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### /home/biocbuild/bbs-3.14-bioc/R/bin/R CMD build --keep-empty-dirs --no-resave-data tidybulk
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* checking for file ‘tidybulk/DESCRIPTION’ ... OK
* preparing ‘tidybulk’:
* checking DESCRIPTION meta-information ... OK
* installing the package to process help pages
* building the PDF package manual
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Converting Rd files to LaTeX ......
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[]\T1/ptm/m/n/10 Underlying cus-tom method: data fil-ter(n_aggr > 1) group_by(!
!.sample,!!.transcript) dplyr::mutate(!!.abundance
[7]
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[]\T1/ptm/m/n/10 <[`tidy-eval`][dplyr_tidy_eval]> Vari-ables, or func-tions or
vari-ables. Use [desc()]
[8]
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[]\T1/zi4/m/n/9 tidybulk::se_mini |> tidybulk() |> select(feature, count) |> h
ead() |> as_matrix(rownames=feature)[]
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[]\T1/zi4/m/n/9 tidybulk::se_mini |> tidybulk() |>filter(sample=="SRR1740034")
|> deconvolve_cellularity(sample, feature, count, cores = 1)[]
[19] [20] [21] [22] [23] [24] [25] [26]
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[]\T1/ptm/m/n/10 When `.drop = TRUE`, empty groups are dropped. See [group_by_d
rop_default()]
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[]\T1/ptm/m/n/10 Underlying method: edgeR::filterByExpr( data, min.count = min-
i-mum_counts, group = string_factor_of_interest,
[30] [31] [32]
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[]\T1/zi4/m/n/9 annotation = tidybulk::counts_SE %>% tidybulk() %>% as_tibble(
) %>% distinct(sample) %>% mutate(source = "AU")[]
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[]\T1/zi4/m/n/9 annotation = tidybulk::counts_SE %>% tidybulk() %>% as_tibble(
) %>% distinct(sample) %>% mutate(source = "AU")[]
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[]\T1/zi4/m/n/9 annotation = tidybulk::counts_SE %>% tidybulk() %>% as_tibble(
) %>% distinct(sample) %>% mutate(source = "AU")[]
[33] [34]
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[]\T1/ptm/m/n/10 Underlying method: edgeR::filterByExpr( data, min.count = min-
i-mum_counts, group = string_factor_of_interest,
[35] [36] [37] [38]
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[]\T1/zi4/m/n/9 annotation = tidybulk::counts_SE %>% tidybulk() %>% as_tibble(
) %>% distinct(sample) %>% mutate(source = "AU")[]
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[]\T1/zi4/m/n/9 counts.MDS.rotated = rotate_dimensions(counts.MDS, \TS1/cmtt/
m/n/9 `\T1/zi4/m/n/9 Dim1\TS1/cmtt/m/n/9 `\T1/zi4/m/n/9 , \TS1/cmtt/m/n/9 `\T1/
zi4/m/n/9 Dim2\TS1/cmtt/m/n/9 `\T1/zi4/m/n/9 , rotation_degrees = 45, .element
= sample)[]
[55] [56] [57] [58]
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[]\T1/ptm/m/n/10 Underlying method edgeR::calcNormFactors(.data, method = c("TM
M","TMMwsp","RLE","upperquartile"))
[59] [60] [61]
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[]\T1/zi4/m/n/9 tidybulk::se_mini |> tidybulk() |> as_tibble() |> symbol_to_en
trez(.transcript = feature, .sample = sample)[]
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[]\T1/ptm/m/n/10 A string char-ac-ter. Ei-ther "edgeR_quasi_likelihood" (i.e.,
QLF), "edgeR_likelihood_ratio"
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\T1/ptm/m/n/10 (i.e., LRT), "edger_robust_likelihood_ratio", "DE-Seq2", "limma_
voom", "limma_voom_sample_weights"
[65]
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\T1/ptm/m/n/10 and limma-voom (i.e., edgeR::calcNormFactors; "TMM","TMMwsp","RL
E","upperquartile").
