GSEA Choices
Jesslyn Goh and Mike Cuoco
7/20/2020
Last updated: 2020-08-17
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Knit directory: jesslyn_ovca/analysis/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | d60aeff | jgoh2 | 2020-08-17 | More DE Analysis of other hallmarks |
| Rmd | a27da95 | jgoh2 | 2020-08-16 | GSEA decisions |
| html | a27da95 | jgoh2 | 2020-08-16 | GSEA decisions |
GSEA CHOICES
- This is a continuation of our GSEA choices exploration within the PDX_choices.Rmd file.
- We have previously decided to use the wilcoxon rank sum test, and attempted to determine the best ranking method for GSEA by plotting genes on Volcano Plots and labeling them with their gene ranks. We compared the following ranking methods:
- logFC descending, then padj ascending
- padj ascending, then logFC descending
- -log10padj x logFC descending
- -log10padj x FC descending
Here is the comparison between each ranking method: [
]- While the volcano plots show us that -log10padj x logFC is the best ranking metric because there is a great separation between highly upregulated and downregulated genes, and the false positives are in the middle of the list, the GSEA plot says otherwise.
- We graphed the GSEA plot for DF20, Patient 8. However, while the -log10padj x logFC metric gave us a negative enrichment score, the -log10padj x FC metric gave us a positive enrichment score (which agrees with results from our VlnPlot).
Here, we would like to have a more in-depth analysis of how GSEA results change depending on our ranking methods, and hopefully be able to detect any problems in our code that might have resulted in the discrepancy in GSEA results based on our ranks.
STEP 1 LOAD SEURAT OBJECT
# Load packages
source(here::here('packages.R'))
#Read in PDX RDS object
PDX_DF20 = readRDS("data/Izar_2020/test/jesslyn_PDX_DF20_processed.RDS")
PDX_DF101 = readRDS("data/Izar_2020/test/jesslyn_PDX_DF101_processed.RDS")
PDX_DF68 = readRDS("data/Izar_2020/test/jesslyn_PDX_DF68_processed.RDS")
#Read in SS2 RDS object
SS2Malignant.8 = readRDS(file = "data/Izar_2020/jesslyn_SS2Malignant8_processed.RDS")
SS2Malignant.9 = readRDS(file = "data/Izar_2020/jesslyn_SS2Malignant9_processed.RDS")
# Read in ccgenes
ccgenes = read_lines("data/gene_lists/regev_lab_cell_cycle_genes.txt")
s.genes <- ccgenes[1:43]
g2m.genes <- ccgenes[44:97]
#Read in hallmarks of interest
hallmark_names = read_lines("data/gene_lists/hallmarks.txt")
hallmark.list <- vector(mode = "list", length = length(hallmark_names))
names(hallmark.list) <- hallmark_names
for(hm in hallmark_names){
if(file.exists(glue("data/gene_lists/hallmarks/{hm}_updated.txt"))){
file <- read_lines(glue("data/gene_lists/hallmarks/{hm}_updated.txt"), skip = 1)
hallmark.list[[hm]] <- file
}
else{
file <- read_lines(glue("data/gene_lists/extra/{hm}.txt"), skip =2)
hallmark.list[[hm]] <- file
}
}
#center module and cell cycle scores and reassign to the metadata of each Seurat object
hm.names <- names(SS2Malignant.8@meta.data)[14:61]
for(i in hm.names){
SS2Malignant8.hm.centered <- scale(SS2Malignant.8[[i]], center = TRUE, scale = FALSE)
