Usage

Import the sc-toolbox API as follows:

import sc_toolbox as sct

You can then access the respective modules like:

sct.pl.cool_fancy_plot()

Preprocessing

Tools

tools.generate_expression_table	Generates a table of cells by genes of expression values as a Pandas DataFrame.
tools.relative_frequencies	Calculates the relative frequencies of conditions grouped by an observation.
tools.relative_frequency_per_cluster	Calculates relative frequencies per cluster
tools.correlate_to_signature	Correlations Score (based on cell type signature (logFC)) - alternative to sc.tl.score
tools.remove_outliers	Remove outlying cells based on UMAP embeddings with DBScan (density based clustering).
tools.add_percentages	Add columns to existing diffxpy table specifying percentage of expressing cells.
tools.ranksums_between_groups	Perform Wilcoxon Rank-sum test between two groups.
tools.generate_count_object	@Meshal what is this really supposed to do?
tools.tidy_de_table	Sorts diffxpy de table and adds percentages of expression per group
tools.correlate_means_to_gene	Calculate gene to gene correlation based on a mean expression table
tools.extended_marker_table	Generates an extended marker table with cell types and percentages of expressed cell types per cluster.
tools.generate_pseudobulk	Generates a pseudobulk for a given key of groups in the AnnData object.
tools.automated_marker_annotation	Calculates a marker gene overlap based on pre-existing annotations.
tools.de_res_to_anndata	Add a tabular differential expression result to AnnData as if it was produced by scanpy.tl.rank_genes_groups.

Plots

plot.Colormaps(value)	Useful Colormaps for e.g.
plot.custom_plot_size(width, height, dpi)	Create a custom axis object of desired sizes.
plot.standard_lineplot(data, order, xlabel, ...)	Draws a standard line plot based on Seaborn's lmplot.
plot.average_expression(gene_expression, ...)	Draw a line plot showing the gene expression over time.
plot.average_expression_per_cluster(...[, ...])	Plots gene expression over time split by cluster identity.
plot.average_expression_split_cluster(...[, ...])	Plot average gene expression as line plots for multiple clusters at once.
plot.average_expression_per_cell(...[, ...])	Plots the average gene expression as a line plot per cell.
plot.gene_expression_dpt_ordered(data, ...)	Plot smoothed expression of all cells ordered by pseudo time.
plot.relative_frequencies_boxplots(...[, ...])	Plots the relative frequencies as split boxplots.
plot.split_boxplot(table, order, xlabel, ylabel)	Draws a boxsplit split by hue.
plot.marker_dendrogram(marker_table[, ...])	Plots a dendogram of used marker genes.
plot.volcano_plot(table[, fdr_thresh, ...])	Scatter plot of differential gene expression results generated by diffxpy
plot.cluster_composition_stacked_barplot(...)	Plot relative frequencies as a stacked barplot.
plot.gene_boxplot(table, palette[, xlabel, ...])	Plot gene values as split boxplots.
plot.colors_overview(colors[, ncols, ...])	Draw an overview plot of all used colors.
plot.relative_frequencies_lineplot(...[, ...])	Plot relative frequencies as a line plot.
plot.annotated_cell_type_umap(adata, ...[, ...])	Plots a UMAP which is colored by the primary_color, but also draws all labels on top of all clusters.
plot.genotype_vs_genotype_umaps(adata, ...)	Plots a two UMAPs of genotypes next to each other displaying only the colors of the second UMAP.