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\T1/zi4/m/n/10 btp616$[][]\T1/ptm/m/n/10 , limma-voom [][]$\T1/zi4/m/n/10 https
: / / doi . org / 10 . 1186 / gb-[]2014-[]15-[]2-[]r29$[][]\T1/ptm/m/n/10 , li
mma_voom_sample_weights
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[][]$\T1/zi4/m/n/10 https : / / doi . org / 10 . 1093 / nar / gkv412$[][] \T1/p
tm/m/n/10 or DE-Seq2 [][]$\T1/zi4/m/n/10 https : / / doi . org / 10 . 1186 / s1
3059-[]014-[]0550-[]8$[][]
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[]\T1/ptm/m/n/10 # Fil-ter keep_abundant( fac-tor_of_interest = !!(as.symbol(pa
rse_formula(.formula)[1])), min-i-mum_counts
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[]\T1/ptm/m/n/10 # For-mat se-lect(!!.transcript,!!.sample,!!.abundance) spread
(!!.sample,!!.abundance) as_matrix(rownames
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[]\T1/ptm/m/n/10 # edgeR edgeR::DGEList(counts = .) edgeR::calcNormFactors(meth
od = scal-ing_method) edgeR::estimateDisp(design)
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[]\T1/ptm/m/n/10 # Fit edgeR::glmQLFit(design) edgeR::glmQLFTest(coef = 2, con-
trast = my_contrasts) // or glmLRT
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[]\T1/ptm/m/n/10 Underlying method for DE-Seq2 frame-work: keep_abundant( fac-t
or_of_interest = !!as.symbol(parse_formula(.formula)[[1]]),
[66] [67] [68]
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\T1/ptm/m/n/10 forms: mul-ti-vari-able (rec-om-mended) or uni-vari-able Re-spec
-tively: \"fac-tor_of_interest
[69]
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[]\T1/ptm/m/n/10 A string char-ac-ter. Ei-ther \"ciber-sort\", \"epic\" or \"ll
sr\". The re-gres-sion method
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\T1/ptm/m/n/10 script, !!.abun-dance, method=method, ref-er-ence = ref-er-ence,
ac-tion="get", ... ) [..] betareg::betareg(.my_formula,
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\T1/ptm/m/n/10 !!.abun-dance, method=method, ref-er-ence = ref-er-ence, ac-tion
="get", ... ) [..] mu-tate(.proportion_0_corrected
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[]\T1/ptm/m/n/10 This wrap-per ex-e-cutes en-sem-ble gene en-rich-ment anal-y-s
es of the dataset us-ing EGSEA (DOI:0.12688/f1000research.12544.1)
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[]\T1/ptm/m/n/10 # Make sure tran-script names are ad-ja-cent [...] as_matrix(r
ownames = !!.en-trez) edgeR::DGEList(counts
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[]\T1/ptm/m/n/10 idx = buil-dIdx(entrezIDs = row-names(dge), species = species,
msigdb.gsets = msigdb.gsets, kegg.exclude
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[]\T1/zi4/m/n/9 df_entrez = tidybulk::se_mini |> tidybulk() |> as_tibble() |>
symbol_to_entrez( .transcript = feature, .sample = sample)[]
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[]\T1/zi4/m/n/9 df_entrez = aggregate_duplicates(df_entrez, aggregation_functi
on = sum, .sample = sample, .transcript = entrez, .abundance = count)[]
[74]
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[] \T1/zi4/m/n/9 gene_sets = c("h", "c1", "c2", "c3", "c4", "c5", "c6"
, "c7", "kegg_disease", "kegg_metabolism", "kegg_signaling"),[]
[75] [76]
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[]\T1/zi4/m/n/9 df_entrez = tidybulk::se_mini |> tidybulk() |> as_tibble() |>
symbol_to_entrez( .transcript = feature, .sample = sample)[]
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[]\T1/zi4/m/n/9 df_entrez = aggregate_duplicates(df_entrez, aggregation_functi
on = sum, .sample = sample, .transcript = entrez, .abundance = count)[]
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[]\T1/zi4/m/n/9 df_entrez = mutate(df_entrez, do_test = feature %in% c("TNFRSF
4", "PLCH2", "PADI4", "PAX7"))[]
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[]\T1/ptm/m/n/10 # Ex-e-cute cal-cu-la-tion nest(data = -gs_cat) mu-tate(fit =
