5: Pathway analysis
Florian Wuenenmann
3/16/2022
Last updated: 2022-04-19
Checks: 7 0
Knit directory: Abatacept_scrnaseq_mi/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| html | a71b19c | Florian Wuennemann | 2022-04-19 | Build site. |
| Rmd | 86ff3e7 | Florian Wuennemann | 2022-04-19 | Main Update to code and adding NichenetR |
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| Rmd | 3796e51 | Florian Wuennemann | 2022-03-25 | wflow_publish("analysis/5_pathway_analysis.Rmd") |
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| Rmd | 8591656 | Florian Wuennemann | 2022-03-24 | wflow_publish(c("analysis/1_process_and_integrate_Seurat_Harmony.Rmd", |
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Aim
In this markdown, we will perform pathway enrichment analysis. We will first score all cells for different pathways and then perform differential pathway activity between cells from control and treatment samples, to identify interesting, differential pathways in response to Abatacept treatment.
Load data
seurat_object_filt <- readRDS(here("../data/2_annotated.seurat_object.rds"))Hallmark gene sets
Let’s use Hallmark gene sets, as they are a small, well defined number of pathways, that can easily be run locally and quickly.
## Load gene sets
gene.sets <- getGeneSets(species = "Mus musculus", library = c("H"))Now we score all cells for pathway activity.
## Only run analysis if it hasn't been computed yet
es_H_object_name <- here("../results/5_enrichment_H.results.rds")
if(file.exists(es_H_object_name)){ ## IF results have been computed, read the object
ES <- readRDS(es_H_object_name)
}else{
ES <- enrichIt(obj = seurat_object_filt,
gene.sets = gene.sets,
groups = 1000, cores = 2,
min.size = NULL)
saveRDS(ES,
file = es_H_object_name)
}Finally, we add the pathway activity as metadata for each cell to the Seurat object.
seurat_ex <- AddMetaData(seurat_object_filt, ES)colorblind_vector <- colorRampPalette(rev(c("#0D0887FF", "#47039FFF",
"#7301A8FF", "#9C179EFF", "#BD3786FF", "#D8576BFF",
"#ED7953FF","#FA9E3BFF", "#FDC926FF", "#F0F921FF")))Perform differential pathway analysis
Now, we will try to identify differential pathways between control and treatment cells for each cell type.
ES2 <- data.frame(seurat_ex[[]], Idents(seurat_ex))all_ES <- data.frame()
for(this_celltype in unique(ES2$cell_type)){
ES2_sub <- subset(ES2,cell_type == this_celltype)
ES2_sub <- ES2_sub %>%
select(starts_with("HALLMARK"),group)
output <- getSignificance(ES2_sub, group = "group", fit = "T.test")
output_form <- output %>%
mutate("cell_type" = this_celltype,
"pathway" = rownames(output))
rownames(output_form) <- 1:nrow(output_form)
all_ES <- rbind(all_ES,output_form)
}
all_ES_sig <- all_ES %>%
arrange(FDR) %>%
subset(FDR < 0.05)Let’s add some more information to this table and save it.
all_ES <- all_ES %>%
mutate("log2foldChange" = log2(median.treatment / median.control)) %>%
mutate("direction" = if_else(log2foldChange > 0,"higher_in_control","higher_in_treatment"))Warning in mask$eval_all_mutate(quo): NaNs produced## Save table
write.table(all_ES,
file = here("../results/pathway_enrichment.GSEA_Hallmark.all_res.tsv"),
sep = "\t",
col.names = TRUE,
row.names = FALSE,
quote = FALSE)Table of enrichment results
Let’s look at a table of the differential pathway results.
## Show table of top differentially active pathways
reactable(all_ES_sig,
resizable = TRUE, showPageSizeOptions = TRUE, searchable = TRUE,
filterable = TRUE,
onClick = "expand", highlight = TRUE)Plot top hits
We can also plot the top hit using a violin plot across cells.
## Plot the top enriched pathway
splitEnrichment(ES2, x.axis = "cell_type", split = "group",
gene.set = all_ES_sig[1,]$pathway) +
coord_flip()
| Version | Author | Date |
|---|---|---|
| 855945a | Florian Wuennemann | 2022-03-25 |
## Let's visualize the enrichment difference for Inflammatory response in activated fibroblasts between control and treatment samples
ES2_fibact <- subset(ES2,cell_type ="Fib_act")
splitEnrichment(ES2_fibact, split = "group", gene.set = all_ES_sig[1,]$pathway)
| Version | Author | Date |
|---|---|---|
| 855945a | Florian Wuennemann | 2022-03-25 |
Heatmap of top differentially regulated pathways by cell type
all_ES_wide <- all_ES %>%
dplyr::select(pathway,cell_type,FDR) %>%
pivot_wider(names_from = cell_type, values_from = FDR)
pathways <- all_ES_wide$pathway
all_ES_wide <- all_ES_wide %>%
select(-pathway)
all_ES_wide <- as.matrix(-log10(all_ES_wide))
rownames(all_ES_wide) <- pathways
library(pheatmap)
ComplexHeatmap::pheatmap(all_ES_wide)
| Version | Author | Date |
|---|---|---|
| a71b19c | Florian Wuennemann | 2022-04-19 |
sessionInfo()R version 4.1.3 (2022-03-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.4 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=de_DE.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=de_DE.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=de_DE.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=de_DE.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] pheatmap_1.0.12 here_1.0.1 reactable_0.2.3 forcats_0.5.1
[5] stringr_1.4.0 dplyr_1.0.8 purrr_0.3.4 readr_2.1.2
[9] tidyr_1.2.0 tibble_3.1.6 tidyverse_1.3.1 dittoSeq_1.7.0
[13] ggplot2_3.3.5 escape_1.4.1 SeuratObject_4.0.4 Seurat_4.1.0
[17] workflowr_1.7.0
loaded via a namespace (and not attached):
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[5] irlba_2.3.5 DelayedArray_0.20.0
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[11] KEGGREST_1.34.0 RCurl_1.98-1.6
[13] generics_0.1.2 BiocGenerics_0.40.0
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[19] RANN_2.6.1 future_1.24.0
[21] bit_4.0.4 tzdb_0.3.0
[23] spatstat.data_2.1-4 xml2_1.3.3
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[27] SummarizedExperiment_1.24.0 assertthat_0.2.1
[29] xfun_0.30 hms_1.1.1
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[165] flexmix_2.3-17 plotly_4.10.0
[167] xtable_1.8-4 jsonlite_1.8.0
[169] modeltools_0.2-23 R6_2.5.1
[171] pillar_1.7.0 htmltools_0.5.2
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