TENET

A tool intergrates trajectory inference and reconstruction of GRN by calculating transfer entropy(TF) See indepent version:

Citation

Dependency

openmpi
JPype
rpy2
princurve

1. Run VeTenet using expression data in a csv file and RNA velocity files in txt format

Initialize an example

import VeTenet as vt
ex1 = vt.VeTenet("embedding.txt", "delta_embedding.txt")

Execute trajectory inference

ex1.vetra(deltaThreshold=12, WCCsizeCutoff=5, clusternumber=3)

Run TENET for all trajectories

ex1.run_tenet_tf(expression="chroman_exp_filtered.csv", thread= 15, history_len = 1, species = 'mouse', bulk_run=True)

Make GRNs for all trajectories

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
.
.
.
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.

Output

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