SS2Malignant.8 <- AddMetaData(SS2Malignant.8, SS2Malignant8.hm.centered, col.name = glue("{i}.centered"))
SS2Malignant9.hm.centered <- scale(SS2Malignant.9[[i]], center = TRUE, scale = FALSE)
SS2Malignant.9 <- AddMetaData(SS2Malignant.9, SS2Malignant9.hm.centered, col.name = glue("{i}.centered"))
DF20.hm.centered <- scale(PDX_DF20[[i]], center = TRUE, scale = FALSE)
PDX_DF20 <- AddMetaData(PDX_DF20, DF20.hm.centered, col.name = glue("{i}.centered"))
DF101.hm.centered <- scale(PDX_DF101[[i]], center = TRUE, scale = FALSE)
PDX_DF101 <- AddMetaData(PDX_DF101, DF101.hm.centered, col.name = glue("{i}.centered"))
DF68.hm.centered <- scale(PDX_DF68[[i]], center = TRUE, scale = FALSE)
PDX_DF68 <- AddMetaData(PDX_DF68, DF68.hm.centered, col.name = glue("{i}.centered"))
}STEP 2 CALL FINDMARKERS
markers <- vector("list", length = 5)
obj.names <- c("DF20", "DF101", "DF68", "SS2.8", "SS2.9")
names(markers) <- obj.names
objs <- c(PDX_DF20, PDX_DF101, PDX_DF68, SS2Malignant.8, SS2Malignant.9)
for(i in 1:5){
obj.name <- obj.names[[i]]
obj <- objs[[i]]
comparisons <- c("MV", "MR", "VR")
obj.markers <- vector("list", length = 3)
names(obj.markers) <- comparisons
if(str_detect(obj.name, "[DF]")){
obj.markers[["MV"]] <- FindMarkers(obj, group.by = "treatment.status", ident.1 = "MRD", ident.2 = "vehicle", logfc.threshold = 0)
obj.markers[["MR"]] <- FindMarkers(obj, group.by = "treatment.status", ident.1 = "MRD", ident.2 = "relapse", logfc.threshold = 0)
obj.markers[["VR"]] <- FindMarkers(obj, group.by = "treatment.status", ident.1 = "vehicle", ident.2 = "relapse", logfc.threshold = 0)
}
else{
p <- str_split(obj.name, "SS2.")[[1]][2]
obj.markers[["MV"]] <- FindMarkers(obj, group.by = "sample", ident.1 = glue("{p}.1"), ident.2 = glue("{p}.0"), logfc.threshold = 0)
obj.markers[["MR"]] <- FindMarkers(obj, group.by = "sample", ident.1 = glue("{p}.1"), ident.2 = glue("{p}.2"), logfc.threshold = 0)
obj.markers[["VR"]] <- FindMarkers(obj, group.by = "sample", ident.1 = glue("{p}.0"), ident.2 = glue("{p}.2"), logfc.threshold = 0)
}
markers[[obj.name]] <- obj.markers
}STEP 3. RANK GENES
# logFC descending, padj ascending ------------
logFC.padj.rank <- vector("list", length = 5)
names(logFC.padj.rank) <- obj.names
for(name in obj.names){
comparisons <- c("MV", "MR", "VR")
obj.markers <- vector("list", length = 3)
names(obj.markers) <- comparisons
for(comparison in comparisons){
obj.markers[[comparison]] <- markers[[name]][[comparison]] %>%
rownames_to_column() %>%
select(rowname, avg_logFC, p_val_adj) %>%
arrange(-avg_logFC, p_val_adj) %>%
select(rowname, avg_logFC)
}
logFC.padj.rank[[name]] <- obj.markers
}
# padj ascending, logFC descending -------------
padj.logFC.rank <- vector("list", length = 5)
names(padj.logFC.rank) <- obj.names
for(name in obj.names){
comparisons <- c("MV", "MR", "VR")
obj.markers <- vector("list", length = 3)
names(obj.markers) <- comparisons
for(comparison in comparisons){
obj.markers[[comparison]] <- markers[[name]][[comparison]] %>%
rownames_to_column() %>%
select(rowname, avg_logFC, p_val_adj) %>%
arrange(p_val_adj, -avg_logFC) %>%
select(rowname, p_val_adj)
}
padj.logFC.rank[[name]] <- obj.markers
}
