This function generates a Relative Log Expression (RLE) plot for visualizing the distribution of expression data after normalization or log transformation.
Usage
plot_rle(
data,
barcodes = NULL,
label_column = NULL,
labels = NULL,
log = TRUE,
batch = NULL,
normalisation = NULL,
spikes = NULL
)Arguments
- data
A tidyseurat object merged with metadata. Must contain columns "Well_ID", "Row", "Column".
- barcodes
A vector of sample barcodes corresponding to Cells(seurat_object).
- label_column
A metadata column name to group the barcodes.
- labels
A vector of labels of the same length as 'barcodes" to group the barcodes.
- log
A logical value indicating whether data should be log-transformed. Defaults to
TRUE.- batch
Either empty, a single value, or a vector corresponding to the number of samples.
- normalisation
One of "raw", "logNorm", "cpm", "clr", "SCT", "DESeq2", "edgeR", "RUVg", "RUVs", "RUVr", "limma_voom", "zinb". If empty, defaults to raw.
- spikes
List of genes to use as spike controls in RUVg
Details
The function performs the following steps:
Ensures integrity of input data
Log-transforms the data
Computes the RLE by subtracting the row medians from each value.
Creates a boxplot using ggplot2 to visualize the distribution of RLE values.
Examples
data("mini_mac")
p <- plot_rle(mini_mac, label_column = "Row")
#> tidyseurat says: Key columns are missing. A data frame is returned for independent data analysis.