Last updated: 2021-08-13
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Knit directory: Turati_NatCancer_2021/
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htmltools::tagList(rmarkdown::html_dependency_font_awesome())# knitr::opts_chunk$set(cache = T, autodep = T)
library(knitr)
library(tidyverse)
library(DESeq2)
library(annotables)
library(DT)In this document, we are looking at the differences between groups of samples.
Please refer to the Data - Bulk RNAseq page for more info on the starting data.
data("bulk4_dds")DGE <- function(deseq, grouping, c1, c2) {
res <- results(deseq, contrast = c(grouping, c1, c2))
resOrdered <- res[order(res$stat), ]
resOrdered <- resOrdered[complete.cases(resOrdered), ]
resOrdered <- rownames_to_column(data.frame(resOrdered), var = "ensgene") %>%
dplyr::select(ensgene, log2FoldChange, stat, pvalue, padj) %>%
left_join(grch38, by = "ensgene")
return(resOrdered)
}run_DESeq2_for_combinations <- function(deseq_obj, combinations, factor, label,
save_data = F, level = 2) {
if (!missing(label)) {
label <- paste0("deseq2-", label, "-")
} else {
label <- "deseq2-"
}
# fgsea_list <- list()
for (i in 1:nrow(combinations)) {
if (!is.null(opts_knit$get("output.dir"))) {
cat(paste0("\n", paste0(rep("#", level), collapse = ""),
" ", combinations$first[i], " vs ", combinations$second[i], "\n\n"))
}
contrast_name <- paste0(combinations$first[i], "-vs-", combinations$second[i])
my_res <- DGE(deseq_obj,
factor,
combinations$first[i],
combinations$second[i])
filename = paste0(label, contrast_name)
print(htmltools::tagList(
datatable(my_res %>% filter(padj < 0.05 & stat > 0) %>% arrange(desc(stat)) %>% head(n = 100),
caption = paste(combinations$first[i],
"vs", combinations$second[i]),
options = list(dom = 'frtip',
searchHighlight = TRUE)
),
htmltools::tags$br(),
datatable(my_res %>% filter(padj < 0.05 & stat < 0) %>% arrange(stat) %>% head(n = 100),
caption = paste(combinations$first[i],
"vs", combinations$second[i]),
options = list(dom = 'frtip',
searchHighlight = TRUE)
),
htmltools::tags$br()
))
if (save_data) {
save_obj <- my_res
usethis::use_directory("output")
saveRDS(save_obj, file = paste0("output/", filename, ".rds"))
}
}
}mini_bulk4_dds <- bulk4_dds[, colData(bulk4_dds)$patient %in% c("PT1", "PT12", "PT13")]
mini_bulk4_dds$patient <- droplevels(mini_bulk4_dds$patient)
mini_bulk4_dds$group <- droplevels(mini_bulk4_dds$group)
design(mini_bulk4_dds) <- formula(~patient + group)
mini_bulk4_dds <- DESeq(mini_bulk4_dds)estimating size factorsestimating dispersionsgene-wise dispersion estimatesmean-dispersion relationshipfinal dispersion estimatesfitting model and testing-- replacing outliers and refitting for 6627 genes
-- DESeq argument 'minReplicatesForReplace' = 7
-- original counts are preserved in counts(dds)estimating dispersionsfitting model and testingmy_combinations <- colData(mini_bulk4_dds) %>% as_tibble() %>%
group_by(patient, group) %>%
summarise(n = n()) %>%
left_join(filter(., group %in% c("Untreated", "Never treated")),
by = c("patient")) %>%
filter(group.x != group.y) %>%
select(patient, first = group.x, second = group.y, first.n = n.x, second.n = n.y) %>%
mutate_at(c("first", "second"), as.character)`summarise()` regrouping output by 'patient' (override with `.groups` argument)datatable(my_combinations, extensions = "Buttons",
options = list(searchHighlight = TRUE,
buttons = list("copy", 'csv', 'excel')))run_DESeq2_for_combinations(mini_bulk4_dds, my_combinations[1, ], "group",
label = "mini_bulk4_dds.3pts", save_data = T)✓ Setting active project to '/Users/javier/Projects/Turati_NatCancer_2021'mini_bulk4_dds <- bulk4_dds[, colData(bulk4_dds)$patient %in% c("PT1")]
