81 lines
2.1 KiB
R
81 lines
2.1 KiB
R
# Principle Component Analysis
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# Normalized with limma::voom
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library(limma)
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library(tidyverse)
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# expression.data. Has columns:
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# - Gene (gene identifier)
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# - [Sample identifiers]
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expression.data <- "mrna_counts_table.csv"
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# results.dir. The directory of where to put the resulting tables (and later your plots.)
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results.dir <- "results"
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# PCA variables
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do.center = TRUE
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do.scale = FALSE
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# The analysis
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# We ise prcomp to calculate the PCAs. Afterwards you should plot the results.
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norm.expr.data <- expression.data %>%
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tibble::column_to_rownames("Gene")
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norm.expr.data <- norm.expr.data[rowSums(norm.expr.data) >= 10,] %>%
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limma::voom() %>%
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as.matrix()
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# Principle Component analysis
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results.dir.pca <- file.path(results.dir, "principle.components")
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dir.create(results.dir.pca, recursive=TRUE)
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norm.expr.data.pcs <- norm.expr.data %>%
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t() %>%
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stats::prcomp(
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center = do.center,
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scale. = do.scale
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)
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# Write summary of PCAs to files
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pcs.summery <- summary(norm.expr.data.pcs)
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pcs.summery$importance %>%
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t() %>%
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as.data.frame() %>%
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tibble::rownames_to_column("PC.name") %>%
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readr::write_csv(
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file.path(results.dir.pca, "importance.csv")
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)
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pcs.summery$x %>%
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t() %>%
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as.data.frame() %>%
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tibble::rownames_to_column("ensembl.id") %>%
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readr::write_csv(
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file.path(results.dir.pca, "values.csv")
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)
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pcs.summery$rotation %>%
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t() %>%
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as.data.frame() %>%
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tibble::rownames_to_column("sample.id") %>%
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readr::write_csv(
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file.path(results.dir.pca, "rotation.csv")
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)
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data.frame(
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rownames = names(pcs.summery$center),
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center = pcs.summery$center,
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scale = pcs.summery$scale
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) %>%
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readr::write_csv(
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file.path(results.dir.pca, "rest.csv")
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)
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# Not saved: pcs.summery$sdev,
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# Next thing to do:
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# - (Optional) scree plot - to determine the optimal cutoff for PCA inclusion based on explaination of variance
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# - (Optional) eigencorplot - to correlate PCAs to clinical variables so that you know which PCA to include for which analysis
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# - (Optional) pairsplot - plot multiple PCAs against each other in a single figure
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# - Plot the first couple of PCAs against each other |