pca_sample {PopComm} | R Documentation |
Generate PCA of Ligand-Receptor Interaction Scores
Description
This function performs principal component analysis (PCA) on ligand-receptor (LR) interaction scores across samples, and generates a scatter plot of the first two principal components. Optionally, sample metadata can be used to color the points.
Usage
pca_sample(
lr_scores,
metadata,
selected_sender = NULL,
selected_receiver = NULL,
color_by = NULL,
n_components = 2
)
Arguments
lr_scores |
Data frame containing LR interaction scores per sample (data frame). |
metadata |
Data frame containing sample metadata (data frame). |
selected_sender |
Specific sender cell type to filter, default is None (use all) (character). |
selected_receiver |
Specific receiver cell type to filter, default is None (use all) (character). |
color_by |
|
n_components |
Number of principal components to extract (numeric, default: 2). |
Value
A list with two elements: the first is a ggplot2 PCA scatter plot and the second is the PCA results data frame.
Examples
# PCA of LR Interaction Scores
data(lr_scores_eg)
data(metadata_eg)
res <- pca_sample(lr_scores_eg, metadata_eg, selected_sender = "Cardiac",
selected_receiver = "Perivascular", color_by = "IFN_type")
print(res$plot)
head(res$df)