Data - Bulk RNAseq

workflowr

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Last updated: 2021-08-16

Checks: 7 0

Knit directory: Turati_NatCancer_2021/

This reproducible R Markdown analysis was created with workflowr (version 1.6.2). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

---

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Repository version: 9254340

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The results in this page were generated with repository version 9254340. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

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Ignored files:
    Ignored:    .Rhistory
    Ignored:    .Rproj.user/
    Ignored:    bulkRNA/
    Ignored:    data/bulk4_counts.rda
    Ignored:    data/bulk4_dds.rda
    Ignored:    data/paper_palette.rda
    Ignored:    data/signatures.rda
    Ignored:    output/deseq2-mini_bulk4_dds.3pts-Treated-vs-Untreated.rds
    Ignored:    output/deseq2-mini_bulk4_dds.pt1-Treated-vs-Untreated.rds
    Ignored:    output/deseq2-mini_bulk4_dds.pt12-Treated-vs-Untreated.rds
    Ignored:    output/deseq2-mini_bulk4_dds.pt13-Treated-vs-Untreated.rds
    Ignored:    output/deseq2-mini_bulk4_dds.pt2-Acutely treated-vs-Chronically treated.rds
    Ignored:    output/deseq2-mini_bulk4_dds.pt2-Acutely treated-vs-Never treated.rds
    Ignored:    output/deseq2-mini_bulk4_dds.pt2-Chronically treated-vs-Never treated.rds
    Ignored:    output/deseq2-mini_bulk4_dds.pt2-Relapse-vs-Never treated.rds
    Ignored:    output/deseq2-mini_bulk4_dds.pt2-Treatment withdrawn-vs-Never treated.rds
    Ignored:    output/fgsea_results.RDS
    Ignored:    output/figures/ExtFig5a_pca_3patients.pdf
    Ignored:    output/figures/ExtFig5b_pca_treatment_response.pdf
    Ignored:    output/figures/Fig5C_fgsea_selected_signatures.pdf
    Ignored:    output/figures/ItemS2.pdf
    Ignored:    output/tables/ExtFig5a_bulkRNAseq_data.xlsx
    Ignored:    output/tables/ExtFig5b_bulkRNAseq_data.xlsx

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

---

These are the previous versions of the repository in which changes were made to the R Markdown (analysis/data-bulkRNAseq.Rmd) and HTML (docs/data-bulkRNAseq.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File	Version	Author	Date	Message
html	b6f5b35	Javier Herrero	2021-08-13	Build site.
html	a7a695e	Javier Herrero	2021-08-13	Build site.
Rmd	85502e2	Javier Herrero	2021-08-13	Fix typo
html	85502e2	Javier Herrero	2021-08-13	Fix typo
Rmd	3b68b3c	Javier Herrero	2021-08-13	Adding data / Bulk RNAseq page
html	3b68b3c	Javier Herrero	2021-08-13	Adding data / Bulk RNAseq page

---

library(tidyverse)
library(DESeq2)
library(DT)
# knitr::opts_chunk$set(cache = T, autodep = T)

This is based on merged_gene_counts.bulk.txt.gz and includes all four patients (PT1, PT2, PT12 and PT13). PT12 and PT13 were added at a later date and the data were re-processed all together from scratch with the NextFlow pipeline described above.

Initial filtering

In this section, we remove:

• genes that are not expressed in almost any sample. More precisely, we remove any gene that does not have at least 1 read count in at least 3 samples.
• samples that do not have at least 20 genes expressed (min read counts > 0)

Then we plot several PCA plots to identify outliers.

merged_gene_counts.bulk <- read.table("data-raw/merged_gene_counts.bulk.txt.gz", row.names = 1, header = T)
colnames(merged_gene_counts.bulk) <- gsub(".", "_", colnames(merged_gene_counts.bulk), fixed = T)
sample_table <- read.csv("data-raw/sample_bulk_rna.csv", stringsAsFactors = F)


