Differential expression analysis
Depression x CT
Katia de Paiva Lopes
Raj Lab
Department of Neuroscience
Icahn School of Medicine at Mount Sinai
NYC, New York
Raj Lab
Department of Neuroscience
Icahn School of Medicine at Mount Sinai
NYC, New York
2021-03-02
Using Dream | Depression x CT.
Differential expression for repeated measures (dream) uses a linear model model to increase power and decrease false positives for RNA-seq datasets with multiple measurements per individual. The analysis fits seamlessly into the widely used workflow of limma/voom (Law et al. 2014).
expression_dir = "~/ad-omics_hydra/microglia_omics/expression_tables/added_pilot_314s/expr_4brain_regions/"
work_plots = "~/pd-omics/katia/Microglia/MiGA_public_release/DE_diagnosis/"
metadata_path = "~/pd-omics/katia/Microglia/mic_255s/metadata_files/"load(paste0(expression_dir, "Expression_filt_255s.Rdata"))
# dim(genes_counts_exp_3rd) # 19376 255
load(paste0(metadata_path, "metadata_255filt_eng_29jun2020.Rdata"))
# str(metadata3rd_pass)Preparing the data for DEG
The Depression samples are under the main_diagnosis = Psychiatric Diagnosis.
metadata <- metadata3rd_pass
samples_case = as.character(metadata$donor_tissue[metadata$diagnosis %in% "Depression" | metadata$diagnosis %in% "Depression with autism" ])
samples_control = as.character(metadata$donor_tissue[metadata$main_diagnosis %in% "Control"])
rownames(metadata) <- metadata$donor_tissue
#To pick up only the samples for DEG analysis
genes_counts4deg = genes_counts_exp_3rd[, colnames(genes_counts_exp_3rd) %in% c(samples_case,samples_control) ]
metadata4deg = metadata[rownames(metadata) %in% c(samples_case,samples_control) ,]
# all(rownames(metadata4deg) == colnames(genes_counts4deg)) # # The rownames of the metadata needs to be in the same order as colnames expression table
# str(metadata4deg) #shows class for all columns
# table(metadata4deg$main_diagnosis)Samples for DEG
Case samples
[1] “16-024-GFM” “16-024-SVZ” “16-024-THA” “16-028-GTS” “16-028-THA” [6] “16-049-GFM” “16-049-GTS” “16-049-SVZ” “16-049-THA” “16-110-GFM” [11] “16-110-GTS” “16-110-SVZ” “16-110-THA” “16-111-GFM” “16-111-SVZ” [16] “16-111-THA” “16-112-GFM” “16-112-GTS” “16-112-SVZ” “16-112-THA” [21] “16-117-GFM” “16-117-GTS” “16-117-SVZ” “16-117-THA” “16-118-GFM” [26] “16-118-GTS” “16-118-SVZ” “16-118-THA” “17-017-GFM” “17-017-SVZ” [31] “17-017-THA” “17-029-GFM” “17-029-GTS” “17-029-SVZ” “17-029-THA” [36] “17-032-GTS” “17-032-SVZ” “17-032-THA” “17-074-GFM” “17-074-GTS” [41] “17-074-THA” “17-094-GFM” “17-094-GTS” “17-094-SVZ” “17-094-THA” [46] “17-099-GFM” “17-099-GTS” “17-099-SVZ” “17-099-THA” “17-136-GFM” [51] “17-136-GTS” “17-136-SVZ” “17-136-THA” “18-010-GFM” “18-010-GTS” [56] “18-010-SVZ” “18-010-THA” “18-012-SVZ” “18-012-THA” “18-023-GFM” [61] “18-023-GTS” “18-023-SVZ” “18-023-THA” “18-063-GFM” “18-063-GTS” [66] “18-063-THA” “18-074-GFM” “18-074-GTS” “18-074-SVZ” “18-074-THA” [71] “18-079-GFM” “18-079-GTS” “18-079-SVZ” “18-079-THA”
Control samples
[1] “14-005-GFM” “14-005-GTS” “14-015-GFM” “14-015-GTS” “14-029-GFM” [6] “14-029-GTS” “14-051-GTS” “14-069-GFM” “14-069-GTS” “14-075-GFM” [11] “14-075-GTS” “15-018-GFM” “15-018-GTS” “15-027-GFM” “15-034-GFM” [16] “15-055-GFM” “15-055-GTS” “15-074-THA” “15-087-GFM” “15-087-GTS” [21] “15-087-THA” “15-089-GFM” “15-089-GTS” “15-089-THA” “16-027-GFM” [26] “16-027-GTS” “16-038-GFM” “16-046-GFM” “16-046-GTS” “16-046-THA” [31] “16-056-GFM” “16-056-THA” “16-067-GFM” “16-067-GTS” “16-067-THA” [36] “16-078-GFM” “16-078-GTS” “16-078-THA” “16-080-GFM” “16-080-GTS” [41] “16-080-SVZ” “16-080-THA” “16-082-SVZ” “16-116-GFM” “16-116-GTS” [46] “16-137-GFM” “16-137-GTS” “16-137-SVZ” “16-137-THA” “17-003-GFM” [51] “17-003-GTS” “17-003-SVZ” “17-003-THA” “17-005-GFM” “17-005-GTS” [56] “17-005-SVZ” “17-005-THA” “17-016-GFM” “17-016-GTS” “17-016-SVZ” [61] “17-016-THA” “17-043-GFM” “17-043-GTS” “17-043-SVZ” “17-078-GFM” [66] “17-078-GTS” “17-078-SVZ” “17-078-THA” “17-097-GFM” “17-097-GTS” [71] “17-097-SVZ” “17-124-GFM” “17-124-GTS” “17-124-SVZ” “17-124-THA” [76] “18-018-GFM” “18-018-SVZ” “18-018-THA” “18-021-GFM” “18-021-GTS” [81] “18-021-SVZ” “18-021-THA” “18-064-GFM” “18-064-GTS” “18-064-SVZ” [86] “18-064-THA” “18-105-GFM” “18-105-GTS” “18-105-THA” “18-112-GFM” [91] “18-112-GTS” “18-112-THA” “MG-03-SVZ” “MG-09-GFM” “MG-11-GFM” [96] “MG-11-SVZ”