mini_bulk4_dds$group <- droplevels(mini_bulk4_dds$group)
design(mini_bulk4_dds) <- formula(~group)
mini_bulk4_dds <- DESeq(mini_bulk4_dds)estimating size factorsestimating dispersionsgene-wise dispersion estimatesmean-dispersion relationshipfinal dispersion estimatesfitting model and testing-- replacing outliers and refitting for 9435 genes
-- DESeq argument 'minReplicatesForReplace' = 7
-- original counts are preserved in counts(dds)estimating dispersionsfitting model and testingrun_DESeq2_for_combinations(mini_bulk4_dds, my_combinations[1, ], "group",
label = "mini_bulk4_dds.pt1", save_data = T)mini_bulk4_dds <- bulk4_dds[, colData(bulk4_dds)$patient %in% c("PT12")]
mini_bulk4_dds$group <- droplevels(mini_bulk4_dds$group)
design(mini_bulk4_dds) <- formula(~group)
mini_bulk4_dds <- DESeq(mini_bulk4_dds)estimating size factorsestimating dispersionsgene-wise dispersion estimatesmean-dispersion relationshipfinal dispersion estimatesfitting model and testingrun_DESeq2_for_combinations(mini_bulk4_dds, my_combinations[1, ], "group",
label = "mini_bulk4_dds.pt12", save_data = T)mini_bulk4_dds <- bulk4_dds[, colData(bulk4_dds)$patient %in% c("PT13")]
mini_bulk4_dds$group <- droplevels(mini_bulk4_dds$group)
design(mini_bulk4_dds) <- formula(~group)
mini_bulk4_dds <- DESeq(mini_bulk4_dds)estimating size factorsestimating dispersionsgene-wise dispersion estimatesmean-dispersion relationshipfinal dispersion estimatesfitting model and testingrun_DESeq2_for_combinations(mini_bulk4_dds, my_combinations[1, ], "group",
label = "mini_bulk4_dds.pt13", save_data = T)mini_bulk4_dds <- bulk4_dds[, colData(bulk4_dds)$patient %in% c("PT2")]
mini_bulk4_dds$group <- droplevels(mini_bulk4_dds$group)
design(mini_bulk4_dds) <- formula(~group) Note: levels of factors in the design contain characters other than
letters, numbers, '_' and '.'. It is recommended (but not required) to use
only letters, numbers, and delimiters '_' or '.', as these are safe characters
for column names in R. [This is a message, not an warning or error]mini_bulk4_dds <- DESeq(mini_bulk4_dds)estimating size factors
Note: levels of factors in the design contain characters other than
letters, numbers, '_' and '.'. It is recommended (but not required) to use
only letters, numbers, and delimiters '_' or '.', as these are safe characters
for column names in R. [This is a message, not an warning or error]estimating dispersionsgene-wise dispersion estimatesmean-dispersion relationship Note: levels of factors in the design contain characters other than
letters, numbers, '_' and '.'. It is recommended (but not required) to use
only letters, numbers, and delimiters '_' or '.', as these are safe characters
for column names in R. [This is a message, not an warning or error]final dispersion estimates Note: levels of factors in the design contain characters other than
letters, numbers, '_' and '.'. It is recommended (but not required) to use
only letters, numbers, and delimiters '_' or '.', as these are safe characters
for column names in R. [This is a message, not an warning or error]fitting model and testing Note: levels of factors in the design contain characters other than
letters, numbers, '_' and '.'. It is recommended (but not required) to use
only letters, numbers, and delimiters '_' or '.', as these are safe characters
for column names in R. [This is a message, not an warning or error]-- replacing outliers and refitting for 2572 genes
-- DESeq argument 'minReplicatesForReplace' = 7
-- original counts are preserved in counts(dds)estimating dispersions Note: levels of factors in the design contain characters other than
letters, numbers, '_' and '.'. It is recommended (but not required) to use
