Targets for which the TF is within its top 50 regulatorsĪ distinction is made between modules which contain targets that are being activated and genes that are being repressed.Targets for which the TF is within its top 10 regulators.Targets for which the TF is within its top 5 regulators.In this way we will create three more gene-sets: Then, these targets can be assigned back to each TF to form the TF-modules. The alternative way to create the TF-modules is to select the best regulators for each gene (this is actually how GENIE3 internally works). Top 50 targets (targets with highest weight).The second method is to select the top targets for a given TF: Targets with importance > the 90th percentile.Targets with importance > the 75th percentile.Targets with importance > the 50th percentile.The first method to create the TF-modules is to select the best targets for each transcription factor:

Regulons are derived from adjacencies based on three methods. The scRNA-Seq data is downloaded from GEO: and loaded into memory:Īdjacencies = grnboost2 ( ex_matrix, tf_names = tf_names, verbose = True ) join ( DATA_FOLDER, 'regulons.p' ) MOTIFS_FNAME = os. join ( RESOURCES_FOLDER, 'GSE60361_C1-3005-Expression.txt' ) REGULONS_FNAME = os. join ( RESOURCES_FOLDER, 'mm_tfs.txt' ) SC_EXP_FNAME = os. join ( RESOURCES_FOLDER, 'motifs-v9-nr.mgi-m0.001-o0.0.tbl' ) MM_TFS_FNAME = os. join ( DATABASE_FOLDER, 'mm9-*.mc9nr.genes_vs_' ) MOTIF_ANNOTATIONS_FNAME = os. Import os import glob import pickle import pandas as pd import numpy as np from dask.diagnostics import ProgressBar from arboreto.utils import load_tf_names from arboreto.algo import grnboost2 from ctxcore.rnkdb import FeatherRankingDatabase as RankingDatabase from pyscenic.utils import modules_from_adjacencies, load_motifs from pyscenic.prune import prune2df, df2regulons from cell import aucell import seaborn as sns DATA_FOLDER = '~/tmp' RESOURCES_FOLDER = '~/resources' DATABASE_FOLDER = '~/databases/' SCHEDULER = '123.122.8.24:8786' DATABASES_GLOB = os.

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