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Principal Component Analysis on Bioconductor/DESeq2 object
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#' Perform Principal Components Analysis on a DESeqTransform object | |
#' | |
#' This function is based on the `DESeq2::plotPCA()` function, but returns the | |
#' results of `prcomp` in a tidy list format. This is more flexible for further | |
#' custom plotting and exploring factor loadings of the PCA. | |
#' | |
#' @param x an object of class DESeqTransform | |
#' @ntop number of most-variable genes to select. Igored if "genes" is specified. | |
#' @genes character vector of specific genes to use | |
#' | |
#' @return a list with four `data.frame` objects: pc_scores, eigen_values, | |
#' loadings (eigen vectors) and the original data. | |
prcomp.DESeqTransform <- function(x, ntop = 500L, genes = NULL, ...){ | |
require(magrittr) | |
# Get sample info | |
sample_info <- as.data.frame(SummarizedExperiment::colData(x)) | |
# Get counts | |
x <- SummarizedExperiment::assay(x) | |
if(!is.null(genes)){ | |
message("Only using ", genes, " genes as requested.") | |
if(!all(genes %in% rownames(x))) stop("Not all provided genes are in the gene count matrix.") | |
selected_genes <- which(genes %in% rownames(x)) | |
} else if(is.numeric(ntop) & ntop < nrow(x)){ | |
ntop <- round(ntop) | |
message("Only using ", ntop, " most variable genes.") | |
# calculate the variance for each gene | |
rv <- genefilter::rowVars(x) | |
# select the ntop genes by variance | |
selected_genes <- order(rv, decreasing=TRUE)[seq_len(min(ntop, length(rv)))] | |
} else { | |
message("Using all ", nrow(x), " genes.") | |
selected_genes <- 1:nrow(x) | |
} | |
# Get the data for those genes | |
selected_expr <- x[selected_genes, ] | |
# perform a PCA on the data in assay(x) for the selected genes | |
## Need to transpose the matrix as prcomp clusters by rows | |
pca <- prcomp(t(selected_expr), ...) | |
#### Eigen scores table #### | |
# Get sample information from DESeq x | |
# and bind the PC scores | |
pc_scores <- sample_info %>% | |
dplyr::bind_cols(as.data.frame(pca$x)) | |
#### Eigen values table #### | |
eigen_values <- data.frame(PC = colnames(pca$x), stdev = pca$sdev) %>% | |
dplyr::mutate(var = stdev^2, | |
var_pct = var/sum(var), | |
cum_var = cumsum(var_pct), | |
PC = forcats::fct_inorder(PC)) | |
#### Factor loadings table #### | |
factor_loadings <- pca$rotation %>% | |
as.data.frame() %>% | |
dplyr::mutate(gene = row.names(.)) %>% | |
dplyr::select(gene, dplyr::everything()) | |
#### Convert the original data to a data.frame #### | |
selected_expr <- selected_expr %>% | |
as.data.frame() %>% | |
dplyr::rename_all(dplyr::funs(paste0("sample", .))) %>% | |
dplyr::mutate(gene = rownames(.)) %>% | |
dplyr::select(gene, dplyr::everything()) | |
# Return a list with each of these xs | |
return(list(pc_scores = pc_scores, | |
eigen_values = eigen_values, | |
loadings = factor_loadings, | |
original = selected_expr)) | |
} |
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