Dream analysis
Depression (74 samples) is a sub-group of the main_diagnosis Psychiatric diagnosis.
params = BiocParallel::MulticoreParam(workers=4, progressbar=T)
register(params)
registerDoParallel(4)
# To include cause of death and C1-C4
metadata4deg$cause_of_death_categories[metadata4deg$cause_of_death_categories %in% NA] <- "Other"
# table(metadata4deg$cause_of_death_categories)
metadata4deg$C1 = metadata4deg$C1 %>% replace_na(median(metadata4deg$C1, na.rm = T))
metadata4deg$C2 = metadata4deg$C2 %>% replace_na(median(metadata4deg$C2, na.rm = T))
metadata4deg$C3 = metadata4deg$C3 %>% replace_na(median(metadata4deg$C3, na.rm = T))
metadata4deg$C4 = metadata4deg$C4 %>% replace_na(median(metadata4deg$C4, na.rm = T))
# Check variance partition version
# packageVersion("variancePartition") # Must be 1.17.7
# The variable to be tested should be a fixed effect
form <- ~ main_diagnosis + sex + (1|donor_id) + age + tissue + (1|cause_of_death_categories) + C1 + C2 + C3 + C4 + picard_pct_mrna_bases + picard_summed_median + picard_pct_ribosomal_bases
# estimate weights using linear mixed model of dream
vobjDream = suppressWarnings( voomWithDreamWeights( genes_counts4deg, form, metadata4deg ) ) # supressing messages because of Biocparallel was generating a lot of messages
# Fit the dream model on each gene
# By default, uses the Satterthwaite approximation for the hypothesis test
fitmm = suppressWarnings (dream( vobjDream, form, metadata4deg ))
# Examine design matrix
#createDT(fitmm$design, 3)
res_dream <- data.frame(topTable(fitmm, coef='main_diagnosisPsychiatric diagnosis',
number=nrow(genes_counts4deg), sort.by = "p"), check.names = F)The t-statistics are not directly comparable since they have different degrees of freedom. In order to be able to compare test statistics, we report z.std which is the p-value transformed into a signed z-score. This can be used for downstream analysis.
Plots from Dream
P Value Distribution
res = res_dream
p = ggplot(res, aes(P.Value))
p + geom_density(color="darkblue", fill="lightblue") +
theme_classic() +
ggtitle("FDR Distribution")
Fold Change Distribution
p = ggplot(res, aes(logFC))
p + geom_density(color = "darkblue", fill = "lightblue") +
theme_classic() +
ggtitle("Fold Change Distribution")
MA Plot
plot.data = res
plot.data$id = rownames(plot.data)
data = data.frame(plot.data)
data$P.Value = -log10(data$P.Value)
data$fifteen = as.factor(abs(data$adj.P.Val < 0.05))
ma = ggplot(data, aes(AveExpr, logFC, color = fifteen))
ma + geom_point() +
scale_color_manual(values = c("black", "red"), labels = c ("> 0.05", "< 0.05")) +
labs(title = "MA plot", color = "labels") +
theme_classic()
#theme(plot.title = element_text(hjust = 0.5)) + ylim (-10,10) + xlim(-4,22)
Volcano Plot
vp = ggplot(data, aes(logFC, P.Value, color = fifteen))
vp + geom_point() +
scale_color_manual(values = c("black", "red"), labels = c("> 0.05", "< 0.05")) +
labs(title = "Gene Level Volcano Plot", color = "FDR") +
#theme(plot.title = element_text(hjust = 0.5)) +
theme_classic() +
xlim(-10,10) + ylim(0, 10) + ylab("-log10 pvalue")
DEG table for download
## Get conversion table for Gencode 30
gencode_30 = read.table("~/pd-omics/katia/ens.geneid.gencode.v30")
colnames(gencode_30) = c("ensembl","symbol")
res$ensembl = rownames(res)
res_name = merge(res, gencode_30, by="ensembl")
rownames(res_name) = res_name$ensembl
res_name = res_name[order(res_name$adj.P.Val), ]
res_name = res_name[, c("symbol", "logFC", "AveExpr", "t", "P.Value", "adj.P.Val", "z.std")]
createDT(res_name)Directionality
LogFC > 0 is up in Case from Dream.