only letters, numbers, and delimiters '_' or '.', as these are safe characters
for column names in R. [This is a message, not an warning or error]fitting model and testing Note: levels of factors in the design contain characters other than
letters, numbers, '_' and '.'. It is recommended (but not required) to use
only letters, numbers, and delimiters '_' or '.', as these are safe characters
for column names in R. [This is a message, not an warning or error]my_combinations <- colData(mini_bulk4_dds) %>% as_tibble() %>%
group_by(patient, group) %>%
summarise(n = n()) %>%
left_join(filter(., group %in% c("Untreated", "Never treated")),
by = c("patient")) %>%
filter(group.x != group.y) %>%
select(patient, first = group.x, second = group.y, first.n = n.x, second.n = n.y) %>%
mutate_at(c("first", "second"), as.character)`summarise()` regrouping output by 'patient' (override with `.groups` argument)additional_combination <- my_combinations %>%
filter(grepl("Acute", first)) %>%
mutate(second = my_combinations %>%
filter(grepl("Chronic", first)) %>% pull(first),
second.n = my_combinations %>%
filter(grepl("Chronic", first)) %>% pull(first.n))
my_combinations <- rbind(my_combinations, additional_combination)
datatable(my_combinations)run_DESeq2_for_combinations(mini_bulk4_dds, my_combinations, "group",
label = "mini_bulk4_dds.pt2", save_data = T)
sessionInfo()R version 3.6.3 (2020-02-29)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Catalina 10.15.7
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
locale:
[1] en_GB.UTF-8/en_GB.UTF-8/en_GB.UTF-8/C/en_GB.UTF-8/en_GB.UTF-8
attached base packages:
[1] parallel stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] DT_0.13 annotables_0.1.91
[3] DESeq2_1.26.0 SummarizedExperiment_1.16.1
[5] DelayedArray_0.12.3 BiocParallel_1.20.1
[7] matrixStats_0.56.0 Biobase_2.46.0
[9] GenomicRanges_1.38.0 GenomeInfoDb_1.22.0
[11] IRanges_2.20.2 S4Vectors_0.24.4
[13] BiocGenerics_0.32.0 forcats_0.5.0
[15] stringr_1.4.0 dplyr_1.0.0
[17] purrr_0.3.3 readr_1.3.1
[19] tidyr_1.0.2 tibble_2.1.3
[21] ggplot2_3.3.1 tidyverse_1.3.0
[23] knitr_1.28
loaded via a namespace (and not attached):
[1] colorspace_1.4-1 ellipsis_0.3.0 rprojroot_1.3-2
[4] htmlTable_1.13.3 XVector_0.26.0 base64enc_0.1-3
[7] fs_1.3.2 rstudioapi_0.11 bit64_0.9-7
[10] AnnotationDbi_1.48.0 lubridate_1.7.4 xml2_1.2.5
[13] splines_3.6.3 geneplotter_1.64.0 Formula_1.2-3
[16] jsonlite_1.6.1 workflowr_1.6.2 broom_0.5.5
[19] annotate_1.64.0 cluster_2.1.0 dbplyr_1.4.2
[22] png_0.1-7 compiler_3.6.3 httr_1.4.1
[25] backports_1.1.5 assertthat_0.2.1 Matrix_1.2-18
[28] cli_3.0.0 later_1.0.0 acepack_1.4.1
[31] htmltools_0.5.1.1 tools_3.6.3 gtable_0.3.0
[34] glue_1.3.2 GenomeInfoDbData_1.2.2 Rcpp_1.0.4
[37] cellranger_1.1.0 vctrs_0.3.0 nlme_3.1-145
[40] crosstalk_1.1.0.1 xfun_0.16 rvest_0.3.5
[43] lifecycle_0.2.0 XML_3.99-0.3 zlibbioc_1.32.0
[46] scales_1.1.0 hms_0.5.3 promises_1.1.0
[49] RColorBrewer_1.1-2 yaml_2.2.1 memoise_1.1.0
[52] gridExtra_2.3 rpart_4.1-15 latticeExtra_0.6-29
[55] stringi_1.4.6 RSQLite_2.2.0 genefilter_1.68.0
[58] checkmate_2.0.0 rlang_0.4.11 pkgconfig_2.0.3
[61] bitops_1.0-6 evaluate_0.14 lattice_0.20-40
[64] htmlwidgets_1.5.1 bit_1.1-15.2 tidyselect_1.1.0
[67] magrittr_1.5 R6_2.4.1 generics_0.0.2
[70] Hmisc_4.3-1 DBI_1.1.0 pillar_1.4.3
[73] haven_2.2.0 foreign_0.8-76 withr_2.4.2
[76] survival_3.1-11 RCurl_1.98-1.1 nnet_7.3-13
[79] modelr_0.1.6 crayon_1.3.4 rmarkdown_2.1
[82] usethis_2.0.1 jpeg_0.1-8.1 locfit_1.5-9.1
[85] grid_3.6.3 readxl_1.3.1 data.table_1.12.8
[88] blob_1.2.1 git2r_0.26.1 reprex_0.3.0
[91] digest_0.6.25 xtable_1.8-4 httpuv_1.5.2
[94] munsell_0.5.0