Last updated: 2020-01-15

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File	Version	Author	Date	Message
Rmd	389248d	ValentinVoillet	2020-01-15	Edits .Rmd (RNA-seq_DE_3)

File creation: February, 22nd 2019
Update: January, 15th 2020

1 Statistical Analysis

1.1 Differential analysis btw outcomes within each time point

Statistical analyses are performed w/ the limma R package (well-established package for RNA-seq and microarray analysis). A linear model is fitted to each gene, and empirical Bayes moderated t-statistics are used to assess differences in expression. Within each time point & PD-1+TIGIT+, four contrasts of interest are investigated

NR vs R - PD-1-TIGIT-;
NR vs R - PD-1+TIGIT+;
NR vs R - PD-1+;
NR vs R - TIGIT+.

We subset the whole dataset into different subsets depending on the combination time point & treatment. An absolute log2-fold change cutoff of 1 and a false discovery rate (FDR) cutoff of 5% are used to determine differentially expressed genes (DEGs); whereas a false discovery rate (FDR) cutoff of 5% is used to determine differentially expressed gene sets (GSEA).

1.1.1 Time point T0 - DEGs

###--- DE Analysis
countData <- readRDS(file = here("output", "RNA_count.rds"))
#- Subsetting
countData.tmp <- countData[, which(countData$time.point == "T0")]
#- phenoData
phenoData.tmp <- pData(countData.tmp)
phenoData.tmp$group <- paste(phenoData.tmp$fraction, phenoData.tmp$outcome, sep = "_")
phenoData.tmp %>% View("pData")
#- Normalization factor
norm.tmp <- calcNormFactors(countData.tmp)
#- Design matrix
myDesign.tmp <- model.matrix(~ 0 + group, data = phenoData.tmp)
colnames(myDesign.tmp) <- str_remove(string = colnames(myDesign.tmp), pattern = "group")
#- Contrast matrix
aovCon.tmp <- makeContrasts(NR_vs_R_DNEG = (DNEG_NR - DNEG_R),
                            NR_vs_R_DPOS = (DPOS_NR - DPOS_R),
                            NR_vs_R_PD1 = (PD1_NR - PD1_R),
                            NR_vs_R_TIGIT = (TIGIT_NR - TIGIT_R),
                            levels = myDesign.tmp)
#- Voom transformation
voomData.tmp <- voom(counts = countData.tmp, design = myDesign.tmp, lib.size = colSums(exprs(countData.tmp)) * norm.tmp)
normData_1 <- voomData.tmp$E

###--- Fitting
fit1.tmp <- lmFit(object = voomData.tmp, design = myDesign.tmp)
fit2.tmp <- contrasts.fit(fit = fit1.tmp, contrasts = aovCon.tmp)
fit2.tmp <- eBayes(fit = fit2.tmp, trend = FALSE)

registerDoMC(2)
results_DEGs_1 <- foreach(i = 1:ncol(aovCon.tmp)) %dopar%
{
  results.tmp <- topTable(fit = fit2.tmp, adjust.method = "fdr", coef = i, number = nrow(voomData.tmp), sort = "P")
  results.tmp <- data.table(Gene = rownames(results.tmp), results.tmp)
  write_csv(x = results.tmp, path = here("output", "output_2019-02-22/", paste0("DEGs_T0_", colnames(aovCon.tmp)[i], ".csv")))
  results.tmp[, Direction := ifelse(adj.P.Val < 0.05 & sign(logFC) == 1 & abs(logFC) >= 1, "Up",
                                    ifelse(adj.P.Val < 0.05 & sign(logFC) == -1 & abs(logFC) >= 1, "Down", "NotDE"))]
  return(results.tmp)
}
names(results_DEGs_1) <- colnames(aovCon.tmp)

results_DEGs_1$NR_vs_R_DNEG[Direction != "NotDE"] # 0 DEG
results_DEGs_1$NR_vs_R_DPOS[Direction != "NotDE"] # 1 DEG
results_DEGs_1$NR_vs_R_PD1[Direction != "NotDE"] # 0 DEG
results_DEGs_1$NR_vs_R_TIGIT[Direction != "NotDE"] # 0 DEG

There are (FDR 5% & log2-FC > 1)

NR vs R - PD-1-TIGIT- - 0 DEG;
NR vs R - PD-1+TIGIT+ - 1 DEG;
NR vs R - PD-1+ - 0 DEG;
NR vs R - TIGIT+ - 0 DEG.