# Identify genes that are not expressed in any sample
minExpression <- 1
minSamples <- 3
genes_in_few_samples <- names(which(apply(merged_gene_counts.bulk >= minExpression, 1, sum) >= minSamples))
merged_gene_counts.bulk <- merged_gene_counts.bulk[genes_in_few_samples, ]

# Identify samples that don't have at least 20 genes expressed (very low hanging fruit)
minExpression <- 1
minGenes <- 20
samples_with_few_genes <- names(which(apply(merged_gene_counts.bulk >= minExpression, 2, sum) < minGenes))
samples_to_remove <- c(samples_with_few_genes)

# Filter sample table
sample_table <- sample_table %>% filter(!(title %in% samples_to_remove))

# Filter counts matrix
merged_gene_counts.bulk <- merged_gene_counts.bulk[, sample_table$title]

# Data wrangling, from EGA metadata
sample_table <- sample_table %>%
  dplyr::rename(sample = title, patient = subjectId, group = description) %>%
  dplyr::select(sample, patient, group) %>%
  dplyr::mutate(tissue = case_when(
    patient == "PT1" ~ "",
    patient == "PT12" ~ "",
    patient == "PT13" ~ "",
    grepl("mouse bone marrow", group) ~ "BM",
    grepl("mouse spleen", group) ~ "Spleen",
    grepl("mouse brain", group) ~ "Brain",
    TRUE ~ group
  )) %>%
  dplyr::mutate(group = case_when(
    patient %in% c("PT1", "PT12", "PT13") & grepl("untreated", group) ~ "Untreated",
    patient %in% c("PT1", "PT12", "PT13") & grepl("treated", group) ~ "Treated",
    patient == "PT2" & grepl("untreated control", group) ~ "Never treated",
    patient == "PT2" & grepl("acutely treated", group) ~ "Acutely treated",
    patient == "PT2" & grepl("treatment withdr", group) ~ "Treatment withdrawn",
    patient == "PT2" & grepl("chronically treated", group) ~ "Chronically treated",
    patient == "PT2" & grepl("relapse", group) ~ "Relapse",
    TRUE ~ group
  )) %>%
  dplyr::mutate(patient = factor(patient), group = factor(group), tissue = factor(tissue))

bulk_dds <- DESeq2::DESeqDataSetFromMatrix(merged_gene_counts.bulk,
                                           colData = sample_table,
                                           design = ~ 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]

bulk_vst <- DESeq2::vst(bulk_dds)
pca.pre1 <- DESeq2::plotPCA(bulk_vst[, colData(bulk_vst)$patient == "PT1"], intgroup = c("patient", "group")) +
  ggrepel::geom_text_repel(aes(label = name), size = 2) +
  ggtitle("PT1")
pca.pre2 <- DESeq2::plotPCA(bulk_vst[, colData(bulk_vst)$patient == "PT2"], intgroup = c("patient", "group")) +
  ggrepel::geom_text_repel(aes(label = name), size = 2) +
  ggtitle("PT2")
pca.pre3 <- DESeq2::plotPCA(bulk_vst[, colData(bulk_vst)$patient == "PT12"], intgroup = c("patient", "group")) +
  ggrepel::geom_text_repel(aes(label = name), size = 2) +
  ggtitle("PT12")
pca.pre4 <- DESeq2::plotPCA(bulk_vst[, colData(bulk_vst)$patient == "PT13"], intgroup = c("patient", "group")) +
  ggrepel::geom_text_repel(aes(label = name), size = 2) +
  ggtitle("PT13")
pca.pre5 <- DESeq2::plotPCA(bulk_vst[, colData(bulk_vst)$patient %in% c("PT1", "PT12")], intgroup = c("patient", "group")) +
  ggrepel::geom_text_repel(aes(label = name), size = 2) +
  ggtitle("PT1 + PT12")
pca.pre6 <- DESeq2::plotPCA(bulk_vst[, colData(bulk_vst)$patient %in% c("PT1", "PT12", "PT13")], intgroup = c("patient", "group")) +
  ggrepel::geom_text_repel(aes(label = name), size = 2) +
  ggtitle("PT1 + PT12 + PT13")
pca.pre7 <- DESeq2::plotPCA(bulk_vst, intgroup = c("patient", "group")) +
  ggrepel::geom_text_repel(aes(label = name), size = 2) +
  ggtitle("All patients")

