API

coloc module

Focused on differential co-localization

OvsE_coloc_test	Observed vs Expected log2 ratios filtered by p-value obtained from comparing them against a background distribution
OvsE_coloc_test_adjPval	Observed vs Expected log2 ratios filtered by adjusted p-value from a per-pair background distribution.
cellCatContained	Check if a cell group (niche/type/category) is contained in a cell type pair
colocNW	(Differential) co-localization network
compute_sc_spatial_knn_coloc_matrix	Compute a kNN-based co-localization probability matrix between cell type pairs.
compute_sc_spatial_radius_coloc_matrix	Compute a radius-based co-localization probability matrix between cell type pairs.
diffColoc_test	Differential co-localization test with table of scores and p-values as output
getColocProbs	Vectorized calculation of cell type pair co-localization probabilities per sample (sum across spots of probabilities of each cell type pair being in the same spot)
reshapeColoc	Transforms matrix obtained with getColocProbs into a matrix of CT pairs x samples CTcoloc=previously obtained colocalisation matrix from getColocprobs complete=list with repeated values (ct1_x_ct2 and ct2_x_ct1)
spatialCTPlot	Plot cell types in spatial data (MERFISH / visium slices)
spatialNichePlot	Plot niches in spatial data (MERFISH / visium slices)
spatialSingleCTPlot	Plot a single cell type proportions in spatial data (MERFISH / visium slices)

comm module

Focused on differential communication

calculate_LR_CT_pair_scores_dir	Get cell communication scores per cell type pair per LR pair by summing that LR pair scores for that cell type pair.
catNW	(Differential) communication network
cellCatContained	Check if a cell group (niche/type/category) is contained in a cell type pair
diffCcommStats	Differential cell communication per LR category (eg: biological process)
getDiffComm	get the differential communication scores for a specific LR category
lr_ctPairScores_perCat_dir	Calculate cell communication scores per ligand category from a database
plotDiffCcommStatsHM	Plot heatmap of differential cell communication statistics
unique	get unique elements in array without re-sorting

tl module

Helpful tools

PIC_BGdoubletsOEratios	Generate distribution of O/E ratios for colocalization probabilities of cell type pairs in randomly generated doublets (background dist) for tests in PIC-seq data
PIC_BGdoubletsOEratios_parallel	Generate one set of O/E ratios for cell type pair co-localization probabilities from randomly paired single cells.
assign_properties	Assign properties to network g to visualize with gravis interactive networks
cellCatContained	Generate boolean element indicating whether the specified element (cell pair) is contained in a list (category)
cells_niche_colors	Make dataframe of cell types with their corresponding niches and colors
compute_network_stats	Compute standard centrality statistics for a (signed) NetworkX graph.
downsample_class	Reduce the number of elements belonging to target_class by a given factor.
getColocFilter	Get filtering object indicating which cell pairs are differentially co-localized
getExpectedColocProbsFromSCs	Compute the expected co-localization probability of each cell type pair by multiplying cell type proportions derived from single-cell (singlets) data.
get_PIC_BG_OEratios_DF	Generate nreps sets of O/E ratios for cell type pair co-localization probabilities by running PIC_BGdoubletsOEratios_parallel() in parallel.
get_pairCatDFdir	Get dataframe of cell pair to niche pair correspondence
get_spatial_radius_BG_OEratios_DF	Optimized version of get_spatial_radius_BG_OEratios_DF using sparse matrix multiplication.
get_spot_ct_props	Get cell type proportions per spot by summing the probabilities of cells of the same kind in each spot
plot_top_stats	Bar plots of the top nodes by betweenness, PageRank, and signed degree centrality.
processCTKRoutput	Remove suffixes '|L' , '|R' and '|TF' from cell - cell communication (CrossTalkeR) tables
pval_filtered_HMdf	Get dataframe for scores heatmap from dataframe obtained from diffColoc_test()
unique	get unique elements in array without re-sorting

niche_stats module

Tools to test and visualize the internal cohesion of each niche relative to its cross-niche connections

community_edge_stats	Per-community positive/negative edge count and weight statistics.
community_edge_stats_df	Flat DataFrame version of community_edge_stats().
community_edge_weight_distributions	Raw edge weight lists per community, split by location and sign.
community_signed_weight_distributions	Combine positive and negative weights into a single signed distribution.
pairwise_ranksums_int_ext	Wilcoxon rank-sum test comparing internal vs external edge weight distributions for each community, with multiple-testing correction.
plot_internal_external_boxplots	Box plots of signed edge weights (internal vs external) per community.

sankey_plot module

Biological process specific sankey plots

plot_custom_sankey

Flat 2D Sankey diagram of ligand-receptor interactions from pyCrossTalkeR results.