spacr.toxo¶
Module Contents¶
- spacr.toxo.custom_volcano_plot(data_path, metadata_path, metadata_column='tagm_location', point_size=50, figsize=20, threshold=0, save_path=None, x_lim=[-0.5, 0.5], y_lims=[[0, 6], [9, 20]])[source]¶
- spacr.toxo.go_term_enrichment_by_column(significant_df, metadata_path, go_term_columns=['Computed GO Processes', 'Curated GO Components', 'Curated GO Functions', 'Curated GO Processes'])[source]¶
Perform GO term enrichment analysis for each GO term column and generate plots.
Parameters: - significant_df: DataFrame containing the significant genes from the screen. - metadata_path: Path to the metadata file containing GO terms. - go_term_columns: List of columns in the metadata corresponding to GO terms.
For each GO term column, this function will: - Split the GO terms by semicolons. - Count the occurrences of GO terms in the hits and in the background. - Perform Fisher’s exact test for enrichment. - Plot the enrichment score vs -log10(p-value).
- spacr.toxo.plot_gene_phenotypes(data, gene_list, x_column='Gene ID', data_column='T.gondii GT1 CRISPR Phenotype - Mean Phenotype', error_column='T.gondii GT1 CRISPR Phenotype - Standard Error', save_path=None)[source]¶
Plot a line graph for the mean phenotype with standard error shading and highlighted genes.
- Parameters:
data (pd.DataFrame) – The input DataFrame containing gene data.
gene_list (list) – A list of gene names to highlight on the plot.
- spacr.toxo.plot_gene_heatmaps(data, gene_list, columns, x_column='Gene ID', normalize=False, save_path=None)[source]¶
Generate a teal-to-white heatmap with the specified columns and genes.
- Parameters:
data (pd.DataFrame) – The input DataFrame containing gene data.
gene_list (list) – A list of genes to include in the heatmap.
columns (list) – A list of column names to visualize as heatmaps.
normalize (bool) – If True, normalize the values for each gene between 0 and 1.
save_path (str) – Optional. If provided, the plot will be saved to this path.