pca.pre1

Past versions of data_wrangling-1.png

Version	Author	Date
a7a695e	Javier Herrero	2021-08-13
3b68b3c	Javier Herrero	2021-08-13

pca.pre2

Past versions of data_wrangling-2.png

Version	Author	Date
a7a695e	Javier Herrero	2021-08-13
3b68b3c	Javier Herrero	2021-08-13

pca.pre3

Past versions of data_wrangling-3.png

Version	Author	Date
a7a695e	Javier Herrero	2021-08-13
3b68b3c	Javier Herrero	2021-08-13

pca.pre4

Past versions of data_wrangling-4.png

Version	Author	Date
a7a695e	Javier Herrero	2021-08-13
3b68b3c	Javier Herrero	2021-08-13

pca.pre5

Past versions of data_wrangling-5.png

Version	Author	Date
a7a695e	Javier Herrero	2021-08-13
3b68b3c	Javier Herrero	2021-08-13

pca.pre6

Past versions of data_wrangling-6.png

Version	Author	Date
a7a695e	Javier Herrero	2021-08-13
3b68b3c	Javier Herrero	2021-08-13

pca.pre7

Past versions of data_wrangling-7.png

Version	Author	Date
a7a695e	Javier Herrero	2021-08-13
3b68b3c	Javier Herrero	2021-08-13

Removing the outliers

List of outliers:

samples_to_remove <- c("VT11_N705_S504", # Outlier in PT1
                       "VTb2_N706_S517", # Acutely treated clustering with untreated
                       "VT47b_N706_S503" # Outlier in PT12
                       )

sample_table %>%
  filter(sample %in% samples_to_remove) %>%
  datatable(rownames = F, options = list(pageLength = 100, ordering = F, dom = 't'))

sample_table <- sample_table %>% filter(!(sample %in% samples_to_remove))

merged_gene_counts.bulk <- merged_gene_counts.bulk[, sample_table$sample]

bulk_dds <- DESeq2::DESeqDataSetFromMatrix(merged_gene_counts.bulk,
                                           colData = sample_table,
                                           design = ~ 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]

bulk_vst <- DESeq2::vst(bulk_dds)

pca.post1 <- DESeq2::plotPCA(bulk_vst[, colData(bulk_vst)$patient == "PT1"], intgroup = c("patient", "group")) +
  ggrepel::geom_text_repel(aes(label = name), size = 2) +
  ggtitle("PT1")
pca.post2 <- DESeq2::plotPCA(bulk_vst[, colData(bulk_vst)$patient == "PT2"], intgroup = c("patient", "group")) +
  ggrepel::geom_text_repel(aes(label = name), size = 2) +
  ggtitle("PT2")
pca.post3 <- DESeq2::plotPCA(bulk_vst[, colData(bulk_vst)$patient == "PT12"], intgroup = c("patient", "group")) +
  ggrepel::geom_text_repel(aes(label = name), size = 2) +
  ggtitle("PT12")
pca.post4 <- DESeq2::plotPCA(bulk_vst[, colData(bulk_vst)$patient == "PT13"], intgroup = c("patient", "group")) +
  ggrepel::geom_text_repel(aes(label = name), size = 2) +
  ggtitle("PT13")
pca.post5 <- DESeq2::plotPCA(bulk_vst[, colData(bulk_vst)$patient %in% c("PT1", "PT12")], intgroup = c("patient", "group")) +
  ggrepel::geom_text_repel(aes(label = name), size = 2) +
  ggtitle("PT1 + PT12")
pca.post6 <- DESeq2::plotPCA(bulk_vst[, colData(bulk_vst)$patient %in% c("PT1", "PT12", "PT13")], intgroup = c("patient", "group")) +
  ggrepel::geom_text_repel(aes(label = name), size = 2) +
  ggtitle("PT1 + PT12 + PT13")
pca.post7 <- DESeq2::plotPCA(bulk_vst, intgroup = c("patient", "group")) +
  ggrepel::geom_text_repel(aes(label = name), size = 2) +
  ggtitle("All patients")

