Differential Gene Expression (DGE) Analysis
Last updated: 2023-07-24
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Knit directory: mecfs-dge-analysis/
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DGE Analysis Setup
Ensure you have all necessary libraries installed and load the helper code.
At a later date, renv will be integrated to ensure
reproducibility of this analysis.
Use the below code to install these packages:
# Install packages from CRAN
install.packages(c("tidyverse", "RColorBrewer", "pheatmap", "gprofiler2", "plotly"))
# Install packages from Bioconductor
install.packages("BiocManager")
BiocManager::install(c("DESeq2", "genefilter", "limma", "biomaRt"))library(tidyverse) # Available via CRAN
library(DESeq2) # Available via Bioconductor
library(RColorBrewer) # Available via CRAN
library(pheatmap) # Available via CRAN
library(genefilter) # Available via Bioconductor
library(limma) # Available via Bioconductor
library(gprofiler2) # Available via CRAN
library(biomaRt) # Available via Bioconductor
library(plotly) # Available via CRAN
library(ggpubr)
library(rmarkdown)Data Import
We will be importing counts data from the star-salmon pipeline and our metadata for the project which is hosted on Box. This also ensures data is properly ordered by sample id.
counts <- read_tsv("data/star-salmon/salmon.merged.gene_counts_length_scaled.tsv")
# Use first column (gene_id) for row names
counts = data.frame(counts, row.names = 1)
counts$Ensembl_ID = row.names(counts)
drop = c("Ensembl_ID","gene_name")
gene_info = counts[,drop]
counts = counts[ , !(names(counts) %in% drop)] # remove both columns
# Import metadata
sample_metadata <- read_csv("data/MECFS_RNAseq_metadata_2023_06_23.csv")
row.names(sample_metadata) <- sample_metadata$RNA_Samples_id
# Check that data is ordered properly
check_order(sample_metadata = sample_metadata, counts = counts)[1] "Data matches and is ordered by sample id."DESeq2 Analysis
sample_metadata$Family = factor(sample_metadata$Family)
sample_metadata$Affected = factor(sample_metadata$Affected)
sample_metadata$Batch = factor(sample_metadata$Batch)
sample_metadata$Gender = factor(sample_metadata$Gender)
# Account for Family later but batch is accounted for
dds <- DESeqDataSetFromMatrix(countData = round(counts), colData = sample_metadata, design = ~ Batch + Affected)
# Pre-filtering: Keep only rows that have at least 10 reads total
keep = rowSums(counts(dds)) >= 10
dds = dds[keep,]
# Run DESeq function
dds = DESeq(dds)
# Normalize gene counts for differences in seq. depth/global differences
counts_norm = counts(dds, normalized=TRUE)Data transformation and visualization
Perform count data transformation by variance stabilizing transformation (vst) on normalized counts.
Batch correction with limma
counts_vst = assay(vsd)
write.csv(counts_vst, file="output/counts_vst.csv")
mm = model.matrix(~ Family + Affected, colData(vsd))
counts_vst_limma = limma::removeBatchEffect(counts_vst, batch=vsd$Batch, design=mm)Coefficients not estimable: batch2 Sample distances heatmap
sampleDists = dist(t(assay(vsd_limma)))
sampleDistMatrix = as.matrix(sampleDists)
rownames(sampleDistMatrix) = paste(vsd_limma$Batch, vsd_limma$Family, sep=" | ")
colnames(sampleDistMatrix) = paste(vsd_limma$RNA_Samples_id, vsd_limma$Family, sep=" | ")
colors = colorRampPalette(rev(brewer.pal(9, "Blues")))(255)
pheatmap(sampleDistMatrix, clustering_distance_rows=sampleDists, clustering_distance_cols=sampleDists, col=colors)
Principal Components Analysis
pcaData = plotPCA(vsd, intgroup=c("Batch", "Family", "Affected"), returnData=TRUE)
percentVar = round(100 * attr(pcaData, "percentVar"))
p1 <- ggplot(pcaData, aes(PC1, PC2, shape=factor(Batch), fill=factor(Affected), color=factor(Family))) + geom_point(size=5) + xlab(paste0("PC1: ",percentVar[1],"% variance")) + ylab(paste0("PC2: ",percentVar[2],"% variance")) + coord_fixed()
p1
ggplot(pcaData, aes(PC1, PC2, shape=factor(Batch), color=factor(Affected))) +
geom_point(size=5) + xlab(paste0("PC1: ",percentVar[1],"% variance")) + ylab(paste0("PC2: ",percentVar[2],"% variance")) + coord_fixed()
Heatmap of top 50 & top 100 genes
This is a heatmap for 50 genes with the highest variance across samples.
topVarGenes = head(order(-rowVars(assay(vsd))),50)
mat = assay(vsd_limma)[ topVarGenes, ]
mat = mat - rowMeans(mat)
df = as.data.frame(colData(vsd)[,c("Batch", "Affected")])
pheatmap(mat, annotation_col=df, fontsize = 5)
This is a heatmap of the top 100 genes with the highest variance across samples.
