gene79 Gene80
0.2859286 0.2813216 0.3475231 0.3334419 0.2949550 0.3108702 0.2959688 0.3295294
Gene81 Gene82 Gene83 Gene84 Gene85 Gene86 Gene87 Gene88
0.3413700 0.2946268 0.3029565 0.2920284 0.2926205 0.2818046 0.3425116 0.2882936
Gene89 Gene90 Gene91 Gene92 Gene93 Gene94 Gene95 Gene96
0.2945459 0.3077919 0.2892134 0.2823787 0.3048049 0.2961408 0.4590012 0.2812784
Gene97 Gene98 Gene99 Gene100
0.2846345 0.2819651 0.3137551 0.2856081
> summary(fit$s2.post)
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.2335 0.2603 0.2997 0.3375 0.3655 0.7812
>
> y$E[1,1] <- NA
> y$E[1,3] <- NA
> fit <- lmFit(y,design)
> fit <- eBayes(fit,trend=TRUE)
> topTable(fit,coef=2)
logFC AveExpr t P.Value adj.P.Val B
Gene3 3.488703 1.03931081 4.604490 0.0007644061 0.07644061 -0.2333915
Gene2 3.729512 1.73488969 4.158038 0.0016033158 0.08016579 -0.9438583
Gene4 2.696676 1.74060725 2.898102 0.0145292666 0.44537707 -3.0530813
Gene33 -1.492317 -0.07525287 -2.784004 0.0178150826 0.44537707 -3.2456324
Gene5 2.387967 1.63066783 2.495395 0.0297982959 0.46902627 -3.7272957
Gene80 -1.839760 -0.32802306 -2.491115 0.0300256116 0.46902627 -3.7343584
Gene39 1.366141 -0.27360750 2.440729 0.0328318388 0.46902627 -3.8172597
Gene1 2.638272 1.47993643 2.227507 0.0530016060 0.58890673 -3.9537576
Gene95 -1.907074 1.26297763 -2.288870 0.0429197808 0.53649726 -4.0642439
Gene50 1.034777 0.01608433 2.063663 0.0635275235 0.60439978 -4.4204731
> fit$df.residual[1]
[1] 0
> fit$df.prior
[1] 8.971891
> fit$s2.prior
[1] 0.7014084 0.9646561 0.4276287 0.9716476 0.8458852 0.2910492 0.3097052
[8] 0.3074225 0.2985517 0.2786374 0.3267121 0.3316013 0.2766404 0.2932679
[15] 0.3154347 0.2869186 0.2761395 0.2799884 0.2795119 0.2946468 0.2794412
[22] 0.2761282 0.3186442 0.2806092 0.4596465 0.2767847 0.2924541 0.2939204
[29] 0.2930568 0.3269177 0.3194905 0.2814293 0.2989389 0.3483845 0.3062977
[36] 0.3110287 0.3127934 0.4418052 0.3254067 0.2761732 0.2780422 0.2773311
[43] 0.2776653 0.3201314 0.3174515 0.3175199 0.2897731 0.2972785 0.3567262
[50] 0.2885556 0.3232426 0.2767207 0.2762915 0.3000062 0.2761306 0.2870975
[57] 0.2947817 0.2766152 0.2901489 0.2813183 0.3568982 0.3724440 0.2972804
[64] 0.2927300 0.2987764 0.3301406 0.3437962 0.3430762 0.2761729 0.2763094
[71] 0.3110958 0.3041715 0.2822004 0.2761654 0.3507694 0.3371214 0.2940441
[78] 0.3132660 0.2953388 0.3331880 0.3448949 0.2946558 0.3040162 0.2902616
[85] 0.2910320 0.2769211 0.3459946 0.2859057 0.2935193 0.3097398 0.2865663
[92] 0.2774968 0.3062327 0.2955576 0.5425422 0.2761214 0.2808585 0.2771484
[99] 0.3164981 0.2817725
> summary(fit$s2.post)
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.2296 0.2581 0.3003 0.3453 0.3652 0.9158
>
> ### voom
>
> y <- matrix(rpois(100*4,lambda=20),100,4)
