`larry._datasets.klein_lab_pp_recipe`¶

Submodules¶

`cell_cycle_genes`([genes_added])
`vscores`(E[, min_mean, nBins, fit_percentile, error_wt])	Calculate v-score (above-Poisson noise statistic) for genes in the input sparse counts matrix
`highly_variable_genes`(adata[, base_ix, ...])	Filter genes by expression level and variability
`remove_cell_cycle_correlated_genes`(adata[, min_corr, ...])	Remove signature-correlated genes from a list of test genes

class larry._datasets.klein_lab_pp_recipe.RunningQuantile(n_bins: int = 50)¶

larry._datasets.klein_lab_pp_recipe.vscores(E, min_mean=0, nBins=50, fit_percentile=0.1, error_wt=1)¶: Calculate v-score (above-Poisson noise statistic) for genes in the input sparse counts matrix Return v-scores and other stats

larry._datasets.klein_lab_pp_recipe.highly_variable_genes(adata, base_ix=[], min_vscore_pctl=85, min_counts=3, min_cells=3, show_vscore_plot=False, sample_name='', return_idx=False)¶: Filter genes by expression level and variability Return list of filtered gene indices

larry._datasets.klein_lab_pp_recipe.remove_cell_cycle_correlated_genes(adata, min_corr=0.1, key_added='use_genes')¶

Remove signature-correlated genes from a list of test genes

E: scipy.sparse.csc_matrix, shape (n_cells, n_genes)

gene_list: numpy array, shape (n_genes,)

exclude_corr_genes_list: list of list(s)

Each sublist is used to build a signature. Test genes correlated with this signature will be removed

test_gene_idx: 1-D numpy array

indices of genes to test for correlation with the gene signatures from exclude_corr_genes_list

min_corr: float (default=0.1)

Test genes with a Pearson correlation of min_corr or higher with any of the gene sets from exclude_corr_genes_list will be excluded

numpy array of gene indices (subset of test_gene_idx) that are not correlated with any of the gene signatures