Skip to contents

Generates linear regression plot for a given gene.

linearRegression.Rd

This function creates a scatter plot comparing gene expression levels between two spatial experiments for a specified gene. It colors data points based on similarity classification and overlays fold-change threshold lines.

Usage

linearRegression(input, gene, assayName = NULL)

Arguments

input

A list. Results from `spatialSimilarity()`. This includes the similarity table, log-transformed pixel data, and analysis parameters.

gene

Character. The name of the gene to visualize.

assayName

A character string or numeric specifying the assay in the Spatial Experiment to use. Default is NULL. If no value is supplied for assayName, then the first assay is used as a default

Value

A ggplot2 scatter plot displaying gene expression values from two spatial experiments. Data points are colored as follows:

  • bluePixels classified as similar (within the fold-change threshold).

  • yellowPixels with greater expression in dataset X than Y.

  • redPixels with greater expression in dataset Y than X.

  • greyPixels with gene expression below the threshold in both experiments.

The plot includes:

  • Solid line:y = x (perfect correlation).

  • Dashed lines:Fold-change similarity thresholds (upper and lower bounds).

Examples

data(speKidney)
##### Rasterize to get pixels at matched spatial locations #####
rastKidney <- SEraster::rasterizeGeneExpression(speKidney,
               assay_name = 'counts', resolution = 0.2, fun = "mean",
               BPPARAM = BiocParallel::MulticoreParam(), square = FALSE)

s <- spatialSimilarity(list(rastKidney$A, rastKidney$C))
linearRegression(s, "Gene")
#> Warning: Removed 14 rows containing missing values or values outside the scale range
#> (`geom_point()`).