And here are the PCA plots after and before removing the outliers:

cat("### {.tabset .unlisted .unnumbered .toc-ignore}\n\n")

cat("#### Before\n\n")

Before

pca.pre1

Past versions of comparison_before_after_outliers-1.png

Version	Author	Date
a7a695e	Javier Herrero	2021-08-13
3b68b3c	Javier Herrero	2021-08-13

cat("#### After {.active}\n\n")

After

pca.post1

Past versions of comparison_before_after_outliers-2.png

Version	Author	Date
a7a695e	Javier Herrero	2021-08-13
3b68b3c	Javier Herrero	2021-08-13

cat("### {.tabset .unlisted .unnumbered .toc-ignore}\n\n")

cat("#### Before\n\n")

Before

pca.pre2

Past versions of comparison_before_after_outliers-3.png

Version	Author	Date
a7a695e	Javier Herrero	2021-08-13
3b68b3c	Javier Herrero	2021-08-13

cat("#### After {.active}\n\n")

After

pca.post2

Past versions of comparison_before_after_outliers-4.png

Version	Author	Date
a7a695e	Javier Herrero	2021-08-13
3b68b3c	Javier Herrero	2021-08-13

cat("### {.tabset .unlisted .unnumbered .toc-ignore}\n\n")

cat("#### Before\n\n")

Before

pca.pre3

Past versions of comparison_before_after_outliers-5.png

Version	Author	Date
a7a695e	Javier Herrero	2021-08-13
3b68b3c	Javier Herrero	2021-08-13

cat("#### After {.active}\n\n")

After

pca.post3

Past versions of comparison_before_after_outliers-6.png

Version	Author	Date
a7a695e	Javier Herrero	2021-08-13
3b68b3c	Javier Herrero	2021-08-13

cat("### {.tabset .unlisted .unnumbered .toc-ignore}\n\n")

cat("#### Before\n\n")

Before

pca.pre4

Past versions of comparison_before_after_outliers-7.png

Version	Author	Date
a7a695e	Javier Herrero	2021-08-13
3b68b3c	Javier Herrero	2021-08-13

cat("#### After {.active}\n\n")

After

pca.post4

Past versions of comparison_before_after_outliers-8.png

Version	Author	Date
a7a695e	Javier Herrero	2021-08-13
3b68b3c	Javier Herrero	2021-08-13

cat("### {.tabset .unlisted .unnumbered .toc-ignore}\n\n")

cat("#### Before\n\n")

Before

pca.pre5

Past versions of comparison_before_after_outliers-9.png

Version	Author	Date
a7a695e	Javier Herrero	2021-08-13
3b68b3c	Javier Herrero	2021-08-13

cat("#### After {.active}\n\n")

After

pca.post5

Past versions of comparison_before_after_outliers-10.png

Version	Author	Date
a7a695e	Javier Herrero	2021-08-13
3b68b3c	Javier Herrero	2021-08-13

cat("### {.tabset .unlisted .unnumbered .toc-ignore}\n\n")

cat("#### Before\n\n")

Before

pca.pre6

Past versions of comparison_before_after_outliers-11.png

Version	Author	Date
a7a695e	Javier Herrero	2021-08-13
3b68b3c	Javier Herrero	2021-08-13

cat("#### After {.active}\n\n")

After

pca.post6

Past versions of comparison_before_after_outliers-12.png

Version	Author	Date
a7a695e	Javier Herrero	2021-08-13
3b68b3c	Javier Herrero	2021-08-13

cat("### {.tabset .unlisted .unnumbered .toc-ignore}\n\n")

cat("#### Before\n\n")

Before

pca.pre7

Past versions of comparison_before_after_outliers-13.png

Version	Author	Date
a7a695e	Javier Herrero	2021-08-13
3b68b3c	Javier Herrero	2021-08-13

cat("#### After {.active}\n\n")

After

pca.post7

Past versions of comparison_before_after_outliers-14.png

Version	Author	Date
a7a695e	Javier Herrero	2021-08-13
3b68b3c	Javier Herrero	2021-08-13

Storing the data

The data objects stored are called: bulk4_counts and bulk4_dds.

