count.pca: PCA and Pearson correlation plots from counts table

In christensensm/COMPOSE: Calculation and Visualization of Phenotypic CRISPR Screens

Description

Basic comparison of CRISPR screen counts: PCA and Pearson correlation plots

Usage

count.pca(
  countsTable,
  metadata,
  identifier1 = NULL,
  identifier2 = NULL,
  identifier3 = NULL,
  batch = FALSE,
  batch.id = NULL,
  save = F
)

Arguments

countsTable	input matrix containing normalized gRNA counts with gRNA ids as row names
metadata	input dataframe containing sample names and other identifiers or data
identifier1	string identifying column name of metadata with which to adjust color in PCA plot
identifier2	string identifying column name of metadata with which to adjust size in PCA plot
identifier3	string identifying column name of metadata with which to adjust shape in PCA plot
batch	logical - correct for batch effects (requires a batch.id input)
batch.id	numerical - column of metadata that identifies the batch effect to remove
save	logical - do you want to save the violin plot to pdf

Value

ggplot object of the violin plot

See Also

ggbiplot which this function uses to plot PCAs

ggplot which this function uses to plot Pearson correlations

Examples

y <- matrix(rnorm(100*9, mean = 10, sd = 1),100,9)
y[,1:3] <- y[,1:3] + 5
metadata <- data.frame(sample = paste0('sample.',1:9), batch = c("A","A","A","B","B","B","C","C","C"))

count.pca(y, metadata, identifier1 = 'sample')
count.pca(y, metadata, identifier1 = 'sample', batch = T, batch.id = 'batch')
...

christensensm/COMPOSE documentation built on Dec. 22, 2020, 3:43 a.m.