> design <- cbind(Int=1,x=c(0,0,1,1))
> v <- voom(y,design)
> names(v)
[1] "E" "weights" "design" "targets"
> summary(v$E)
V1 V2 V3 V4
Min. :12.25 Min. :12.58 Min. :12.19 Min. :12.24
1st Qu.:13.13 1st Qu.:13.07 1st Qu.:13.15 1st Qu.:13.03
Median :13.29 Median :13.30 Median :13.30 Median :13.27
Mean :13.28 Mean :13.29 Mean :13.29 Mean :13.28
3rd Qu.:13.49 3rd Qu.:13.51 3rd Qu.:13.50 3rd Qu.:13.50
Max. :14.23 Max. :14.28 Max. :13.97 Max. :13.96
> summary(v$weights)
V1 V2 V3 V4
Min. : 5.935 Min. : 5.935 Min. : 5.935 Min. : 5.935
1st Qu.: 6.788 1st Qu.: 7.049 1st Qu.: 7.207 1st Qu.: 6.825
Median :11.066 Median :10.443 Median :10.606 Median :10.414
Mean :10.421 Mean :10.485 Mean :10.571 Mean :10.532
3rd Qu.:13.485 3rd Qu.:14.155 3rd Qu.:13.859 3rd Qu.:14.121
Max. :15.083 Max. :15.101 Max. :15.095 Max. :15.063
>
> ### goana
>
> EB <- c("133746","1339","134","1340","134083","134111","134147","134187","134218","134266",
+ "134353","134359","134391","134429","134430","1345","134510","134526","134549","1346",
+ "134637","1347","134701","134728","1348","134829","134860","134864","1349","134957",
+ "135","1350","1351","135112","135114","135138","135152","135154","1352","135228",
+ "135250","135293","135295","1353","135458","1355","1356","135644","135656","1357",
+ "1358","135892","1359","135924","135935","135941","135946","135948","136","1360",
+ "136051","1361","1362","136227","136242","136259","1363","136306","136319","136332",
+ "136371","1364","1365","136541","1366","136647","1368","136853","1369","136991",
+ "1370","137075","1371","137209","1373","137362","1374","137492","1375","1376",
+ "137682","137695","137735","1378","137814","137868","137872","137886","137902","137964")
> go <- goana(fit,FDR=0.8,geneid=EB)
> topGO(go,n=10,truncate.term=30)
Term Ont N Up Down P.Up P.Down
GO:0055082 cellular chemical homeostas... BP 2 0 2 1.000000000 0.009090909
GO:0006915 apoptotic process BP 5 4 1 0.009503355 0.416247633
GO:0098609 cell-cell adhesion BP 5 4 0 0.009503355 1.000000000
GO:0040011 locomotion BP 5 4 0 0.009503355 1.000000000
GO:0012501 programmed cell death BP 5 4 1 0.009503355 0.416247633
GO:0042981 regulation of apoptotic pro... BP 5 4 1 0.009503355 0.416247633
GO:0043067 regulation of programmed ce... BP 5 4 1 0.009503355 0.416247633
GO:0097190 apoptotic signaling pathway BP 3 3 0 0.010952381 1.000000000
GO:0031252 cell leading edge CC 3 3 0 0.010952381 1.000000000
GO:0006897 endocytosis BP 3 3 0 0.010952381 1.000000000
> topGO(go,n=10,truncate.term=30,sort="down")
Term Ont N Up Down P.Up P.Down
GO:0055082 cellular chemical homeostas... BP 2 0 2 1.0000000 0.009090909
GO:0032502 developmental process BP 25 4 6 0.8946593 0.014492712
GO:0009887 animal organ morphogenesis BP 3 0 2 1.0000000 0.025788497
