## ----code, echo=TRUE, eval=FALSE----------------------------------------------
# if (!require("BiocManager", quietly = TRUE))
# install.packages("BiocManager")
#
# BiocManager::install("MOSim")
## ----code1, echo=TRUE, eval=FALSE---------------------------------------------
# library(devtools)
#
# devtools::install_github("ConesaLab/MOSim")
## ----load, echo=FALSE, eval=TRUE, include=TRUE--------------------------------
library(MOSim)
## ----code2, echo=TRUE, eval=TRUE----------------------------------------------
library(MOSim)
omic_list <- c("RNA-seq")
rnaseq_simulation <- mosim(omics = omic_list)
## ----code3, echo=TRUE, eval=TRUE, include=TRUE--------------------------------
rnaseq_simulation <- mosim(omics = c("RNA-seq"),
times = 0,
numberGroups = 2,
numberReps = 4)
## ----code4, echo=TRUE, eval=FALSE, include=TRUE, error=TRUE-------------------
# rnaseq_simulation <- mosim(omics = c("RNA-seq"),
# times = 0,
# numberGroups = 1,
# numberReps = 4)
## ----code5, echo=TRUE, eval=FALSE, include=TRUE-------------------------------
# multi_simulation <- mosim(omics = c("RNA-seq", "DNase-seq"),
# times = c(0, 5, 10),
# numberGroups = 2,
# numberReps = 4,
# diffGenes = .3)
#
# dnase_simulation <- mosim(omics = c("DNase-seq"),
# times = c(0, 5, 10),
# numberGroups = 2,
# numberReps = 4,
# diffGenes = .3)
## ----code6, echo=TRUE, eval=TRUE,cache=TRUE, tidy=FALSE, results='hide', message=FALSE----
# Take a subset of the included dataset for illustration
# purposes. We could also load it from a csv file or RData,
# as long as we transform it to have 1 column named "Counts"
# and the identifiers as row names.
data("sampleData")
custom_rnaseq <- head(sampleData$SimRNAseq$data, 100)
# In this case, 'custom_rnaseq' is a data frame with
# the structure:
head(custom_rnaseq)
## Counts
## ENSMUSG00000000001 6572
## ENSMUSG00000000003 0
## ENSMUSG00000000028 4644
## ENSMUSG00000000031 8
## ENSMUSG00000000037 0
## ENSMUSG00000000049 0
# The helper 'omicData' returns an object with our custom data.
rnaseq_customdata <- omicData("RNA-seq", data = custom_rnaseq)
# We use the associative list of 'omics' parameter to pass
# the RNA-seq object.
rnaseq_simulation <- mosim(omics = list("RNA-seq" = rnaseq_customdata))
## ----code7, echo=TRUE, eval=TRUE, tidy=FALSE, include=TRUE, results='hide', message=FALSE----
# Select a subset of the available data as a custom dataset
data("sampleData")
custom_dnaseseq <- head(sampleData$SimDNaseseq$data, 100)
# Retrieve a subset of the default association list.
dnase_genes <- sampleData$SimDNaseseq$idToGene
dnase_genes <- dnase_genes[dnase_genes$ID %in%
rownames(custom_dnaseseq), ]
# In this case, 'custom_dnaseseq' is a data frame with
# the structure:
head(custom_dnaseseq)
## Counts
## 1_63176480_63177113 513
## 1_125435495_125436168 1058
## 1_128319376_128319506 37
## 1_139067124_139067654 235
## 1_152305595_152305752 105
## 1_172490322_172490824 290
# The association list 'dnase_genes' is another data frame
# with the structure:
head(dnase_genes)
## ID Gene
## 29195 1_3670777_3670902 ENSMUSG00000051951
## 29196 1_3873195_3873351 ENSMUSG00000089420
## 29197 1_4332428_4332928 ENSMUSG00000025900
## 29198 1_4346315_4346445 ENSMUSG00000025900
## 29199 1_4416827_4416973 ENSMUSG00000025900
## 29200 1_4516660_4516798 ENSMUSG00000096126
dnaseseq_customdata <- omicData("DNase-seq",
data = custom_dnaseseq,
associationList = dnase_genes)
multi_simulation <- mosim(omics = list(
"RNA-seq" = rnaseq_customdata,
"DNase-seq" = dnaseseq_customdata)
)
## ----code8, echo=TRUE, eval=TRUE, tidy=FALSE, include = TRUE, results='hide', message=FALSE----
# Select a subset of the available data as a custom dataset
data("sampleData")
custom_tf <- head(sampleData$SimRNAseq$TFtoGene, 100)
# TF TFgene LinkedGene
# 1 Aebp2 ENSMUSG00000030232 ENSMUSG00000000711
# 2 Aebp2 ENSMUSG00000030232 ENSMUSG00000001157
# 3 Aebp2 ENSMUSG00000030232 ENSMUSG00000001211
# 4 Aebp2 ENSMUSG00000030232 ENSMUSG00000001227
# 5 Aebp2 ENSMUSG00000030232 ENSMUSG00000001305
# 6 Aebp2 ENSMUSG00000030232 ENSMUSG00000001794
multi_simulation <- mosim(omics = list(
"RNA-seq" = rnaseq_customdata,
"DNase-seq" = dnaseseq_customdata),
# The option is passed directly to mosim function instead of
# being an element inside "omics" parameter.
TFtoGene = custom_tf
)
## ----code9, echo=TRUE, eval=TRUE, tidy=FALSE, results='hide', message=FALSE----
# Select a subset of the available data as a custom dataset
data("sampleData")
custom_cpgs <- head(sampleData$SimMethylseq$idToGene, 100)