map( data, ~ clus-ter-Pro-filer::GSEA( my_entrez_rank,
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[]\T1/zi4/m/n/9 df_entrez = tidybulk::se_mini |> tidybulk() |> as_tibble() |>
symbol_to_entrez( .transcript = feature, .sample = sample)[]
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[]\T1/zi4/m/n/9 df_entrez = aggregate_duplicates(df_entrez, aggregation_functi
on = sum, .sample = sample, .transcript = entrez, .abundance = count)[]
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[]\T1/zi4/m/n/9 df_entrez = mutate(df_entrez, do_test = feature %in% c("TNFRSF
4", "PLCH2", "PADI4", "PAX7"))[]
[80] [81]
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\T1/ptm/m/n/10 forms: mul-ti-vari-able (rec-om-mended) or uni-vari-able Re-spec
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[82]
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[]\T1/ptm/m/n/10 A string char-ac-ter. Ei-ther \"ciber-sort\", \"epic\" or \"ll
sr\". The re-gres-sion method
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[]\T1/ptm/m/n/10 Underlying cus-tom method: data fil-ter(n_aggr > 1) group_by(!
!.sample,!!.transcript) dplyr::mutate(!!.abundance
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[]\T1/zi4/m/n/9 tidybulk::se_mini |> tidybulk() |> select(feature, count) |> h
ead() |> as_matrix(rownames=feature)[]
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[]\T1/zi4/m/n/9 tidybulk::se_mini |> tidybulk() |>filter(sample=="SRR1740034")
|> deconvolve_cellularity(sample, feature, count, cores = 1)[]
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[]\T1/ptm/m/n/10 When `.drop = TRUE`, empty groups are dropped. See [group_by_d
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[]\T1/ptm/m/n/10 Underlying method: edgeR::filterByExpr( data, min.count = min-
i-mum_counts, group = string_factor_of_interest,
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[]\T1/zi4/m/n/9 annotation = tidybulk::counts_SE %>% tidybulk() %>% as_tibble(
) %>% distinct(sample) %>% mutate(source = "AU")[]
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[]\T1/zi4/m/n/9 annotation = tidybulk::counts_SE %>% tidybulk() %>% as_tibble(
) %>% distinct(sample) %>% mutate(source = "AU")[]
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[]\T1/zi4/m/n/9 annotation = tidybulk::counts_SE %>% tidybulk() %>% as_tibble(
) %>% distinct(sample) %>% mutate(source = "AU")[]
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[]\T1/ptm/m/n/10 Underlying method: edgeR::filterByExpr( data, min.count = min-
i-mum_counts, group = string_factor_of_interest,
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[]\T1/zi4/m/n/9 annotation = tidybulk::counts_SE %>% tidybulk() %>% as_tibble(
) %>% distinct(sample) %>% mutate(source = "AU")[]
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[]\T1/zi4/m/n/9 counts.MDS.rotated = rotate_dimensions(counts.MDS, \TS1/cmtt/
m/n/9 `\T1/zi4/m/n/9 Dim1\TS1/cmtt/m/n/9 `\T1/zi4/m/n/9 , \TS1/cmtt/m/n/9 `\T1/
zi4/m/n/9 Dim2\TS1/cmtt/m/n/9 `\T1/zi4/m/n/9 , rotation_degrees = 45, .element
= sample)[]
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[]\T1/ptm/m/n/10 Underlying method edgeR::calcNormFactors(.data, method = c("TM
M","TMMwsp","RLE","upperquartile"))
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[]\T1/zi4/m/n/9 tidybulk::se_mini |> tidybulk() |> as_tibble() |> symbol_to_en
trez(.transcript = feature, .sample = sample)[]
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[]\T1/ptm/m/n/10 A string char-ac-ter. Ei-ther "edgeR_quasi_likelihood" (i.e.,
QLF), "edgeR_likelihood_ratio"
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\T1/ptm/m/n/10 (i.e., LRT), "edger_robust_likelihood_ratio", "DE-Seq2", "limma_
voom", "limma_voom_sample_weights"
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\T1/ptm/m/n/10 and limma-voom (i.e., edgeR::calcNormFactors; "TMM","TMMwsp","RL
E","upperquartile").
