besca.pp Package

Functions

filter(adata[, max_genes, min_genes, ...])	Filter cell outliers based on counts, numbers of genes expressed, number of cells expressing a gene and mitochondrial gene content.
filter_gene_list(adata, filepath[, use_raw, ...])	Function to remove all genes specified in a gene list read from file.
frac_pos(adata[, threshold])	Calculate the fraction of cells positive for expression of a gene.
frac_reads(adata)	Cacluate the fraction of reads being attributed to a specific gene.
mean_expr(adata)	Calculate the mean expression of a gene.
top_expressed_genes(adata[, top_n])	Give out the genes most frequently expressed in cells.
fraction_counts(adata[, species, name, ...])	Function to calculate fraction of counts per cell from a gene list.
top_counts_genes(adata[, top_n])	Give out the genes that contribute the largest fraction to the total UMI counts.
normalize_geometric(adata)	Perform geometric normalization on CITEseq data.
valOutlier(adata[, nmads, rlib_loc])	Estimates and returns the thresholds to use for gene/cell filtering based on outliers calculated from the deviation to the median QCs.
scTransform(adata[, hvg, n_genes, rlib_loc])	Function to call scTransform normalization or HVG selection from Python.

besca.pl Package

Functions

kp_genes(adata[, threshold, min_genes, ax, ...])	visualize the minimum gene per cell threshold.
kp_counts(adata[, min_counts, ax, figsize])	visualize the minimum UMI counts per cell threshold.
kp_cells(adata[, threshold, min_cells, ax, ...])	visualize the minimum number of cells expressing a gene threshold.
max_counts(adata[, max_counts, ax, figsize])	visualize maximum UMI counts per cell threshold.
max_genes(adata[, max_genes, ax, figsize])	visualize maximum number of genes per cell threshold.
max_mito(adata[, max_mito, annotation_type, ...])	visualize maximum mitochondrial gene percentage threshold.
dropouts(adata[, ax, bins, figsize])	Plot number of dropouts.
detected_genes(adata[, ax, bins, figsize])	Plot number of detected genes.
library_size(adata[, ax, bins, figsize])	Plot library size.
librarysize_overview(adata[, bins, figsize])	Generates overview figure of libarysize, dropouts and detected genes.
transcript_capture_efficiency(adata[, ax, ...])	Plot total gene counts vs detection probability.
top_genes_counts(adata[, top_n, ax, figsize])	plot top n genes that contribute to fraction of counts per cell
gene_expr_split(adata, genes[, ...])	visualize gene expression of two groups as a split violin plot
gene_expr_split_stacked(adata, genes, ...[, ...])	Stacked violin plot for visualization of genes expression.
box_per_ind(plotdata, y_axis, x_axis[, ...])	plot boxplot with values per individual.
stacked_split_violin(tidy_data, x_axis, ...)	plot stacked split violin plots.
celllabel_quant_boxplot(adata, ...[, ...])	generate a box and whisker plot with overlayed swarm plot of celltype abundances
celllabel_quant_stackedbar(adata, ...[, ...])	Generate a stacked bar plot of the percentage of labelcounts within each AnnData subset
dot_heatmap(adata, genes[, group_by, ...])	Generate a dot plot, filled with heatmap of individuals cells gene expression.
dot_heatmap_split(adata, genes, split_by[, ...])	Generate a dot plot, filled with heatmap of individuals cells gene expression to compare two conditions.
dot_heatmap_split_greyscale(adata, genes, ...)	Generate a dot plot, filled with heatmap of individuals cells gene expression to compare two conditions (greyscale).
update_qualitative_palette(adata, palette[, ...])	Update adata object such that the umap will adhere to the palette provided.
nomenclature_network(config_file[, ...])	Plot a nomenclature network based on annotation config file.
riverplot_2categories(adata, categories[, ...])	Generate a riverplot/sanker diagram between two categories.
flex_dotplot(df, X, Y, HUE, SIZE, title[, ...])	Generate a dot plot showing average expression and fraction positive cells

besca.tl Package

Functions

count_occurrence(adata[, count_variable, ...])	Generate dataframe containing the label counts/percentages of a specific column in adata.obs
count_occurrence_subset(adata, subset_variable)	count occurrence of a label in adata.obs after subseting adata object
count_occurrence_subset_conditions(adata, ...)	count occurrence of a label for each condition in adata.obs after subseting adata object
annotate_cells_clustering(adata, ...[, ...])	Function to add annotation to adata.obs based on clustering This function replaces the original cluster labels located in the column clustering_label with the new values specified in the list new_cluster_lables.
report(adata_pred, celltype, method, ...[, ...])	reports basic metrics, produces confusion matrices and plots umap of prediction
plot_confusion_matrix(y_true, y_pred, ...[, ...])	plots confusion matrices