# The ID column will be splitted using the "_" char
# assuming <chr>_<start>_<end>.
#
# These positions will be considered as CpG sites
# and used to generate CpG islands and other elements.
#
# Please refer to MOSim paper for more information.
#
# ID Gene
# 1 11_3101154_3101154 ENSMUSG00000082286
# 2 11_3101170_3101170 ENSMUSG00000082286
# 3 11_3101229_3101229 ENSMUSG00000082286
# 4 11_3101287_3101287 ENSMUSG00000082286
# 5 11_3101329_3101329 ENSMUSG00000082286
# 6 11_3101404_3101404 ENSMUSG00000082286
## ----code10, echo=TRUE, eval=FALSE, tidy = FALSE, results='hide', message=FALSE----
# omic_list <- c("RNA-seq")
#
# rnaseq_options <- omicSim("RNA-seq", totalFeatures = 2500)
#
# # The return value is an associative list compatible with
# # 'omicsOptions'
# rnaseq_simulation <- mosim(omics = omic_list,
# omicsOptions = rnaseq_options)
## ----code11, echo=TRUE, eval=TRUE, tidy = FALSE, results='hide', message=FALSE----
omics_list <- c("RNA-seq", "DNase-seq")
# In R concatenaning two lists creates another one merging
# its elements, we use that for 'omicsOptions' parameter.
omics_options <- c(omicSim("RNA-seq", totalFeatures = 2500, depth = 74),
omicSim("DNase-seq",
# Limit the number of features to simulate
totalFeatures = 1500,
# Modify the percentage of regulators with effects.
regulatorEffect = list(
'activator' = 0.68,
'repressor' = 0.3,
'NE' = 0.02
)))
set.seed(12345)
multi_simulation <- mosim(omics = omics_list,
omicsOptions = omics_options)
## ----code12, echo=TRUE, eval=TRUE, tidy = FALSE, results='hide', message=FALSE----
rnaseq_customdata <- omicData("RNA-seq", data = custom_rnaseq)
rnaseq_options <- omicSim("RNA-seq", totalFeatures = 100)
rnaseq_simulation <- mosim(omics = list("RNA-seq" = rnaseq_customdata),
omicsOptions = rnaseq_options)
## ----code13, echo=TRUE, eval=TRUE, tidy = FALSE, results='hide', message=FALSE----
# This will be a data frame with RNA-seq settings (DE flag, profiles)
rnaseq_settings <- omicSettings(multi_simulation, "RNA-seq")
# This will be a list containing all the simulated omics (RNA-seq
# and DNase-seq in this case)
all_settings <- omicSettings(multi_simulation)
## ----code14, echo=TRUE, eval=TRUE, tidy=FALSE, results='hide', message=FALSE----
# This will be a list with 3 keys: settings, association and regulators
dnase_settings <- omicSettings(multi_simulation, "DNase-seq",
association = TRUE)
## ----code15, echo=TRUE, eval=TRUE, tidy = FALSE, results='hide', message=FALSE----
# multi_simulation is an object returned by mosim function.
# This will be a data frame with RNA-seq counts
rnaseq_simulated <- omicResults(multi_simulation, "RNA-seq")
# Group1.Time0.Rep1 Group1.Time0.Rep2 Group1.Time0.Rep3 ...
# ENSMUSG00000073155 4539 5374 5808 ...
# ENSMUSG00000026251 0 0 0 ...
# ENSMUSG00000040472 2742 2714 2912 ...
# ENSMUSG00000021598 5256 4640 5130 ...
# ENSMUSG00000032348 421 348 492 ...
# ENSMUSG00000097226 16 14 9 ...
# ENSMUSG00000027857 0 0 0 ...
# ENSMUSG00000032081 1 0 0 ...
# ENSMUSG00000097164 794 822 965 ...
# ENSMUSG00000097871 0 0 0 ...
# This will be a list containing RNA-seq and DNase-seq counts
all_simulated <- omicResults(multi_simulation)
## ----code16, echo=TRUE, eval=TRUE, tidy = FALSE, results = "hide", message=FALSE, warning=FALSE----
# Generate a simulation object
omic_list <- c("RNA-seq")
rnaseq_simulation <- mosim(omics = omic_list)
# This will be a data frame with RNA-seq counts
design_matrix <- experimentalDesign(rnaseq_simulation)
design_matrix
## ----code17, echo=TRUE, eval=FALSE, tidy = FALSE, results = "hide", message=FALSE, warning=FALSE----
#
# # The methods returns a ggplot plot, if called directly
# # it will be automatically plotted.
# plotProfile(multi_simulation,
# omics = c("RNA-seq", "DNase-seq"),
# featureIDS = list(
# "RNA-seq" = "ENSMUSG00000024691",
# "DNase-seq" = "19_12574278_12574408"
# ))
## ----code18, echo=TRUE, eval=FALSE, tidy = FALSE, results = "hide", message=FALSE, warning=FALSE----
# library(ggplot2)
#
# # Store the plot in a variable
# profile_plot <- plotProfile(multi_simulation,
# omics = c("RNA-seq", "DNase-seq"),
# featureIDS = list(
# "RNA-seq" = "ENSMUSG00000024691",
# "DNase-seq" = "19_12574278_12574408"
# ))
#
# # Modify the title and print
# profile_plot +
# ggtitle("My custom title") +
# theme_bw() +
# theme(legend.position="top")
## ----code19, echo=TRUE, eval=TRUE, tidy = FALSE, results = "hide", message=FALSE, warning=FALSE----
rnaseq_simulation <- mosim(omics = c("RNA-seq", "ChIP-seq"))
rnaseq_simulated <- omicResults(rnaseq_simulation, c("RNA-seq", "ChIP-seq"))
discrete_ChIP <- discretize(rnaseq_simulated, "ChIP-seq")