# -log10padj x logFC descending -------------
neglog10padj.logFC.rank <- vector("list", length = 5)
names(neglog10padj.logFC.rank) <- obj.names
for(name in obj.names){
comparisons <- c("MV", "MR", "VR")
obj.markers <- vector("list", length = 3)
names(obj.markers) <- comparisons
for(comparison in comparisons){
p <- markers[[name]][[comparison]] %>%
rownames_to_column() %>%
select(rowname, avg_logFC, p_val_adj) %>%
mutate("neglog10padj" = -(log10(p_val_adj)))
p <- p %>% mutate("rank.metric" = neglog10padj * avg_logFC)
obj.markers[[comparison]] <- p %>%
arrange(-rank.metric) %>%
select(rowname, rank.metric)
}
neglog10padj.logFC.rank[[name]] <- obj.markers
}
# -log10padj x FC descending -------------
neglog10padj.FC.rank <- vector("list", length = 5)
names(neglog10padj.FC.rank) <- obj.names
for(name in obj.names){
comparisons <- c("MV", "MR", "VR")
obj.markers <- vector("list", length = 3)
names(obj.markers) <- comparisons
for(comparison in comparisons){
p <- markers[[name]][[comparison]] %>%
rownames_to_column() %>%
select(rowname, avg_logFC, p_val_adj) %>%
mutate("neglog10padj" = -(log10(p_val_adj))) %>%
mutate("FC" = exp(avg_logFC))
p <- p %>% mutate("rank.metric" = neglog10padj * FC)
obj.markers[[comparison]] <- p %>% arrange(-rank.metric) %>%
select(rowname, rank.metric)
}
neglog10padj.FC.rank[[name]] <- obj.markers
}STEP 4. RUN GSEA
- We will run GSEA with each ranking method and compare results
# logFC descending, padj ascending -----------
logFC.padj.GSEA.results <- vector("list", length = 5)
logFC.padj.GSEA.plots <- vector("list", length = 5)
names(logFC.padj.GSEA.plots) <- obj.names
names(logFC.padj.GSEA.results) <- obj.names
for(name in obj.names){
comparisons <- c("MV", "MR", "VR")
obj.results <- vector("list", length = 3)
names(obj.results) <- comparisons
obj.plots <- vector("list", length = 3)
names(obj.plots) <- comparisons
for(comparison in comparisons){
ranks <- logFC.padj.rank[[name]][[comparison]] %>% deframe()
obj.results[[comparison]] <- fgsea(hallmark.list, stats = ranks, minSize = 15, maxSize = 500, nperm = 1000) %>%
as_tibble() %>%
select(-leadingEdge, -ES, -nMoreExtreme) %>%
arrange(desc(NES), padj)
padj <- obj.results[[comparison]] %>% filter(pathway == "UNUPDATED.OXPHOS") %>% select(padj) %>% deframe() %>% round(digits = 3)
NES = obj.results[[comparison]] %>% filter(pathway == "UNUPDATED.OXPHOS") %>% select(NES) %>% deframe() %>% round(digits = 3)
obj.plots[[comparison]] <- plotEnrichment(pathway = hallmark.list[["UNUPDATED.OXPHOS"]], stats = ranks) + labs(title = glue("{name} {comparison} OXPHOS GSEA results"), subtitle = glue("(NES = {NES}, padj = {padj})"), caption = "logFC descending, padj ascending")
}
logFC.padj.GSEA.results[[name]] <- obj.results
logFC.padj.GSEA.plots[[name]] <- obj.plots
}
# padj ascending, logFC descending -----------
padj.logFC.GSEA.results <- vector("list", length = 5)
padj.logFC.GSEA.plots <- vector("list", length = 5)
names(padj.logFC.GSEA.plots) <- obj.names
names(padj.logFC.GSEA.results) <- obj.names
for(name in obj.names){
comparisons <- c("MV", "MR", "VR")
obj.results <- vector("list", length = 3)
names(obj.results) <- comparisons
