Package: CrossExpression 1.0.0

Ameer Sarwar

CrossExpression: Cross-Expression Analysis of Spatial Transcriptomics Data

Analyzes spatial transcriptomic data using cells-by-genes and cell location matrices to find gene pairs that coordinate their expression between spatially adjacent cells. It enables quantitative analysis and graphical assessment of these cross-expression patterns. See Sarwar et al. (2025) <doi:10.1101/2024.09.17.613579> and <https://github.com/gillislab/CrossExpression/> for more details.

Authors:Ameer Sarwar [aut, cre], Sarah Choi [ctb], Leon French [ctb], Jesse Gillis [aut]

CrossExpression_1.0.0.tar.gz
CrossExpression_1.0.0.zip(r-4.7-any)CrossExpression_1.0.0.zip(r-4.6-any)
CrossExpression_1.0.0.tar.gz(r-4.7-any)CrossExpression_1.0.0.tar.gz(r-4.6-any)
CrossExpression_1.0.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
CrossExpression/json (API)

# Install 'CrossExpression' in R:
install.packages('CrossExpression', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))
Datasets:

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.00 score 1 scripts 250 downloads 8 exports 35 dependencies

Last updated from:7450c48c04. Checks:6 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK135
source / vignettesOK205
linux-releaseOK139
windows-develOK78
windows-releaseOK88
wasm-releaseOK112

Exports:bullseye_plotbullseye_scorescross_expressioncross_expression_correlationrotate_coordinatessmooth_cellsspatial_enrichmenttissue_expression_plot

Dependencies:clicpp11dplyrfarvergenericsggplot2gluegtableisobandlabelinglatticelifecyclemagrittrMatrixpillarpkgconfigR6RANNRColorBrewerRcppRcppArmadilloRcppParallelRfastrlangS7scalesstringistringrtibbletidyselectutf8vctrsviridisLitewithrzigg

Readme and manuals

Help Manual

Help pageTopics
Outputs a circular bullseye plot for a gene pair. The central circle is gene B in cells expressing gene A. Rings indicate neighbors with gene B, where the first ring is the first neighbor.bullseye_plot
Calculates bullseye statistics for ALL gene pairs. Counts the number of cells with gene B in those with gene A And in their neighbors Neighbor scores are cumulative and normalized by window size.bullseye_scores
Computes Pearson's correlation between pairs of columns. If one matrix is provided, the output is the pairwise correlations between its columns. If two matrices are provided, the output is the pairwise correlations between their columns.correlation
Computes cross-expression and co-expression p-values between all gene pairs.cross_expression
Computes gene-gene correlations between cross-expressing cell-neighbor pairs. Cell and neighbor masks are used to consider mutually exclusive expression per gene pair.cross_expression_correlation
Example gene expression matrixexpression
Calculates the number of elements common between columns of two matrices. This function performs a simple dot product when binarize = FALSE.get_cooccurrence_stats
Example cell location matrixlocations
This function takes x and y coordinates and rotates them counterclockwise by the specified number of degrees, and mean centers the points.rotate_coordinates
Smooths cells' gene expression by averaging its expression by the nearest neighbors. Optionally computes genes by genes Pearson's correlation matrix between cells by genes and neighbors by genes matrices.smooth_cells
Determines whether the supplied genes show spatial enrichment in cross-expression. Spatial enrichment can be interpreted as delineating anatomical boundaries.spatial_enrichment
Plots gene expression and cross-expression on tissue by coloring cells.tissue_expression_plot