Volcano plots

Tables

Boxplot

1.1.2 Time point T0 - GSEA

Three databases are used (from http://software.broadinstitute.org/gsea/msigdb/collections.jsp)

KEGG pathways;
Hallmark pathways;
Immunologic signatures - c7.

###--- GSEA
gene.sets <- readRDS(file = here("data", "gene-sets", "genesets_human.rds"))
countData <- readRDS(file = here("output", "RNA_count.rds"))

#- countData & norm
countData.tmp <- countData[, which(countData$time.point == "T0")]
phenoData.tmp <- pData(countData.tmp)
phenoData.tmp$group <- paste(phenoData.tmp$fraction, phenoData.tmp$outcome, sep = "_")
norm.tmp <- calcNormFactors(countData.tmp)
myDesign.tmp <- model.matrix(~ 0 + group, data = phenoData.tmp)
colnames(myDesign.tmp) <- str_remove(string = colnames(myDesign.tmp), pattern = "group")
aovCon.tmp <- makeContrasts(NR_vs_R_DNEG = (DNEG_NR - DNEG_R),
                            NR_vs_R_DPOS = (DPOS_NR - DPOS_R),
                            NR_vs_R_PD1 = (PD1_NR - PD1_R),
                            NR_vs_R_TIGIT = (TIGIT_NR - TIGIT_R),
                            levels = myDesign.tmp)
voomData.tmp <- voom(counts = countData.tmp, design = myDesign.tmp, lib.size = colSums(exprs(countData.tmp)) * norm.tmp)

#- Get indices
registerDoMC(2)
indices.list <- foreach(i = 1:length(gene.sets)) %dopar%
{
  indices.tmp <- limma::ids2indices(gene.sets[[i]], rownames(normData_1))
  indices.tmp <- indices.tmp[sapply(indices.tmp, length) >= 5]
  return(indices.tmp)
}
names(indices.list) <- names(gene.sets)

#- GSEA - camera
registerDoMC(2)
results.GSEA_1 <- foreach(i = 1:length(results_DEGs_1)) %dopar%
{
  GSEA.tmp <- foreach(j = 1:length(indices.list)) %do%
  {
    results.tmp <- camera(voomData.tmp, indices.list[[j]], design = myDesign.tmp, contrast = aovCon.tmp[, i], sort = TRUE)
    results.tmp <- data.table(`Gene set` = rownames(results.tmp), results.tmp)
    results.tmp[, Genes := paste(rownames(voomData.tmp)[unlist(indices.list[[j]][`Gene set`])], collapse = ", "), by = `Gene set`]
    write_csv(x = results.tmp, path = here("output", "output_2019-02-22/", paste0("GSEA_T0_", names(indices.list)[j], "_", colnames(aovCon.tmp)[i], ".csv")))
    results.tmp <- results.tmp[FDR < 0.05]
    return(results.tmp)
  }
  names(GSEA.tmp) <- names(indices.list)
  return(GSEA.tmp)
}
names(results.GSEA_1) <- names(results_DEGs_1)

There are (FDR 5%)

NR vs R - PD-1-TIGIT- - 1 gene set - KEGG;
NR vs R - PD-1-TIGIT- - 1 gene set - Hallmark;
NR vs R - PD-1-TIGIT- - 0 gene set - c7;
NR vs R - PD-1+TIGIT+ - 0 gene set - KEGG;
NR vs R - PD-1+TIGIT+ - 1 gene set - Hallmark;
NR vs R - PD-1+TIGIT+ - 0 gene set - c7;
NR vs R - PD-1+ - 0 gene set - KEGG;
NR vs R - PD-1+ - 1 gene set - Hallmark;
NR vs R - PD-1+ - 0 gene set - c7;
NR vs R - TIGIT+ - 1 gene set - KEGG;
NR vs R - TIGIT+ - 1 gene set - Hallmark;
NR vs R - TIGIT+ - 0 gene set - c7.

Please look at files (results) in the output/output_2019-02-22/ folder.