obj.plots <- vector("list", length = 3)
names(obj.plots) <- comparisons
for(comparison in comparisons){
ranks <- padj.logFC.rank[[name]][[comparison]] %>% deframe()
obj.results[[comparison]] <- fgsea(hallmark.list, stats = ranks, minSize = 15, maxSize = 500, nperm = 1000) %>%
as_tibble() %>%
select(-leadingEdge, -ES, -nMoreExtreme) %>%
arrange(desc(NES), padj)
padj <- obj.results[[comparison]] %>% filter(pathway == "UNUPDATED.OXPHOS") %>% select(padj) %>% deframe() %>% round(digits = 3)
NES = obj.results[[comparison]] %>% filter(pathway == "UNUPDATED.OXPHOS") %>% select(NES) %>% deframe() %>% round(digits = 3)
obj.plots[[comparison]] <- plotEnrichment(pathway = hallmark.list[["UNUPDATED.OXPHOS"]], stats = ranks) + labs(title = glue("{name} {comparison} OXPHOS GSEA results"), subtitle = glue("(NES = {NES}, padj = {padj})"), caption = "padj asecdning, logFC descending")
}
padj.logFC.GSEA.results[[name]] <- obj.results
padj.logFC.GSEA.plots[[name]] <- obj.plots
}
# neglog10padj x logFC -----------
neglogpadj.logFC.GSEA.results <- vector("list", length = 5)
neglogpadj.logFC.GSEA.plots <- vector("list", length = 5)
names(neglogpadj.logFC.GSEA.plots) <- obj.names
names(neglogpadj.logFC.GSEA.results) <- obj.names
for(name in obj.names){
comparisons <- c("MV", "MR", "VR")
obj.results <- vector("list", length = 3)
names(obj.results) <- comparisons
obj.plots <- vector("list", length = 3)
names(obj.plots) <- comparisons
for(comparison in comparisons){
ranks <- neglog10padj.logFC.rank[[name]][[comparison]] %>% deframe()
obj.results[[comparison]] <- fgsea(hallmark.list, stats = ranks, minSize = 15, maxSize = 500, nperm = 1000) %>%
as_tibble() %>%
select(-leadingEdge, -ES, -nMoreExtreme) %>%
arrange(desc(NES), padj)
padj <- obj.results[[comparison]] %>% filter(pathway == "UNUPDATED.OXPHOS") %>% select(padj) %>% deframe() %>% round(digits = 3)
NES = obj.results[[comparison]] %>% filter(pathway == "UNUPDATED.OXPHOS") %>% select(NES) %>% deframe() %>% round(digits = 3)
obj.plots[[comparison]] <- plotEnrichment(pathway = hallmark.list[["UNUPDATED.OXPHOS"]], stats = ranks) + labs(title = glue("{name} {comparison} OXPHOS GSEA results"), subtitle = glue("(NES = {NES}, padj = {padj})"), caption = "-log10padj x logFC descending")
}
neglogpadj.logFC.GSEA.results[[name]] <- obj.results
neglogpadj.logFC.GSEA.plots[[name]] <- obj.plots
}
# neglog10padj x FC -----------
neglogpadj.FC.GSEA.results <- vector("list", length = 5)
neglogpadj.FC.GSEA.plots <- vector("list", length = 5)
names(neglogpadj.FC.GSEA.plots) <- obj.names
names(neglogpadj.FC.GSEA.results) <- obj.names
for(name in obj.names){
comparisons <- c("MV", "MR", "VR")
obj.results <- vector("list", length = 3)
names(obj.results) <- comparisons
obj.plots <- vector("list", length = 3)
names(obj.plots) <- comparisons
for(comparison in comparisons){
ranks <- neglog10padj.FC.rank[[name]][[comparison]] %>% deframe()
obj.results[[comparison]] <- fgsea(hallmark.list, stats = ranks, minSize = 15, maxSize = 500, nperm = 1000) %>%
as_tibble() %>%
select(-leadingEdge, -ES, -nMoreExtreme) %>%
arrange(desc(NES), padj)