besca.tl.bcor Package

Functions

batch_correct(adata, batch_to_correct)	function to perform batch correction
postprocess_mnnpy(adata, bdata)	postprocessing to generate a newly functional AnnData object

besca.tl.dge Package

Functions

perform_dge(adata, design_matrix, ...[, ...])	Perform differential gene expression between two conditions over many adata subsets.
plot_interactive_volcano(top_table_path, outdir)	plot an interactive volcano plot based on toptable file.
get_de(adata, mygroup[, demethod, topnr, ...])	Get a table of significant DE genes at certain cutoffs Based on an AnnData object and an annotation category (e.g.

besca.tl.rc Package

Functions

recluster(adata, celltype[, celltype_label, ...])	Perform subclustering on specific celltype to identify subclusters.
annotate_new_cellnames(adata, ...[, ...])	annotate new cellnames to each of the subclusters identified by running recluster.

besca.Import Package

Functions

read_mtx(filepath[, annotation, use_genes, ...])	Read matrix.mtx, genes.tsv, barcodes.tsv to AnnData object. By specifiying an input folder this function reads the contained matrix.mtx, genes.tsv and barcodes.tsv files to an AnnData object. In case annotation = True it also adds the annotation contained in metadata.tsv to the object. :param filepath: filepath as string to the directory containg the matrix.mtx, genes.tsv, barcodes.tsv and if applicable metadata.tsv :type filepath: str :param annotation: boolian identifier if an annotation file is also located in the folder and should be added to the AnnData object :type annotation: bool (default = True) :param use_genes: either SYMBOL or ENSEMBL. Other genenames are not yet supported. :type use_genes: str :param species: string specifying the species, only needs to be used when no Gene Symbols are supplied and you only have the ENSEMBLE gene ids to perform a lookup. :type species: str | default = 'human' :param citeseq: string indicating if only gene expression values (gex_only) or only protein expression values ('citeseq_only') or everything is read if None is specified :type citeseq: 'gex_only' or 'citeseq_only' or False or None | default = None.
add_cell_labeling(adata, filepath[, label])	add a labeling written out in the FAIR formating to adata.obs
assert_adata(adata[, attempFix])	Asserts that an adata object is containing information needed for the besca pipeline to run and export information.

besca.export Package

Functions

X_to_mtx(adata[, outpath, write_metadata, ...])	export adata object to mtx format (matrix.mtx, genes.tsv, barcodes.tsv)
raw_to_mtx(adata[, outpath, write_metadata, ...])	export adata.raw to .mtx (matrix.mtx, genes.tsv, barcodes, tsv)
clustering(adata[, outpath, export_average, ...])	export mapping of cells to clusters to .tsv file
write_labeling_to_files(adata[, outpath, ...])	export mapping of cells to specified label to .tsv file
labeling_info([outpath, description, ...])	write out labeling info for uploading to database
analysis_metadata(adata[, outpath, ...])	export plotting coordinates to analysis_metadata.tsv
generate_gep(adata[, filename, column, ...])	Generate Gene Expression Profile (GEP) from scRNA-seq annotations
ranked_genes(adata[, type, outpath, ...])	export marker genes for each cluster to .gct file
pseudobulk(adata[, outpath, column, label, ...])	export pseudobulk profiles of cells to .gct files

besca.st Package

Functions

read_matrix(root_path[, citeseq, ...])	Read matrix file as expected for the standard workflow.
filtering_cells_genes_min(adata, ...)
filtering_mito_genes_max(adata, ...)
export_cp10k(adata, basepath)	Export raw cp10k to FAIR format for loading into database
export_regressedOut(adata, basepath)	Export regressedOut to FAIR format for loading into database
export_clustering(adata, basepath, method)	Export cluster to cell mapping to FAIR format for loading into database
export_metadata(adata, basepath[, n_pcs, ...])	Export metadata in FAIR format for loading into database
export_rank(adata, basepath[, type, ...])	Export ranked genes to FAIR format for loading into database
export_celltype(adata, basepath)	Export celltype annotation to cell mapping in FAIR format for loading into database
additional_labeling(adata, labeling_to_use, ...)	Standard Workflow function to export an additional labeling besides louvain to FAIR format.
celltype_labeling(adata, labeling_author, ...)	Standard Workflow function to export an additional labeling besides louvain to FAIR format.