GO:0019725 cellular homeostasis BP 3 0 2 1.0000000 0.025788497
GO:0072359 circulatory system developm... BP 3 0 2 1.0000000 0.025788497
GO:0007507 heart development BP 3 0 2 1.0000000 0.025788497
GO:0048232 male gamete generation BP 3 0 2 1.0000000 0.025788497
GO:0007283 spermatogenesis BP 3 0 2 1.0000000 0.025788497
GO:0070062 extracellular exosome CC 14 3 4 0.6749330 0.031604687
GO:0043230 extracellular organelle CC 14 3 4 0.6749330 0.031604687
>
> proc.time()
user system elapsed
2.10 0.12 2.25
limma.Rcheck/limma-Ex.timings
| name | user | system | elapsed | |
| LargeDataObject | 0.001 | 0.000 | 0.001 | |
| PrintLayout | 0.003 | 0.000 | 0.003 | |
| TestResults | 0.001 | 0.000 | 0.001 | |
| alias2Symbol | 3.632 | 0.134 | 3.790 | |
| arrayWeights | 0.003 | 0.000 | 0.003 | |
| arrayWeightsQuick | 0.001 | 0.000 | 0.002 | |
| asMatrixWeights | 0.001 | 0.001 | 0.002 | |
| auROC | 0.002 | 0.000 | 0.003 | |
| avearrays | 0.002 | 0.001 | 0.002 | |
| avereps | 0.003 | 0.000 | 0.003 | |
| backgroundcorrect | 0.006 | 0.001 | 0.007 | |
| barcodeplot | 0.050 | 0.006 | 0.056 | |
| beadCountWeights | 0.002 | 0.000 | 0.001 | |
| blockDiag | 0.001 | 0.001 | 0.001 | |
| camera | 0.044 | 0.007 | 0.053 | |
| cbind | 0.006 | 0.001 | 0.007 | |
| changelog | 0.001 | 0.001 | 0.002 | |
| channel2M | 0.002 | 0.000 | 0.002 | |
| classifytests | 0.002 | 0.001 | 0.003 | |
| contrastAsCoef | 0.007 | 0.001 | 0.009 | |
| contrasts.fit | 0.013 | 0.002 | 0.015 | |
| controlStatus | 0.010 | 0.001 | 0.012 | |
| coolmap | 0.133 | 0.020 | 0.153 | |
| cumOverlap | 0.002 | 0.000 | 0.002 | |
| detectionPValue | 0.001 | 0.000 | 0.001 | |
| diffSplice | 0.002 | 0.000 | 0.002 | |
| dim | 0.003 | 0.001 | 0.004 | |
| dupcor | 0.431 | 0.034 | 0.471 | |
| ebayes | 0.011 | 0.001 | 0.013 | |
| fitGammaIntercept | 0.002 | 0.000 | 0.002 | |
| fitfdist | 0.002 | 0.000 | 0.002 | |
| fitmixture | 0.016 | 0.001 | 0.018 | |
| genas | 0.091 | 0.006 | 0.098 | |
| geneSetTest | 0.002 | 0.001 | 0.002 | |
| getSpacing | 0.002 | 0.000 | 0.002 | |
| getlayout | 0.002 | 0.000 | 0.002 | |
| goana | 0.004 | 0.000 | 0.005 | |
| heatdiagram | 0.002 | 0.001 | 0.002 | |
| helpMethods | 0.002 | 0.000 | 0.001 | |
| ids2indices | 0.002 | 0.001 | 0.002 | |
| imageplot | 0.028 | 0.002 | 0.030 | |
| intraspotCorrelation | 0.002 | 0.000 | 0.002 | |
| isfullrank | 0.001 | 0.000 | 0.002 | |
| isnumeric | 0.002 | 0.001 | 0.003 | |
| kooperberg | 0.001 | 0.000 | 0.001 | |
| limmaUsersGuide | 0.001 | 0.000 | 0.002 | |
| lm.series | 0.000 | 0.000 | 0.001 | |
| lmFit | 0.396 | 0.004 | 0.406 | |
| lmscFit | 0.003 | 0.000 | 0.003 | |
| loessfit | 0.009 | 0.002 | 0.010 | |
| logcosh | 0.001 | 0.000 | 0.002 | |
| logsumexp | 0.001 | 0.000 | 0.002 | |