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\T1/zi4/m/n/10 btp616$[][]\T1/ptm/m/n/10 , limma-voom [][]$\T1/zi4/m/n/10 https
: / / doi . org / 10 . 1186 / gb-[]2014-[]15-[]2-[]r29$[][]\T1/ptm/m/n/10 , li
mma_voom_sample_weights
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[][]$\T1/zi4/m/n/10 https : / / doi . org / 10 . 1093 / nar / gkv412$[][] \T1/p
tm/m/n/10 or DE-Seq2 [][]$\T1/zi4/m/n/10 https : / / doi . org / 10 . 1186 / s1
3059-[]014-[]0550-[]8$[][]
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[]\T1/ptm/m/n/10 # Fil-ter keep_abundant( fac-tor_of_interest = !!(as.symbol(pa
rse_formula(.formula)[1])), min-i-mum_counts
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[]\T1/ptm/m/n/10 # For-mat se-lect(!!.transcript,!!.sample,!!.abundance) spread
(!!.sample,!!.abundance) as_matrix(rownames
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[]\T1/ptm/m/n/10 # edgeR edgeR::DGEList(counts = .) edgeR::calcNormFactors(meth
od = scal-ing_method) edgeR::estimateDisp(design)
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[]\T1/ptm/m/n/10 # Fit edgeR::glmQLFit(design) edgeR::glmQLFTest(coef = 2, con-
trast = my_contrasts) // or glmLRT
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[]\T1/ptm/m/n/10 Underlying method for DE-Seq2 frame-work: keep_abundant( fac-t
or_of_interest = !!as.symbol(parse_formula(.formula)[[1]]),
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-tively: \"fac-tor_of_interest
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[]\T1/ptm/m/n/10 A string char-ac-ter. Ei-ther \"ciber-sort\", \"epic\" or \"ll
sr\". The re-gres-sion method
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\T1/ptm/m/n/10 script, !!.abun-dance, method=method, ref-er-ence = ref-er-ence,
ac-tion="get", ... ) [..] betareg::betareg(.my_formula,
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\T1/ptm/m/n/10 !!.abun-dance, method=method, ref-er-ence = ref-er-ence, ac-tion
="get", ... ) [..] mu-tate(.proportion_0_corrected
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[]\T1/ptm/m/n/10 This wrap-per ex-e-cutes en-sem-ble gene en-rich-ment anal-y-s
es of the dataset us-ing EGSEA (DOI:0.12688/f1000research.12544.1)
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[]\T1/ptm/m/n/10 # Make sure tran-script names are ad-ja-cent [...] as_matrix(r
ownames = !!.en-trez) edgeR::DGEList(counts
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[]\T1/ptm/m/n/10 idx = buil-dIdx(entrezIDs = row-names(dge), species = species,
msigdb.gsets = msigdb.gsets, kegg.exclude
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[]\T1/zi4/m/n/9 df_entrez = tidybulk::se_mini |> tidybulk() |> as_tibble() |>
symbol_to_entrez( .transcript = feature, .sample = sample)[]
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[]\T1/zi4/m/n/9 df_entrez = aggregate_duplicates(df_entrez, aggregation_functi
on = sum, .sample = sample, .transcript = entrez, .abundance = count)[]
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[] \T1/zi4/m/n/9 gene_sets = c("h", "c1", "c2", "c3", "c4", "c5", "c6"
, "c7", "kegg_disease", "kegg_metabolism", "kegg_signaling"),[]
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[]\T1/zi4/m/n/9 df_entrez = tidybulk::se_mini |> tidybulk() |> as_tibble() |>
symbol_to_entrez( .transcript = feature, .sample = sample)[]
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[]\T1/zi4/m/n/9 df_entrez = aggregate_duplicates(df_entrez, aggregation_functi
on = sum, .sample = sample, .transcript = entrez, .abundance = count)[]
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[]\T1/zi4/m/n/9 df_entrez = mutate(df_entrez, do_test = feature %in% c("TNFRSF
4", "PLCH2", "PADI4", "PAX7"))[]
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[]\T1/ptm/m/n/10 # Ex-e-cute cal-cu-la-tion nest(data = -gs_cat) mu-tate(fit =
map( data, ~ clus-ter-Pro-filer::GSEA( my_entrez_rank,