1.1.3 Time point M1 - DEGs

###--- DE Analysis
countData <- readRDS(file = here("output", "RNA_count.rds"))
#- Subsetting
countData.tmp <- countData[, which(countData$time.point == "M1")]
#- phenoData
phenoData.tmp <- pData(countData.tmp)
phenoData.tmp$group <- paste(phenoData.tmp$fraction, phenoData.tmp$outcome, sep = "_")
phenoData.tmp %>% View("pData")
#- Normalization factor
norm.tmp <- calcNormFactors(countData.tmp)
#- Design matrix
myDesign.tmp <- model.matrix(~ 0 + group, data = phenoData.tmp)
colnames(myDesign.tmp) <- str_remove(string = colnames(myDesign.tmp), pattern = "group")
#- Contrast matrix
aovCon.tmp <- makeContrasts(NR_vs_R_DNEG = (DNEG_NR - DNEG_R),
                            NR_vs_R_DPOS = (DPOS_NR - DPOS_R),
                            NR_vs_R_PD1 = (PD1_NR - PD1_R),
                            NR_vs_R_TIGIT = (TIGIT_NR - TIGIT_R),
                            levels = myDesign.tmp)
#- Voom transformation
voomData.tmp <- voom(counts = countData.tmp, design = myDesign.tmp, lib.size = colSums(exprs(countData.tmp)) * norm.tmp)
normData_2 <- voomData.tmp$E

###--- Fitting
fit1.tmp <- lmFit(object = voomData.tmp, design = myDesign.tmp)
fit2.tmp <- contrasts.fit(fit = fit1.tmp, contrasts = aovCon.tmp)
fit2.tmp <- eBayes(fit = fit2.tmp, trend = FALSE)

registerDoMC(2)
results_DEGs_2 <- foreach(i = 1:ncol(aovCon.tmp)) %dopar%
{
  results.tmp <- topTable(fit = fit2.tmp, adjust.method = "fdr", coef = i, number = nrow(voomData.tmp), sort = "P")
  results.tmp <- data.table(Gene = rownames(results.tmp), results.tmp)
  write_csv(x = results.tmp, path = here("output", "output_2019-02-22/", paste0("DEGs_M1_", colnames(aovCon.tmp)[i], ".csv")))
  results.tmp[, Direction := ifelse(adj.P.Val < 0.05 & sign(logFC) == 1 & abs(logFC) >= 1, "Up",
                                    ifelse(adj.P.Val < 0.05 & sign(logFC) == -1 & abs(logFC) >= 1, "Down", "NotDE"))]
  return(results.tmp)
}
names(results_DEGs_2) <- colnames(aovCon.tmp)

results_DEGs_2$NR_vs_R_DNEG[Direction != "NotDE"] # 0 DEG
results_DEGs_2$NR_vs_R_DPOS[Direction != "NotDE"] # 0 DEG
results_DEGs_2$NR_vs_R_PD1[Direction != "NotDE"] # 0 DEG
results_DEGs_2$NR_vs_R_TIGIT[Direction != "NotDE"] # 0 DEG

There are (FDR 5% & log2-FC > 1)

NR vs R - PD-1-TIGIT- - 0 DEG;
NR vs R - PD-1+TIGIT+ - 0 DEG;
NR vs R - PD-1+ - 0 DEG;
NR vs R - TIGIT+ - 0 DEG.

Volcano plots

Tables

1.1.4 Time point M1 - GSEA

Three databases are used (from http://software.broadinstitute.org/gsea/msigdb/collections.jsp)

KEGG pathways;
Hallmark pathways;
Immunologic signatures - c7.

###--- GSEA
gene.sets <- readRDS(file = here("data", "gene-sets", "genesets_human.rds"))
countData <- readRDS(file = here("output", "RNA_count.rds"))

#- countData & norm
countData.tmp <- countData[, which(countData$time.point == "M1")]
phenoData.tmp <- pData(countData.tmp)
phenoData.tmp$group <- paste(phenoData.tmp$fraction, phenoData.tmp$outcome, sep = "_")
norm.tmp <- calcNormFactors(countData.tmp)
myDesign.tmp <- model.matrix(~ 0 + group, data = phenoData.tmp)
colnames(myDesign.tmp) <- str_remove(string = colnames(myDesign.tmp), pattern = "group")
aovCon.tmp <- makeContrasts(NR_vs_R_DNEG = (DNEG_NR - DNEG_R),
                            NR_vs_R_DPOS = (DPOS_NR - DPOS_R),
                            NR_vs_R_PD1 = (PD1_NR - PD1_R),
                            NR_vs_R_TIGIT = (TIGIT_NR - TIGIT_R),
                            levels = myDesign.tmp)
voomData.tmp <- voom(counts = countData.tmp, design = myDesign.tmp, lib.size = colSums(exprs(countData.tmp)) * norm.tmp)

