A tool intergrating Vetra a trajectory inference tool and TENET a GRN reconstruction tool See indepent version:
openmpi
JPype
rpy2
princurve
import VeTenet as vt
ex1 = vt.VeTenet("embedding.txt", "delta_embedding.txt")
ex1.vetra(deltaThreshold=12, WCCsizeCutoff=5, clusternumber=3)
ex1.run_tenet_tf(expression="chroman_exp_filtered.csv", thread= 15, history_len = 1, species = 'mouse', bulk_run=True)
ex1.makeGRN_tf(method= "links", threshold = 1000, bulk_run=True)
(1) expression_file - a csv file with N cells in the rows and M genes in the columns (same format with wishbone pseudotime package).
GENE_1 GENE_2 GENE_3 ... GENE_M
CELL_1
CELL_2
CELL_3
.
.
.
CELL_N
(2) embedding_file - a txt file with N cells in rows and 2D embedding coordinates in columns. The 2D embedding can be results of PCA, tSNE, or UMAP.
-1.662885950152473424e-02 -1.019793607352199594e-01
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.
.
2.932140719083742297e-02 2.628113801391315230e-01
(3) delta_embedding_file - a txt file with N cells in rows and 2D velocity vectorss in columns. Users can run "velocyto"(http://velocyto.org/) or "scVelo" (https://github.com/theislab/scvelo) to get delta_embedding.
-6.344244118975589153e+00 1.268898329669120084e+00
.
.
.
-3.511296625112249714e+00 -3.950214438779450082e-02
(4) history_length - the length of history. In the benchmark data TENET provides best result when the length of history set to 1.
TE_result_matrix.txt - TEij, M genes x M genes matrix representing the causal relationship from GENEi to GENEj.
TE GENE_1 GENE_2 GENE_3 ... GENE_M
GENE_1 0 0.05 0.02 ... 0.004
GENE_2 0.01 0 0.04 ... 0.12
GENE_3 0.003 0.003 0 ... 0.001
.
.
.
GENE_M 0.34 0.012 0.032 ... 0
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