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[]\T1/zi4/m/n/9 df_entrez = tidybulk::se_mini |> tidybulk() |> as_tibble() |>
symbol_to_entrez( .transcript = feature, .sample = sample)[]
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[]\T1/zi4/m/n/9 df_entrez = mutate(df_entrez, do_test = feature %in% c("TNFRSF
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[]\T1/zi4/m/n/9 annotation = tidybulk::counts_SE %>% tidybulk() %>% as_tibble(
) %>% distinct(sample) %>% mutate(source = "AU")[]
[34] [35] [36]
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[]\T1/ptm/m/n/10 Underlying method: edgeR::filterByExpr( data, min.count = min-
i-mum_counts, group = string_factor_of_interest,
[37]
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[39]
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[]\T1/zi4/m/n/9 annotation = tidybulk::counts_SE %>% tidybulk() %>% as_tibble(
) %>% distinct(sample) %>% mutate(source = "AU")[]
[40] [41] [42] [43] [44] [45] [46]
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[48] [49] [50]
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[52] [53] [54]
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[56]
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[]\T1/zi4/m/n/9 counts.MDS.rotated = rotate_dimensions(counts.MDS, \TS1/cmtt/
m/n/9 `\T1/zi4/m/n/9 Dim1\TS1/cmtt/m/n/9 `\T1/zi4/m/n/9 , \TS1/cmtt/m/n/9 `\T1/
zi4/m/n/9 Dim2\TS1/cmtt/m/n/9 `\T1/zi4/m/n/9 , rotation_degrees = 45, .element
= sample)[]
[57] [58] [59]
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[]\T1/ptm/m/n/10 Underlying method edgeR::calcNormFactors(.data, method = c("TM
M","TMMwsp","RLE","upperquartile"))
[60] [61] [62]
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[]\T1/zi4/m/n/9 tidybulk::se_mini |> tidybulk() |> as_tibble() |> symbol_to_en
trez(.transcript = feature, .sample = sample)[]
[63] [64]
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[]\T1/ptm/m/n/10 A string char-ac-ter. Ei-ther "edgeR_quasi_likelihood" (i.e.,
QLF), "edgeR_likelihood_ratio"
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\T1/ptm/m/n/10 (i.e., LRT), "edger_robust_likelihood_ratio", "DE-Seq2", "limma_
voom", "limma_voom_sample_weights"
[66]
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\T1/ptm/m/n/10 and limma-voom (i.e., edgeR::calcNormFactors; "TMM","TMMwsp","RL
E","upperquartile").
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\T1/zi4/m/n/10 btp616$[][]\T1/ptm/m/n/10 , limma-voom [][]$\T1/zi4/m/n/10 https
: / / doi . org / 10 . 1186 / gb-[]2014-[]15-[]2-[]r29$[][]\T1/ptm/m/n/10 , li
mma_voom_sample_weights
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[][]$\T1/zi4/m/n/10 https : / / doi . org / 10 . 1093 / nar / gkv412$[][] \T1/p
tm/m/n/10 or DE-Seq2 [][]$\T1/zi4/m/n/10 https : / / doi . org / 10 . 1186 / s1
3059-[]014-[]0550-[]8$[][]
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[]\T1/ptm/m/n/10 # Fil-ter keep_abundant( fac-tor_of_interest = !!(as.symbol(pa
rse_formula(.formula)[1])), min-i-mum_counts
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[]\T1/ptm/m/n/10 # For-mat se-lect(!!.transcript,!!.sample,!!.abundance) spread
(!!.sample,!!.abundance) as_matrix(rownames
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[]\T1/ptm/m/n/10 # edgeR edgeR::DGEList(counts = .) edgeR::calcNormFactors(meth
od = scal-ing_method) edgeR::estimateDisp(design)
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[]\T1/ptm/m/n/10 # Fit edgeR::glmQLFit(design) edgeR::glmQLFTest(coef = 2, con-
trast = my_contrasts) // or glmLRT
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[]\T1/ptm/m/n/10 Underlying method for DE-Seq2 frame-work: keep_abundant( fac-t
or_of_interest = !!as.symbol(parse_formula(.formula)[[1]]),
[67] [68] [69]
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\T1/ptm/m/n/10 forms: mul-ti-vari-able (rec-om-mended) or uni-vari-able Re-spec