#- Get indices
registerDoMC(2)
indices.list <- foreach(i = 1:length(gene.sets)) %dopar%
{
  indices.tmp <- limma::ids2indices(gene.sets[[i]], rownames(normData_2))
  indices.tmp <- indices.tmp[sapply(indices.tmp, length) >= 5]
  return(indices.tmp)
}
names(indices.list) <- names(gene.sets)

#- GSEA - camera
registerDoMC(2)
results.GSEA_2 <- foreach(i = 1:length(results_DEGs_2)) %dopar%
{
  GSEA.tmp <- foreach(j = 1:length(indices.list)) %do%
  {
    results.tmp <- camera(voomData.tmp, indices.list[[j]], design = myDesign.tmp, contrast = aovCon.tmp[, i], sort = TRUE)
    results.tmp <- data.table(`Gene set` = rownames(results.tmp), results.tmp)
    results.tmp[, Genes := paste(rownames(voomData.tmp)[unlist(indices.list[[j]][`Gene set`])], collapse = ", "), by = `Gene set`]
    write_csv(x = results.tmp, path = here("output", "output_2019-02-22/", paste0("GSEA_M1_", names(indices.list)[j], "_", colnames(aovCon.tmp)[i], ".csv")))
    results.tmp <- results.tmp[FDR < 0.05]
    return(results.tmp)
  }
  names(GSEA.tmp) <- names(indices.list)
  return(GSEA.tmp)
}
names(results.GSEA_2) <- names(results_DEGs_2)

There are (FDR 5%)

NR vs R - PD-1-TIGIT- - 0 gene set - KEGG;
NR vs R - PD-1-TIGIT- - 3 gene sets - Hallmark;
NR vs R - PD-1-TIGIT- - 14 gene sets - c7;
NR vs R - PD-1+TIGIT+ - 1 gene set - KEGG;
NR vs R - PD-1+TIGIT+ - 3 gene sets - Hallmark;
NR vs R - PD-1+TIGIT+ - 41 gene sets - c7;
NR vs R - PD-1+ - 4 gene sets - KEGG;
NR vs R - PD-1+ - 0 gene set - Hallmark;
NR vs R - PD-1+ - 0 gene set - c7;
NR vs R - TIGIT+ - 1 gene set - KEGG;
NR vs R - TIGIT+ - 0 gene set - Hallmark;
NR vs R - TIGIT+ - 3 gene sets - c7.

Please look at files (results) in the output/output_2019-02-22/ folder.

1.1.5 Time point M2 - DEGs

###--- DE Analysis
countData <- readRDS(file = here("output", "RNA_count.rds"))
#- Subsetting
countData.tmp <- countData[, which(countData$time.point == "M2")]
#- phenoData
phenoData.tmp <- pData(countData.tmp)
phenoData.tmp$group <- paste(phenoData.tmp$fraction, phenoData.tmp$outcome, sep = "_")
phenoData.tmp %>% View("pData")
#- Normalization factor
norm.tmp <- calcNormFactors(countData.tmp)
#- Design matrix
myDesign.tmp <- model.matrix(~ 0 + group, data = phenoData.tmp)
colnames(myDesign.tmp) <- str_remove(string = colnames(myDesign.tmp), pattern = "group")
#- Contrast matrix
aovCon.tmp <- makeContrasts(NR_vs_R_DNEG = (DNEG_NR - DNEG_R),
                            NR_vs_R_DPOS = (DPOS_NR - DPOS_R),
                            NR_vs_R_PD1 = (PD1_NR - PD1_R),
                            NR_vs_R_TIGIT = (TIGIT_NR - TIGIT_R),
                            levels = myDesign.tmp)
#- Voom transformation
voomData.tmp <- voom(counts = countData.tmp, design = myDesign.tmp, lib.size = colSums(exprs(countData.tmp)) * norm.tmp)
normData_3 <- voomData.tmp$E

###--- Fitting
fit1.tmp <- lmFit(object = voomData.tmp, design = myDesign.tmp)
fit2.tmp <- contrasts.fit(fit = fit1.tmp, contrasts = aovCon.tmp)
fit2.tmp <- eBayes(fit = fit2.tmp, trend = FALSE)

