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August 27, 2024 01:39
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Share with Kehinde / raivivek / DeSeq2 analysis of raw bulk RNA-seq
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| [...] | |
| run_differential <- function(counts_mat, metadata, | |
| design = "Disease.Status + Age + Sex + BMI", | |
| min_reads_per_sample = 5, | |
| min_fraction_of_sample = .25) { | |
| sample_ids <- colnames(counts_mat) | |
| message(glue("info: counts matrix: {paste(dim(counts_mat), collapse = ', ')} \t Covariate matrix: {paste(dim(metadata), collapse = ', ')}")) | |
| message("info: preparing colData..") | |
| colData <- metadata %>% | |
| mutate(Sample.ID = factor(Sample.ID, levels = sample_ids, ordered = T), | |
| Sex = factor(Sex), | |
| Age = scale(Age), | |
| BMI = scale(BMI)) %>% | |
| arrange(Sample.ID) %>% | |
| column_to_rownames(var = "Sample.ID") | |
| message(glue("info: filtering genes using {min_reads_per_sample} reads per sample in >={min_fraction_of_sample*100}% of samples..")) | |
| counts_mat <- counts_mat[apply(counts_mat, 1, function(x) sum(x > min_reads_per_sample) >= length(x) * min_fraction_of_sample), ] | |
| message(glue("info: left with {nrow(counts_mat)} genes..")) | |
| if (any(rownames(colData) != colnames(counts_mat))) { print("names don't match. check or error!") } | |
| message("info: creating dds object..") | |
| dds <- DESeq2::DESeqDataSetFromMatrix(countData = counts_mat, colData = colData, | |
| design = ~ !!treat_string_as_expr(design)) | |
| message("info: starting differential analysis..") | |
| dds <- DESeq2::DESeq(dds) | |
| rld <- DESeq2::varianceStabilizingTransformation(dds, blind=TRUE) | |
| message("info: done.") | |
| return(list(dds = dds, rld = rld)) | |
| } | |
| # Start reading data | |
| counts <- read_tsv(opts$counts_file) # counts | |
| protein_coding_idx <- opts$gtf$type == "gene" & opts$gtf$gene_type == "protein_coding" & (!seqnames(opts$gtf) %in% c("chrX", "chrY", "chrM")) | |
| select_idx <- counts$Geneid %in% opts$gtf[protein_coding_idx, ]$gene_id # only protein coding | |
| counts_mat <- as.matrix(counts[select_idx, -c(1:2)]) | |
| rownames(counts_mat) <- opts$gtf[protein_coding_idx, ]$gene_id | |
| metadata <- readRDS(opts$metadata) %>% | |
| filter(!!treat_inputs_as_exprs(opts$samples_exclude_string)) | |
| # exclude QC'ed samples | |
| counts_mat <- counts_mat[, colnames(counts_mat) %in% opts$metadata[[opts$samples_name_col]]] | |
| resDe <- run_differential( | |
| counts_mat, | |
| metadata, | |
| design = opts$design, | |
| min_reads_per_sample = opts$min_reads_per_sample, | |
| min_fraction_of_sample = opts$min_fraction_of_sample | |
| ) | |
| result <- results(resDe$dds, contrast = c(str_split(opts$contrast, ",", simplify = T))) |
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