-tively: \"fac-tor_of_interest
[70]
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[]\T1/ptm/m/n/10 A string char-ac-ter. Ei-ther \"ciber-sort\", \"epic\" or \"ll
sr\". The re-gres-sion method
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\T1/ptm/m/n/10 script, !!.abun-dance, method=method, ref-er-ence = ref-er-ence,
ac-tion="get", ... ) [..] betareg::betareg(.my_formula,
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\T1/ptm/m/n/10 !!.abun-dance, method=method, ref-er-ence = ref-er-ence, ac-tion
="get", ... ) [..] mu-tate(.proportion_0_corrected
[71] [72]
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[74]
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[]\T1/ptm/m/n/10 This wrap-per ex-e-cutes en-sem-ble gene en-rich-ment anal-y-s
es of the dataset us-ing EGSEA (DOI:0.12688/f1000research.12544.1)
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[]\T1/ptm/m/n/10 # Make sure tran-script names are ad-ja-cent [...] as_matrix(r
ownames = !!.en-trez) edgeR::DGEList(counts
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[]\T1/ptm/m/n/10 idx = buil-dIdx(entrezIDs = row-names(dge), species = species,
msigdb.gsets = msigdb.gsets, kegg.exclude
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[]\T1/zi4/m/n/9 df_entrez = tidybulk::se_mini |> tidybulk() |> as_tibble() |>
symbol_to_entrez( .transcript = feature, .sample = sample)[]
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[]\T1/zi4/m/n/9 df_entrez = aggregate_duplicates(df_entrez, aggregation_functi
on = sum, .sample = sample, .transcript = entrez, .abundance = count)[]
[75]
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[] \T1/zi4/m/n/9 gene_sets = c("h", "c1", "c2", "c3", "c4", "c5", "c6"
, "c7", "kegg_disease", "kegg_metabolism", "kegg_signaling"),[]
[76] [77]
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[]\T1/zi4/m/n/9 df_entrez = tidybulk::se_mini |> tidybulk() |> as_tibble() |>
symbol_to_entrez( .transcript = feature, .sample = sample)[]
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[]\T1/zi4/m/n/9 df_entrez = aggregate_duplicates(df_entrez, aggregation_functi
on = sum, .sample = sample, .transcript = entrez, .abundance = count)[]
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[]\T1/zi4/m/n/9 df_entrez = mutate(df_entrez, do_test = feature %in% c("TNFRSF
4", "PLCH2", "PADI4", "PAX7"))[]
[78] [79] [80]
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[]\T1/ptm/m/n/10 # Ex-e-cute cal-cu-la-tion nest(data = -gs_cat) mu-tate(fit =
map( data, ~ clus-ter-Pro-filer::GSEA( my_entrez_rank,
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[]\T1/zi4/m/n/9 df_entrez = tidybulk::se_mini |> tidybulk() |> as_tibble() |>
symbol_to_entrez( .transcript = feature, .sample = sample)[]
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[]\T1/zi4/m/n/9 df_entrez = aggregate_duplicates(df_entrez, aggregation_functi
on = sum, .sample = sample, .transcript = entrez, .abundance = count)[]
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[]\T1/zi4/m/n/9 df_entrez = mutate(df_entrez, do_test = feature %in% c("TNFRSF
4", "PLCH2", "PADI4", "PAX7"))[]
[81] [82]
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\T1/ptm/m/n/10 forms: mul-ti-vari-able (rec-om-mended) or uni-vari-able Re-spec
-tively: \"fac-tor_of_interest
[83]
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[]\T1/ptm/m/n/10 A string char-ac-ter. Ei-ther \"ciber-sort\", \"epic\" or \"ll
sr\". The re-gres-sion method
[84] [85] [86] [87] [88]
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[]| \T1/zi4/m/n/10 vignette_manuscript_signature_boxplot\T1/ptm/m/n/10 ,
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[91]
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\T1/ptm/m/n/10 ,
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[]\T1/ptm/m/n/10 Underlying cus-tom method: data fil-ter(n_aggr > 1) group_by(!