topVarGenes = head(order(-rowVars(assay(vsd_limma))),100)
mat = assay(vsd_limma)[ topVarGenes, ]
mat = mat - rowMeans(mat)
df = as.data.frame(colData(vsd_limma)[,c("Batch", "Family", "Affected")])
pheatmap(mat, annotation_col=df, fontsize = 6)
Comparison/Contrast of Affected_Yes_vs_No
res_aff_vs_unaff = results(dds, contrast=c("Affected", "Yes", "No"))
res_aff_vs_unaff= res_aff_vs_unaff[order(res_aff_vs_unaff$padj),]
summary(res_aff_vs_unaff)
out of 29623 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 20, 0.068%
LFC < 0 (down) : 10, 0.034%
outliers [1] : 134, 0.45%
low counts [2] : 3997, 13%
(mean count < 1)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?resultswrite.csv(res_aff_vs_unaff, file="output/res_aff_vs_unaff.csv")
res_aff_vs_unaff_df = as.data.frame(res_aff_vs_unaff)
res_aff_vs_unaff_05 = subset(res_aff_vs_unaff_df, padj < 0.05) topgenes_byensemblid = head(rownames(res_aff_vs_unaff_05),50)
topgenes_aff_vs_unaff_05 = assay(vsd_limma)[topgenes_byensemblid,]
topgenes_aff_vs_unaff_05 = topgenes_aff_vs_unaff_05 - rowMeans(topgenes_aff_vs_unaff_05)
# Convert ensemblids
ensemblids <- topgenes_byensemblid
rownames(topgenes_aff_vs_unaff_05) <- gene_info$gene_name[match(ensemblids, gene_info$Ensembl_ID)]
topgenes_aff_vs_unaff_05 <- topgenes_aff_vs_unaff_05[order(row.names(topgenes_aff_vs_unaff_05)), ]
df = as.data.frame(colData(vsd_limma)[,c("Batch", "Family", "Affected")])
pheatmap(topgenes_aff_vs_unaff_05, annotation_col=df, fontsize = 5)
res_aff_vs_unaff_df_genename = res_aff_vs_unaff_df
res_aff_vs_unaff_df_genename$Ensembl_ID = row.names(res_aff_vs_unaff_df)
res_aff_vs_unaff_df_genename = merge(x=res_aff_vs_unaff_df_genename, y=gene_info, by.x ="Ensembl_ID", by.y="Ensembl_ID", all.x=T)
res_aff_vs_unaff_df_genename = res_aff_vs_unaff_df_genename[,c(dim(res_aff_vs_unaff_df_genename)[2],1:dim(res_aff_vs_unaff_df_genename)[2]-1)]
res_aff_vs_unaff_df_genename = res_aff_vs_unaff_df_genename[order(res_aff_vs_unaff_df_genename[,"padj"]),]
write.csv(res_aff_vs_unaff_df_genename,file="output/res_aff_vs_unaff_genename.csv" )res_aff_vs_unaff_df_genename_05= subset(res_aff_vs_unaff_df_genename, padj < 0.05)
res_aff_vs_unaff_df_genename_05 = res_aff_vs_unaff_df_genename_05[order(res_aff_vs_unaff_df_genename_05$padj),]
write.csv(res_aff_vs_unaff_df_genename_05, file="output/res_aff_vs_unaff_df_genename_05.csv")# Select specific genes to show
# set top = 0, then specify genes using label.select argument
ggmaplot(res_aff_vs_unaff_df_genename, main = "Affected vs Unaffected",
fdr = .05, fc = 2, size = 0.4,
genenames = as.vector(res_aff_vs_unaff_df_genename$gene_name),
ggtheme = ggplot2::theme_minimal(),
legend = "top", top = 19, font.label = c("bold", 8), label.rectangle = TRUE,
font.legend = "bold", font.main = "bold"
)
| Version | Author | Date |
|---|---|---|
| ad9ce58 | sdhutchins | 2023-07-01 |
Enrichment analysis
all_genes_res <- gost(query = genes_biomart$ensembl_gene_id, organism = "hsapiens", significant = TRUE)
gostplot(all_genes_res, capped = FALSE, interactive = TRUE)publish_gosttable(all_genes_res, use_colors = TRUE, show_columns = c("source", "term_name", "term_size", "intersection_size"), filename = NULL)
| Version | Author | Date |
|---|---|---|
| 53d12da | sdhutchins | 2023-07-06 |
Genes of Interest