registerDoMC(2)
results_DEGs_3 <- foreach(i = 1:ncol(aovCon.tmp)) %dopar%
{
  results.tmp <- topTable(fit = fit2.tmp, adjust.method = "fdr", coef = i, number = nrow(voomData.tmp), sort = "P")
  results.tmp <- data.table(Gene = rownames(results.tmp), results.tmp)
  write_csv(x = results.tmp, path = here("output", "output_2019-02-22/", paste0("DEGs_M2_", colnames(aovCon.tmp)[i], ".csv")))
  results.tmp[, Direction := ifelse(adj.P.Val < 0.05 & sign(logFC) == 1 & abs(logFC) >= 1, "Up",
                                    ifelse(adj.P.Val < 0.05 & sign(logFC) == -1 & abs(logFC) >= 1, "Down", "NotDE"))]
  return(results.tmp)
}
names(results_DEGs_3) <- colnames(aovCon.tmp)

results_DEGs_3$NR_vs_R_DNEG[Direction != "NotDE"] # 0 DEG
results_DEGs_3$NR_vs_R_DPOS[Direction != "NotDE"] # 0 DEG
results_DEGs_3$NR_vs_R_PD1[Direction != "NotDE"] # 0 DEG
results_DEGs_3$NR_vs_R_TIGIT[Direction != "NotDE"] # 0 DEG

There are (FDR 5% & log2-FC > 1)

NR vs R - PD-1-TIGIT- - 0 DEG;
NR vs R - PD-1+TIGIT+ - 0 DEG;
NR vs R - PD-1+ - 0 DEG;
NR vs R - TIGIT+ - 0 DEG.

Volcano plots

Tables

1.1.6 Time point M2 - GSEA

Three databases are used (from http://software.broadinstitute.org/gsea/msigdb/collections.jsp)

KEGG pathways;
Hallmark pathways;
Immunologic signatures - c7.

###--- GSEA
gene.sets <- readRDS(file = here("data", "gene-sets", "genesets_human.rds"))
countData <- readRDS(file = here("output", "RNA_count.rds"))

#- countData & norm
countData.tmp <- countData[, which(countData$time.point == "M2")]
phenoData.tmp <- pData(countData.tmp)
phenoData.tmp$group <- paste(phenoData.tmp$fraction, phenoData.tmp$outcome, sep = "_")
norm.tmp <- calcNormFactors(countData.tmp)
myDesign.tmp <- model.matrix(~ 0 + group, data = phenoData.tmp)
colnames(myDesign.tmp) <- str_remove(string = colnames(myDesign.tmp), pattern = "group")
aovCon.tmp <- makeContrasts(NR_vs_R_DNEG = (DNEG_NR - DNEG_R),
                            NR_vs_R_DPOS = (DPOS_NR - DPOS_R),
                            NR_vs_R_PD1 = (PD1_NR - PD1_R),
                            NR_vs_R_TIGIT = (TIGIT_NR - TIGIT_R),
                            levels = myDesign.tmp)
voomData.tmp <- voom(counts = countData.tmp, design = myDesign.tmp, lib.size = colSums(exprs(countData.tmp)) * norm.tmp)

#- Get indices
registerDoMC(2)
indices.list <- foreach(i = 1:length(gene.sets)) %dopar%
{
  indices.tmp <- limma::ids2indices(gene.sets[[i]], rownames(normData_3))
  indices.tmp <- indices.tmp[sapply(indices.tmp, length) >= 5]
  return(indices.tmp)
}
names(indices.list) <- names(gene.sets)

#- GSEA - camera
registerDoMC(2)
results.GSEA_3 <- foreach(i = 1:length(results_DEGs_3)) %dopar%
{
  GSEA.tmp <- foreach(j = 1:length(indices.list)) %do%
  {
    results.tmp <- camera(voomData.tmp, indices.list[[j]], design = myDesign.tmp, contrast = aovCon.tmp[, i], sort = TRUE)
    results.tmp <- data.table(`Gene set` = rownames(results.tmp), results.tmp)
    results.tmp[, Genes := paste(rownames(voomData.tmp)[unlist(indices.list[[j]][`Gene set`])], collapse = ", "), by = `Gene set`]
    write_csv(x = results.tmp, path = here("output", "output_2019-02-22/", paste0("GSEA_M2_", names(indices.list)[j], "_", colnames(aovCon.tmp)[i], ".csv")))
    results.tmp <- results.tmp[FDR < 0.05]
    return(results.tmp)
  }
  names(GSEA.tmp) <- names(indices.list)
  return(GSEA.tmp)
}
names(results.GSEA_3) <- names(results_DEGs_3)

There are (FDR 5%)