!.sample,!!.transcript) dplyr::mutate(!!.abundance
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[]\T1/ptm/m/n/10 <[`tidy-eval`][dplyr_tidy_eval]> Vari-ables, or func-tions or
vari-ables. Use [desc()]
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[]\T1/zi4/m/n/9 tidybulk::se_mini |> tidybulk() |> select(feature, count) |> h
ead() |> as_matrix(rownames=feature)[]
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[]\T1/zi4/m/n/9 tidybulk::se_mini |> tidybulk() |>filter(sample=="SRR1740034")
|> deconvolve_cellularity(sample, feature, count, cores = 1)[]
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[]\T1/ptm/m/n/10 When `.drop = TRUE`, empty groups are dropped. See [group_by_d
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[]\T1/ptm/m/n/10 Underlying method: edgeR::filterByExpr( data, min.count = min-
i-mum_counts, group = string_factor_of_interest,
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[]\T1/zi4/m/n/9 annotation = tidybulk::counts_SE %>% tidybulk() %>% as_tibble(
) %>% distinct(sample) %>% mutate(source = "AU")[]
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[]\T1/zi4/m/n/9 annotation = tidybulk::counts_SE %>% tidybulk() %>% as_tibble(
) %>% distinct(sample) %>% mutate(source = "AU")[]
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[]\T1/zi4/m/n/9 annotation = tidybulk::counts_SE %>% tidybulk() %>% as_tibble(
) %>% distinct(sample) %>% mutate(source = "AU")[]
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[]\T1/ptm/m/n/10 Underlying method: edgeR::filterByExpr( data, min.count = min-
i-mum_counts, group = string_factor_of_interest,
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[]\T1/zi4/m/n/9 annotation = tidybulk::counts_SE %>% tidybulk() %>% as_tibble(
) %>% distinct(sample) %>% mutate(source = "AU")[]
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[]\T1/zi4/m/n/9 counts.MDS.rotated = rotate_dimensions(counts.MDS, \TS1/cmtt/
m/n/9 `\T1/zi4/m/n/9 Dim1\TS1/cmtt/m/n/9 `\T1/zi4/m/n/9 , \TS1/cmtt/m/n/9 `\T1/
zi4/m/n/9 Dim2\TS1/cmtt/m/n/9 `\T1/zi4/m/n/9 , rotation_degrees = 45, .element
= sample)[]
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[]\T1/ptm/m/n/10 Underlying method edgeR::calcNormFactors(.data, method = c("TM
M","TMMwsp","RLE","upperquartile"))
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[]\T1/zi4/m/n/9 tidybulk::se_mini |> tidybulk() |> as_tibble() |> symbol_to_en
trez(.transcript = feature, .sample = sample)[]
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[]\T1/ptm/m/n/10 A string char-ac-ter. Ei-ther "edgeR_quasi_likelihood" (i.e.,
QLF), "edgeR_likelihood_ratio"
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\T1/ptm/m/n/10 (i.e., LRT), "edger_robust_likelihood_ratio", "DE-Seq2", "limma_
voom", "limma_voom_sample_weights"
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E","upperquartile").