padj <- obj.results[[comparison]] %>% filter(pathway == "UNUPDATED.OXPHOS") %>% select(padj) %>% deframe() %>% round(digits = 3)
NES = obj.results[[comparison]] %>% filter(pathway == "UNUPDATED.OXPHOS") %>% select(NES) %>% deframe() %>% round(digits = 3)
obj.plots[[comparison]] <- plotEnrichment(pathway = hallmark.list[["UNUPDATED.OXPHOS"]], stats = ranks) + labs(title = glue("{name} {comparison} OXPHOS GSEA results"), subtitle = glue("(NES = {NES}, padj = {padj})"), caption = "-log10padj x FC descending")
}
neglogpadj.FC.GSEA.results[[name]] <- obj.results
neglogpadj.FC.GSEA.plots[[name]] <- obj.plots
}
# show plots ----------
GSEA.plots <- vector("list", length = 5)
names(GSEA.plots) <- obj.names
for(name in obj.names){
comparisons <- c("MV", "MR", "VR")
obj.plots <- vector("list", length =3)
names(obj.plots) <- comparisons
for(comparison in comparisons){
p <- logFC.padj.GSEA.plots[[name]][[comparison]] + padj.logFC.GSEA.plots[[name]][[comparison]] + neglogpadj.logFC.GSEA.plots[[name]][[comparison]] + neglogpadj.FC.GSEA.plots[[name]][[comparison]] + plot_layout(ncol = 2, nrow = 2)
obj.plots[[comparison]] <- p
ggsave(plot = p, filename = glue("GSEA.{name}{comparison}.png"), path = "jesslyn_plots/PDX_test/GSEA_paired/test", width = 10, height = 10)
}
GSEA.plots[[name]] <- obj.plots
}
GSEA.plots[["DF20"]][["MV"]]
| Version | Author | Date |
|---|---|---|
| a27da95 | jgoh2 | 2020-08-16 |
GSEA.plots[["DF20"]][["MR"]]
| Version | Author | Date |
|---|---|---|
| a27da95 | jgoh2 | 2020-08-16 |
GSEA.plots[["DF20"]][["VR"]]
| Version | Author | Date |
|---|---|---|
| a27da95 | jgoh2 | 2020-08-16 |
GSEA.plots[["DF101"]][["MV"]]
| Version | Author | Date |
|---|---|---|
| a27da95 | jgoh2 | 2020-08-16 |
GSEA.plots[["DF101"]][["MR"]]
| Version | Author | Date |
|---|---|---|
| a27da95 | jgoh2 | 2020-08-16 |
GSEA.plots[["DF101"]][["VR"]]
| Version | Author | Date |
|---|---|---|
| a27da95 | jgoh2 | 2020-08-16 |
GSEA.plots[["DF68"]][["MV"]]
| Version | Author | Date |
|---|---|---|
| a27da95 | jgoh2 | 2020-08-16 |
GSEA.plots[["DF68"]][["MR"]]
| Version | Author | Date |
|---|---|---|
| a27da95 | jgoh2 | 2020-08-16 |
GSEA.plots[["DF68"]][["VR"]]
| Version | Author | Date |
|---|---|---|
| a27da95 | jgoh2 | 2020-08-16 |
GSEA.plots[["SS2.8"]][["MV"]]
| Version | Author | Date |
|---|---|---|
| a27da95 | jgoh2 | 2020-08-16 |
GSEA.plots[["SS2.8"]][["MR"]]
| Version | Author | Date |
|---|---|---|
| a27da95 | jgoh2 | 2020-08-16 |
GSEA.plots[["SS2.8"]][["VR"]]
| Version | Author | Date |
|---|---|---|
| a27da95 | jgoh2 | 2020-08-16 |
GSEA.plots[["SS2.9"]][["MV"]]
| Version | Author | Date |
|---|---|---|
| a27da95 | jgoh2 | 2020-08-16 |
GSEA.plots[["SS2.9"]][["MR"]]
| Version | Author | Date |
|---|---|---|
| a27da95 | jgoh2 | 2020-08-16 |
GSEA.plots[["SS2.9"]][["VR"]]
| Version | Author | Date |
|---|---|---|
| a27da95 | jgoh2 | 2020-08-16 |
- We get very different results depending on the ranking method we use. -log10padj x logFC and -log10padj x FC seem to give us opposite NES values most of the time. Which of the two metrics is better is not consistent, with x FC sometimes giving us GSEA results that agree more with our other analyses, and x logFC at other times.
