elastic_net_gene_selection.utils package

Submodules

elastic_net_gene_selection.utils.data module

elastic_net_gene_selection.utils.data.preprocess(adata_in, nz_thresh=0.05, transform=<ufunc 'arcsinh'>, coding_genes=['2', '3', '5', '7', '11', '13', '17', '19', '23', '29', '31', '37', '41', '43', '47', '53', '59', '61', '67', '71', '73', '79', '83', '89', '97'])

elastic_net_gene_selection.utils.data.split(adata, test_size=0.2, random_state=0)

Split anndata into train/test and return a dict of the integer indices (from the original anndata) in each split and a dict of each split anndata

elastic_net_gene_selection.utils.data.subset(adata, days=['0'])

elastic_net_gene_selection.utils.data.tidy(arr)

Take a numpy ndarray and turn it into a tidy dataframe.

elastic_net_gene_selection.utils.genes module

elastic_net_gene_selection.utils.genes.filter_out_unpenalized_genes(beta, unpenalized_genes, all_genes)

elastic_net_gene_selection.utils.genes.get_gene_set(df, num_genes=25)

elastic_net_gene_selection.utils.genes.get_thresh_lambda_df(boot_results, gene_names=[], thresholds=array([0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1, 0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5, 0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6, 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.7, 0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.8, 0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.9, 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, 0.99, 1. ]), lambdas=array([10., 9.32603347, 8.69749003, 8.11130831, 7.56463328, 7.05480231, 6.57933225, 6.13590727, 5.72236766, 5.33669923, 4.97702356, 4.64158883, 4.32876128, 4.03701726, 3.76493581, 3.51119173, 3.27454916, 3.05385551, 2.84803587, 2.65608778, 2.47707636, 2.3101297, 2.15443469, 2.009233, 1.87381742, 1.7475284, 1.62975083, 1.51991108, 1.41747416, 1.32194115, 1.23284674, 1.149757, 1.07226722, 1., 0.93260335, 0.869749, 0.81113083, 0.75646333, 0.70548023, 0.65793322, 0.61359073, 0.57223677, 0.53366992, 0.49770236, 0.46415888, 0.43287613, 0.40370173, 0.37649358, 0.35111917, 0.32745492, 0.30538555, 0.28480359, 0.26560878, 0.24770764, 0.23101297, 0.21544347, 0.2009233, 0.18738174, 0.17475284, 0.16297508, 0.15199111, 0.14174742, 0.13219411, 0.12328467, 0.1149757, 0.10722672, 0.1, 0.09326033, 0.0869749, 0.08111308, 0.07564633, 0.07054802, 0.06579332, 0.06135907, 0.05722368, 0.05336699, 0.04977024, 0.04641589, 0.04328761, 0.04037017, 0.03764936, 0.03511192, 0.03274549, 0.03053856, 0.02848036, 0.02656088, 0.02477076, 0.0231013, 0.02154435, 0.02009233, 0.01873817, 0.01747528, 0.01629751, 0.01519911, 0.01417474, 0.01321941, 0.01232847, 0.01149757, 0.01072267, 0.01 ]))

get names of selected genes for each combination of thresholds and lambdas. returns a df of results, with the list of gene names merged into one string for eachthreshold/lambda combo.

TODO: make this work (efficiently) with unpenalized genes

elastic_net_gene_selection.utils.plots module

elastic_net_gene_selection.utils.plots.hub_persistence_plot(adata, boot_results, downsample_backgound=1000, width=400, height=400)

elastic_net_gene_selection.utils.plots.thresh_lambda_plot(df)

Module contents

Utilities package for elastic_net_gene_selection.