NR vs R - PD-1-TIGIT- - 5 gene sets - KEGG;
NR vs R - PD-1-TIGIT- - 3 gene sets - Hallmark;
NR vs R - PD-1-TIGIT- - 0 gene set - c7;
NR vs R - PD-1+TIGIT+ - 4 gene sets - KEGG;
NR vs R - PD-1+TIGIT+ - 3 gene sets - Hallmark;
NR vs R - PD-1+TIGIT+ - 0 gene set - c7;
NR vs R - PD-1+ - 4 gene sets - KEGG;
NR vs R - PD-1+ - 2 gene sets - Hallmark;
NR vs R - PD-1+ - 0 gene sets - c7;
NR vs R - TIGIT+ - 4 gene sets - KEGG;
NR vs R - TIGIT+ - 1 gene set - Hallmark;
NR vs R - TIGIT+ - 4 gene sets - c7.

Please look at files (results) in the output/output_2019-02-22/ folder.

sessionInfo()

R version 3.6.2 (2019-12-12)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Mojave 10.14.6

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] fr_FR.UTF-8/fr_FR.UTF-8/fr_FR.UTF-8/C/fr_FR.UTF-8/fr_FR.UTF-8

attached base packages:
[1] grid      parallel  stats     graphics  grDevices utils     datasets 
[8] methods   base     

other attached packages:
 [1] ComplexHeatmap_2.2.0 DT_0.11              doMC_1.3.6          
 [4] iterators_1.0.12     foreach_1.4.7        here_0.1            
 [7] edgeR_3.28.0         limma_3.42.0         Biobase_2.46.0      
[10] BiocGenerics_0.32.0  data.table_1.12.8    janitor_1.2.0       
[13] forcats_0.4.0        stringr_1.4.0        dplyr_0.8.3         
[16] purrr_0.3.3          readr_1.3.1          tidyr_1.0.0         
[19] tibble_2.1.3         ggplot2_3.2.1        tidyverse_1.3.0     

loaded via a namespace (and not attached):
 [1] nlme_3.1-143        fs_1.3.1            lubridate_1.7.4    
 [4] RColorBrewer_1.1-2  httr_1.4.1          rprojroot_1.3-2    
 [7] tools_3.6.2         backports_1.1.5     R6_2.4.1           
[10] DBI_1.1.0           lazyeval_0.2.2      colorspace_1.4-1   
[13] GetoptLong_0.1.8    withr_2.1.2         tidyselect_0.2.5   
[16] compiler_3.6.2      git2r_0.26.1        cli_2.0.1          
[19] rvest_0.3.5         xml2_1.2.2          labeling_0.3       
[22] scales_1.1.0        digest_0.6.23       rmarkdown_2.0      
[25] pkgconfig_2.0.3     htmltools_0.4.0     fastmap_1.0.1      
[28] dbplyr_1.4.2        htmlwidgets_1.5.1   rlang_0.4.2        
[31] GlobalOptions_0.1.1 readxl_1.3.1        rstudioapi_0.10    
[34] shiny_1.4.0         shape_1.4.4         generics_0.0.2     
[37] farver_2.0.2        jsonlite_1.6        crosstalk_1.0.0    
[40] magrittr_1.5        Rcpp_1.0.3          munsell_0.5.0      
[43] fansi_0.4.1         lifecycle_0.1.0     stringi_1.4.5      
[46] whisker_0.4         yaml_2.2.0          promises_1.1.0     
[49] crayon_1.3.4        lattice_0.20-38     haven_2.2.0        
[52] circlize_0.4.8      hms_0.5.3           locfit_1.5-9.1     
[55] zeallot_0.1.0       knitr_1.26          pillar_1.4.3       
[58] rjson_0.2.20        codetools_0.2-16    reprex_0.3.0       
[61] glue_1.3.1          evaluate_0.14       modelr_0.1.5       
[64] png_0.1-7           vctrs_0.2.1         httpuv_1.5.2       
[67] cellranger_1.1.0    gtable_0.3.0        clue_0.3-57        
[70] assertthat_0.2.1    xfun_0.12           mime_0.8           
[73] xtable_1.8-4        broom_0.5.3         later_1.0.0        
[76] workflowr_1.6.0     cluster_2.1.0

- RNA-seq - DE Analysis (3) -

1 Statistical Analysis

1.1 Differential analysis btw outcomes within each time point

1.1.1 Time point T0 - DEGs

1.1.2 Time point T0 - GSEA

1.1.3 Time point M1 - DEGs

1.1.4 Time point M1 - GSEA

1.1.5 Time point M2 - DEGs

1.1.6 Time point M2 - GSEA