top_6 = head(res_name)
top_6$ensembl = rownames(top_6)
rownames(metadata4deg) = metadata4deg$donor_tissue
genes_voom = genes_counts_voom_3rd[, rownames(metadata4deg)] # Voom 1st pass only for the samples used in this comparison
gene2check = as.data.frame( genes_voom[rownames(top_6) ,])
gene2check$ensembl = rownames(gene2check)
gene2check = merge(gene2check, top_6[, c("symbol", "ensembl")], by = "ensembl")
gene2check_m = melt(gene2check, id.vars = c("ensembl", "symbol"))
gene2check_charac = merge(gene2check_m, metadata4deg, by.x = "variable", by.y = "donor_tissue")
ggplot(gene2check_charac, aes(x = main_diagnosis, y = value, fill = main_diagnosis)) +
geom_boxplot(notch = F, na.rm = T, outlier.color = NA) +
geom_jitter() +
theme_classic() +
facet_wrap(~symbol, scales = "free_y") +
ggpubr::stat_compare_means(label = "p.format", label.x.npc = "centre", method = "wilcox.test")
R version 3.6.2 (2019-12-12) Platform: x86_64-apple-darwin15.6.0 (64-bit) Running under: macOS 10.16
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_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages: [1] parallel stats graphics grDevices utils datasets methods
[8] base
other attached packages: [1] tidyr_1.1.2 dplyr_1.0.2 doParallel_1.0.15
[4] iterators_1.0.12 ggpubr_0.2.4 magrittr_2.0.1
[7] statmod_1.4.32 BiocParallel_1.20.1 variancePartition_1.17.7 [10] Biobase_2.46.0 BiocGenerics_0.32.0 scales_1.1.1
[13] foreach_1.4.8 reshape2_1.4.4 ggplot2_3.3.2
[16] gplots_3.1.0 RColorBrewer_1.1-2 edgeR_3.28.0
[19] limma_3.42.0
loaded via a namespace (and not attached): [1] jsonlite_1.7.1 splines_3.6.2 gtools_3.8.2
[4] BiocManager_1.30.10 yaml_2.2.1 progress_1.2.2
[7] numDeriv_2016.8-1.1 pillar_1.4.7 lattice_0.20-38
[10] glue_1.4.2 digest_0.6.27 ggsignif_0.6.0
[13] minqa_1.2.4 colorspace_2.0-0 htmltools_0.5.0
[16] Matrix_1.2-18 plyr_1.8.6 pkgconfig_2.0.3
[19] purrr_0.3.4 lme4_1.1-21 tibble_3.0.4
[22] generics_0.1.0 farver_2.0.3 ellipsis_0.3.1
[25] DT_0.13 withr_2.3.0 pbkrtest_0.4-8.6
[28] crayon_1.3.4 evaluate_0.14 nlme_3.1-142
[31] MASS_7.3-51.6 tools_3.6.2 prettyunits_1.1.1
[34] hms_0.5.3 lifecycle_0.2.0 stringr_1.4.0
[37] munsell_0.5.0 locfit_1.5-9.1 colorRamps_2.3
[40] compiler_3.6.2 caTools_1.18.0 rlang_0.4.8
[43] grid_3.6.2 nloptr_1.2.1 htmlwidgets_1.5.2
[46] crosstalk_1.1.0.1 bitops_1.0-6 labeling_0.4.2
[49] rmarkdown_2.0 boot_1.3-23 gtable_0.3.0
[52] codetools_0.2-16 lmerTest_3.1-1 R6_2.5.0
[55] knitr_1.26 KernSmooth_2.23-16 stringi_1.5.3
[58] Rcpp_1.0.5 vctrs_0.3.5 tidyselect_1.1.0
[61] xfun_0.11