# Save this data object
usethis::use_directory("data")

✓ Setting active project to '/Users/javier/Projects/Turati_NatCancer_2021'

bulk4_counts <- merged_gene_counts.bulk
save(bulk4_counts, file = "data/bulk4_counts.rda")

usethis::use_directory("data")
bulk4_dds <- bulk_dds
save(bulk4_dds, file = "data/bulk4_dds.rda")

Session information

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                     DESeq2_1.26.0              
 [3] SummarizedExperiment_1.16.1 DelayedArray_0.12.3        
 [5] BiocParallel_1.20.1         matrixStats_0.56.0         
 [7] Biobase_2.46.0              GenomicRanges_1.38.0       
 [9] GenomeInfoDb_1.22.0         IRanges_2.20.2             
[11] S4Vectors_0.24.4            BiocGenerics_0.32.0        
[13] forcats_0.5.0               stringr_1.4.0              
[15] dplyr_1.0.0                 purrr_0.3.3                
[17] readr_1.3.1                 tidyr_1.0.2                
[19] tibble_2.1.3                ggplot2_3.3.1              
[21] tidyverse_1.3.0             workflowr_1.6.2            

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        farver_2.0.3          
[10] ggrepel_0.8.2          bit64_0.9-7            AnnotationDbi_1.48.0  
[13] lubridate_1.7.4        xml2_1.2.5             splines_3.6.3         
[16] geneplotter_1.64.0     knitr_1.28             Formula_1.2-3         
[19] jsonlite_1.6.1         broom_0.5.5            annotate_1.64.0       
[22] cluster_2.1.0          dbplyr_1.4.2           png_0.1-7             
[25] compiler_3.6.3         httr_1.4.1             backports_1.1.5       
[28] assertthat_0.2.1       Matrix_1.2-18          cli_3.0.0             
[31] later_1.0.0            acepack_1.4.1          htmltools_0.5.1.1     
[34] tools_3.6.3            gtable_0.3.0           glue_1.3.2            
[37] GenomeInfoDbData_1.2.2 Rcpp_1.0.4             cellranger_1.1.0      
[40] vctrs_0.3.0            nlme_3.1-145           crosstalk_1.1.0.1     
[43] xfun_0.16              rvest_0.3.5            lifecycle_0.2.0       
[46] XML_3.99-0.3           zlibbioc_1.32.0        scales_1.1.0          
[49] hms_0.5.3              promises_1.1.0         RColorBrewer_1.1-2    
[52] yaml_2.2.1             memoise_1.1.0          gridExtra_2.3         
[55] rpart_4.1-15           latticeExtra_0.6-29    stringi_1.4.6         
[58] RSQLite_2.2.0          genefilter_1.68.0      checkmate_2.0.0       
[61] rlang_0.4.11           pkgconfig_2.0.3        bitops_1.0-6          
[64] evaluate_0.14          lattice_0.20-40        labeling_0.3          
[67] htmlwidgets_1.5.1      bit_1.1-15.2           tidyselect_1.1.0      
[70] magrittr_1.5           R6_2.4.1               generics_0.0.2        
[73] Hmisc_4.3-1            DBI_1.1.0              pillar_1.4.3          
[76] haven_2.2.0            whisker_0.4            foreign_0.8-76        
[79] withr_2.4.2            survival_3.1-11        RCurl_1.98-1.1        
[82] nnet_7.3-13            modelr_0.1.6           crayon_1.3.4          
[85] rmarkdown_2.1          usethis_2.0.1          jpeg_0.1-8.1          
[88] locfit_1.5-9.1         grid_3.6.3             readxl_1.3.1          
[91] data.table_1.12.8      blob_1.2.1             git2r_0.26.1          
[94] reprex_0.3.0           digest_0.6.25          xtable_1.8-4          
[97] httpuv_1.5.2           munsell_0.5.0