- Interesting to note that GSEA plots using -log10padj x logFC or FC metrics give us plots with straight lines, while the plots using only logFC or padj give us curvier plots that look more like what GSEA plots usually look like.
sessionInfo()R version 4.0.2 (2020-06-22)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Mojave 10.14.6
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices datasets utils methods base
other attached packages:
[1] ggbeeswarm_0.6.0 ggpubr_0.4.0 GGally_2.0.0 gt_0.2.1 reshape2_1.4.4
[6] tidyselect_1.1.0 fgsea_1.14.0 presto_1.0.0 data.table_1.12.8 Rcpp_1.0.5
[11] glue_1.4.1 patchwork_1.0.1 EnhancedVolcano_1.6.0 ggrepel_0.8.2 here_0.1
[16] readxl_1.3.1 forcats_0.5.0 stringr_1.4.0 dplyr_1.0.0 purrr_0.3.4
[21] readr_1.3.1 tidyr_1.1.0 tibble_3.0.3 ggplot2_3.3.2 tidyverse_1.3.0
[26] cowplot_1.0.0 Seurat_3.1.5 BiocManager_1.30.10 renv_0.11.0-4
loaded via a namespace (and not attached):
[1] backports_1.1.8 fastmatch_1.1-0 workflowr_1.6.2 plyr_1.8.6 igraph_1.2.5
[6] lazyeval_0.2.2 splines_4.0.2 BiocParallel_1.22.0 listenv_0.8.0 digest_0.6.25
[11] htmltools_0.5.0 fansi_0.4.1 magrittr_1.5 cluster_2.1.0 ROCR_1.0-11
[16] openxlsx_4.1.5 limma_3.44.3 globals_0.12.5 modelr_0.1.8 colorspace_1.4-1
[21] blob_1.2.1 rvest_0.3.5 haven_2.3.1 xfun_0.15 crayon_1.3.4
[26] jsonlite_1.7.0 survival_3.2-3 zoo_1.8-8 ape_5.4 gtable_0.3.0
[31] leiden_0.3.3 car_3.0-8 future.apply_1.6.0 abind_1.4-5 scales_1.1.1
[36] DBI_1.1.0 rstatix_0.6.0 viridisLite_0.3.0 reticulate_1.16 foreign_0.8-80
[41] rsvd_1.0.3 tsne_0.1-3 htmlwidgets_1.5.1 httr_1.4.1 RColorBrewer_1.1-2
[46] ellipsis_0.3.1 ica_1.0-2 farver_2.0.3 pkgconfig_2.0.3 reshape_0.8.8
[51] uwot_0.1.8 dbplyr_1.4.4 labeling_0.3 rlang_0.4.7 later_1.1.0.1
[56] munsell_0.5.0 cellranger_1.1.0 tools_4.0.2 cli_2.0.2 generics_0.0.2
[61] broom_0.7.0 ggridges_0.5.2 evaluate_0.14 yaml_2.2.1 knitr_1.29
[66] fs_1.4.2 fitdistrplus_1.1-1 zip_2.0.4 RANN_2.6.1 pbapply_1.4-2
[71] future_1.18.0 nlme_3.1-148 whisker_0.4 xml2_1.3.2 compiler_4.0.2
[76] rstudioapi_0.11 beeswarm_0.2.3 plotly_4.9.2.1 curl_4.3 png_0.1-7
[81] ggsignif_0.6.0 reprex_0.3.0 stringi_1.4.6 lattice_0.20-41 Matrix_1.2-18
[86] vctrs_0.3.2 pillar_1.4.6 lifecycle_0.2.0 lmtest_0.9-37 RcppAnnoy_0.0.16
[91] irlba_2.3.3 httpuv_1.5.4 R6_2.4.1 promises_1.1.1 KernSmooth_2.23-17
[96] gridExtra_2.3 rio_0.5.16 vipor_0.4.5 codetools_0.2-16 MASS_7.3-51.6
[101] assertthat_0.2.1 rprojroot_1.3-2 withr_2.2.0 sctransform_0.2.1 parallel_4.0.2
[106] hms_0.5.3 grid_4.0.2 rmarkdown_2.3 carData_3.0-4 Rtsne_0.15
[111] git2r_0.27.1 lubridate_1.7.9