| ma3x3 | 0.003 | 0.000 | 0.002 | |
| makeContrasts | 0.002 | 0.000 | 0.002 | |
| makeunique | 0.003 | 0.000 | 0.002 | |
| mdplot | 0.004 | 0.000 | 0.004 | |
| merge | 0.006 | 0.000 | 0.006 | |
| mergeScansRG | 0.001 | 0.000 | 0.002 | |
| modelMatrix | 0.005 | 0.000 | 0.005 | |
| modifyWeights | 0.002 | 0.000 | 0.002 | |
| nec | 0.002 | 0.000 | 0.003 | |
| normalizeMedianAbsValues | 0.002 | 0.000 | 0.003 | |
| normalizeRobustSpline | 0.023 | 0.002 | 0.026 | |
| normalizeVSN | 0.601 | 0.034 | 1.221 | |
| normalizebetweenarrays | 0.003 | 0.000 | 0.004 | |
| normalizeprintorder | 0.001 | 0.000 | 0.001 | |
| normexpfit | 0.002 | 0.000 | 0.003 | |
| normexpfitcontrol | 0.001 | 0.001 | 0.001 | |
| normexpfitdetectionp | 0.002 | 0.000 | 0.001 | |
| normexpsignal | 0.001 | 0.000 | 0.001 | |
| plotDensities | 0.001 | 0.000 | 0.002 | |
| plotExons | 0.001 | 0.000 | 0.002 | |
| plotMD | 0.027 | 0.003 | 0.030 | |
| plotMDS | 0.012 | 0.001 | 0.014 | |
| plotRLDF | 0.011 | 0.001 | 0.013 | |
| plotSplice | 0.001 | 0.000 | 0.001 | |
| plotWithHighlights | 0.008 | 0.001 | 0.009 | |
| plotma | 0.031 | 0.003 | 0.034 | |
| poolvar | 0.003 | 0.000 | 0.003 | |
| predFCm | 0.019 | 0.002 | 0.020 | |
| printorder | 0.010 | 0.005 | 0.015 | |
| printtipWeights | 0.003 | 0.000 | 0.003 | |
| propTrueNull | 0.005 | 0.001 | 0.005 | |
| propexpr | 0.001 | 0.000 | 0.001 | |
| protectMetachar | 0.001 | 0.000 | 0.001 | |
| qqt | 0.003 | 0.000 | 0.003 | |
| qualwt | 0.001 | 0.000 | 0.001 | |
| rankSumTestwithCorrelation | 0.011 | 0.000 | 0.011 | |
| read.idat | 0.002 | 0.001 | 0.001 | |
| read.ilmn | 0.002 | 0.000 | 0.002 | |
| read.maimages | 0.002 | 0.000 | 0.002 | |
| readImaGeneHeader | 0.001 | 0.000 | 0.001 | |
| readgal | 0.001 | 0.000 | 0.001 | |
| removeBatchEffect | 0.018 | 0.000 | 0.018 | |
| removeext | 0.002 | 0.000 | 0.002 | |
| roast | 0.018 | 0.001 | 0.018 | |
| romer | 0.030 | 0.002 | 0.033 | |
| selectmodel | 0.040 | 0.000 | 0.041 | |
| squeezeVar | 0.001 | 0.001 | 0.001 | |
| strsplit2 | 0.002 | 0.000 | 0.002 | |
| subsetting | 0.005 | 0.001 | 0.005 | |
| targetsA2C | 0.006 | 0.000 | 0.005 | |
| topGO | 0.001 | 0.000 | 0.001 | |
| topRomer | 0.002 | 0.000 | 0.001 | |
| topSplice | 0.001 | 0.000 | 0.002 | |
| toptable | 0.001 | 0.000 | 0.001 | |
| tricubeMovingAverage | 0.003 | 0.000 | 0.004 | |
| trigammainverse | 0.001 | 0.001 | 0.002 | |
| trimWhiteSpace | 0.002 | 0.000 | 0.002 | |
| uniquegenelist | 0.002 | 0.000 | 0.002 | |
| unwrapdups | 0.002 | 0.000 | 0.003 | |
| venn | 0.025 | 0.002 | 0.026 | |
| volcanoplot | 0.001 | 0.000 | 0.002 | |
| voom | 0.002 | 0.000 | 0.002 | |
| weightedLowess | 0.010 | 0.000 | 0.012 | |
| weightedmedian | 0.001 | 0.000 | 0.001 | |
| zscore | 0.002 | 0.000 | 0.002 | |