Package index • jazzPanda

All functions

compute_permp(): Calculate a p-value for correlation with permutation.

create_genesets(): Convert the coordinates of set of genes into vectors.

.check_binning(): helper function to check the input of binning

.check_valid_input(): helper function to check the inputs passed to marker detection function

.compute_observation(): Compute observation statistic for permutation framework

.compute_permutation(): Compute permutation statistics for permutation framework

.convert_data(): Convert SingleCellExperiment/SpatialExperiment/SpatialFeatureExperiment objects to list object for jazzPanda.

.create_cor_mg_result(): Create a marker gene result object for correlation approach

.create_lm_mg_result(): Create a marker gene result object for linear modelling approach

.get_cluster_vectors(): Create spatial vectors for clusters

.get_gene_vectors_cm(): Create spatial vectors for genes from count matrix and cell coordinates

.get_gene_vectors_tr(): Create spatial vectors for genes from transcript coordinates

.get_lasso_coef(): help function to get lasso coefficient for every cluster for a given model

get_cor(): Get observed correlation cor_mg_result

get_full_mg(): Get full lasso result from glm_mg_result

get_perm_adjp(): Get permutation adjusted p value from cor_mg_result

get_perm_p(): Get permutation p value from cor_mg_result

get_top_mg(): Get top lasso result from glm_mg_result

get_vectors(): Vectorise the spatial coordinates

jazzPanda-package jazzPanda: jazzPanda: A hybrid approach to find spatially relevant marker genes in image-based spatial transcriptomics data

lasso_markers(): Find marker genes with spatial coordinates

rep1_clusters: Rep1 selected cells

rep1_neg: Rep1 negative control genes within the selected region.

rep1_sub: A small section of Xenium human breast cancer rep1.

rep2_clusters: Rep2 selected cells

rep2_neg: Rep2 negative control genes within the selected region.

rep2_sub: A small section of Xenium human breast cancer rep2.