genes_of_interest <- c("ACAD9/CFAP92", "ACADM", "ADORA2A", "ADRA1D", "ANKZF1", "AVPR1B", "CARMIL2", "CCDC178", "CENPF", "COQ2", "CR2", "DCTPP1", "DNASE1L3", "DPEP1", "EN03", "FCRL3", "FASTKD1", "GCKR", "GIMAP2", "HAGHL", "HSD11B1", "IRF2BP2", "KCNJ18", "LRCOL1", "LRBA, MAB21L2", "MFN1", "MRPS18B", "NLRP12", "P2RX7", "PGP", "PIEZO1", "PLCG2", "RERGL", "RPS6KC1", "SCN4A", "SIAE", "SLC11A2", "SLC12A3", "SLC4A1", "SLC6A12", "SLC9A9", "TDO2", "THEMIS", "TF", "TRAFD1", "UBASH3B", "WASHC5")
genes_interest_mart <- retrieve_gene_info(values = genes_of_interest, filters = "hgnc_symbol")
filtered_by_interest <- filter(res_aff_vs_unaff_df_genename, Ensembl_ID %in% genes_interest_mart$ensembl_gene_id_version)
paged_table(filtered_by_interest, options = list(rows.print = 15))
R version 4.1.1 (2021-08-10)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur 10.16
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.1/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] stats4 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] rmarkdown_2.22 ggpubr_0.6.0
[3] plotly_4.10.2 biomaRt_2.50.3
[5] gprofiler2_0.2.2 limma_3.50.3
[7] genefilter_1.76.0 pheatmap_1.0.12
[9] RColorBrewer_1.1-3 DESeq2_1.34.0
[11] SummarizedExperiment_1.24.0 Biobase_2.54.0
[13] MatrixGenerics_1.6.0 matrixStats_1.0.0
[15] GenomicRanges_1.46.1 GenomeInfoDb_1.30.1
[17] IRanges_2.28.0 S4Vectors_0.32.4
[19] BiocGenerics_0.40.0 lubridate_1.9.2
[21] forcats_1.0.0 stringr_1.5.0
[23] dplyr_1.1.2 purrr_1.0.1
[25] readr_2.1.4 tidyr_1.3.0
[27] tibble_3.2.1 ggplot2_3.4.2.9000
[29] tidyverse_2.0.0 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] colorspace_2.1-0 ggsignif_0.6.4 ellipsis_0.3.2
[4] rprojroot_2.0.3 XVector_0.34.0 fs_1.6.2
[7] rstudioapi_0.14 farver_2.1.1 ggrepel_0.9.3
[10] bit64_4.0.5 AnnotationDbi_1.56.2 fansi_1.0.4
[13] xml2_1.3.4 splines_4.1.1 cachem_1.0.8
[16] geneplotter_1.72.0 knitr_1.43 jsonlite_1.8.5
[19] broom_1.0.5 annotate_1.72.0 dbplyr_2.3.2
[22] png_0.1-8 shiny_1.7.4 compiler_4.1.1
[25] httr_1.4.6 backports_1.4.1 Matrix_1.5-4.1
[28] fastmap_1.1.1 lazyeval_0.2.2 cli_3.6.1
[31] later_1.3.1 htmltools_0.5.5 prettyunits_1.1.1
[34] tools_4.1.1 gtable_0.3.3 glue_1.6.2
[37] GenomeInfoDbData_1.2.7 rappdirs_0.3.3 Rcpp_1.0.10
[40] carData_3.0-5 jquerylib_0.1.4 vctrs_0.6.3
[43] Biostrings_2.62.0 crosstalk_1.2.0 xfun_0.39
[46] ps_1.7.5 mime_0.12 timechange_0.2.0
[49] lifecycle_1.0.3 rstatix_0.7.2 XML_3.99-0.14
[52] getPass_0.2-2 zlibbioc_1.40.0 scales_1.2.1
[55] vroom_1.6.3 hms_1.1.3 promises_1.2.0.1
[58] parallel_4.1.1 yaml_2.3.7 curl_5.0.1
[61] gridExtra_2.3 memoise_2.0.1 sass_0.4.6
[64] stringi_1.7.12 RSQLite_2.3.1 highr_0.10
[67] filelock_1.0.2 BiocParallel_1.28.3 rlang_1.1.1
[70] pkgconfig_2.0.3 bitops_1.0-7 evaluate_0.21
[73] lattice_0.21-8 labeling_0.4.2 htmlwidgets_1.6.2
[76] bit_4.0.5 processx_3.8.1 tidyselect_1.2.0
[79] magrittr_2.0.3 R6_2.5.1 generics_0.1.3
[82] DelayedArray_0.20.0 DBI_1.1.3 pillar_1.9.0
[85] whisker_0.4.1 withr_2.5.0 abind_1.4-5
[88] survival_3.5-5 KEGGREST_1.34.0 RCurl_1.98-1.12
[91] car_3.1-2 crayon_1.5.2 utf8_1.2.3
[94] BiocFileCache_2.2.1 tzdb_0.4.0 progress_1.2.2
[97] locfit_1.5-9.8 grid_4.1.1 data.table_1.14.8
[100] blob_1.2.4 callr_3.7.3 git2r_0.32.0
[103] digest_0.6.32 xtable_1.8-4 httpuv_1.6.11
[106] munsell_0.5.0 viridisLite_0.4.2 bslib_0.5.0