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\T1/zi4/m/n/10 btp616$[][]\T1/ptm/m/n/10 , limma-voom [][]$\T1/zi4/m/n/10 https
: / / doi . org / 10 . 1186 / gb-[]2014-[]15-[]2-[]r29$[][]\T1/ptm/m/n/10 , li
mma_voom_sample_weights
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[][]$\T1/zi4/m/n/10 https : / / doi . org / 10 . 1093 / nar / gkv412$[][] \T1/p
tm/m/n/10 or DE-Seq2 [][]$\T1/zi4/m/n/10 https : / / doi . org / 10 . 1186 / s1
3059-[]014-[]0550-[]8$[][]
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[]\T1/ptm/m/n/10 # Fil-ter keep_abundant( fac-tor_of_interest = !!(as.symbol(pa
rse_formula(.formula)[1])), min-i-mum_counts
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[]\T1/ptm/m/n/10 # For-mat se-lect(!!.transcript,!!.sample,!!.abundance) spread
(!!.sample,!!.abundance) as_matrix(rownames
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[]\T1/ptm/m/n/10 # edgeR edgeR::DGEList(counts = .) edgeR::calcNormFactors(meth
od = scal-ing_method) edgeR::estimateDisp(design)
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[]\T1/ptm/m/n/10 # Fit edgeR::glmQLFit(design) edgeR::glmQLFTest(coef = 2, con-
trast = my_contrasts) // or glmLRT
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[]\T1/ptm/m/n/10 Underlying method for DE-Seq2 frame-work: keep_abundant( fac-t
or_of_interest = !!as.symbol(parse_formula(.formula)[[1]]),
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[]\T1/ptm/m/n/10 A string char-ac-ter. Ei-ther \"ciber-sort\", \"epic\" or \"ll
sr\". The re-gres-sion method
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\T1/ptm/m/n/10 script, !!.abun-dance, method=method, ref-er-ence = ref-er-ence,
ac-tion="get", ... ) [..] betareg::betareg(.my_formula,
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\T1/ptm/m/n/10 !!.abun-dance, method=method, ref-er-ence = ref-er-ence, ac-tion
="get", ... ) [..] mu-tate(.proportion_0_corrected
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[]\T1/ptm/m/n/10 This wrap-per ex-e-cutes en-sem-ble gene en-rich-ment anal-y-s
es of the dataset us-ing EGSEA (DOI:0.12688/f1000research.12544.1)
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[]\T1/ptm/m/n/10 # Make sure tran-script names are ad-ja-cent [...] as_matrix(r
ownames = !!.en-trez) edgeR::DGEList(counts
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[]\T1/ptm/m/n/10 idx = buil-dIdx(entrezIDs = row-names(dge), species = species,
msigdb.gsets = msigdb.gsets, kegg.exclude
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[]\T1/zi4/m/n/9 df_entrez = tidybulk::se_mini |> tidybulk() |> as_tibble() |>
symbol_to_entrez( .transcript = feature, .sample = sample)[]
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[]\T1/zi4/m/n/9 df_entrez = aggregate_duplicates(df_entrez, aggregation_functi
on = sum, .sample = sample, .transcript = entrez, .abundance = count)[]
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[] \T1/zi4/m/n/9 gene_sets = c("h", "c1", "c2", "c3", "c4", "c5", "c6"
, "c7", "kegg_disease", "kegg_metabolism", "kegg_signaling"),[]
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[]\T1/zi4/m/n/9 df_entrez = tidybulk::se_mini |> tidybulk() |> as_tibble() |>
symbol_to_entrez( .transcript = feature, .sample = sample)[]
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[]\T1/zi4/m/n/9 df_entrez = aggregate_duplicates(df_entrez, aggregation_functi
on = sum, .sample = sample, .transcript = entrez, .abundance = count)[]
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[]\T1/zi4/m/n/9 df_entrez = mutate(df_entrez, do_test = feature %in% c("TNFRSF
4", "PLCH2", "PADI4", "PAX7"))[]
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[]\T1/ptm/m/n/10 # Ex-e-cute cal-cu-la-tion nest(data = -gs_cat) mu-tate(fit =
map( data, ~ clus-ter-Pro-filer::GSEA( my_entrez_rank,
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[]\T1/zi4/m/n/9 df_entrez = tidybulk::se_mini |> tidybulk() |> as_tibble() |>
symbol_to_entrez( .transcript = feature, .sample = sample)[]
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[]\T1/zi4/m/n/9 df_entrez = mutate(df_entrez, do_test = feature %in% c("TNFRSF
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[]\T1/ptm/m/n/10 A string char-ac-ter. Ei-ther \"ciber-sort\", \"epic\" or \"ll
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