GMaciag/TENET

TENET is a tool for reconstructing gene regulatory networks from pseudo-time ordered single-cell transcriptomic data.

TENET

A tool for reconstructing Transfer Entropy-based causal gene NETwork from pseudo-time ordered single cell transcriptomic data

Citation

Nucleic Acids Research, gkaa1014, https://doi.org/10.1093/nar/gkaa1014

Dependency

python3
openmpi (>4.0)
JPype

1. Run TENET using expression data in a csv file and pseudotime result in a text file

Usage

./TENET [expression_file_name] [number_of_threads] [trajectory_file_name] [cell_select_file_name] [history_length]

example

./TENET expression_data.csv 10 trajectory.txt cell_select.txt 1

Input

(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) number_of_threads - You can use this multi-threads option. This will take lots of memory depending on the squared number of genes * the number of cells. If the program fail, you need to reduce this.

(3) trajectory_file - a text file of pseudotime data with N time points in the same order as the N cells of the expression file.

0.098
0.040
0.023
.
.
.
0.565

(4) cell_select_file - a text file of cell selection data with N Boolean (1 for select and 0 for non-select) data in the same order as the N cells of the expression file.

1
1
0
.
.
.
1

(5) 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

2. Run TENET with hdf5 file including PAGA pseudotime result

Usage

./TENET4PAGAhdf5 [hdf5_file_name] [number_of_threads] [history_length]

example

./TENET4PAGAhdf5 Data.Tuck/Tuck_PAGA510genes.h5ad 10 1

3. Run TENET from TF to target using expression data in a csv file and pseudotime result in a text file

Usage

    ./TENET_TF [expression_file_name] [number_of_threads] [trajectory_file_name] [cell_select_file_name] [history_length] [species]

example

    ./TENET_TF expression_data.csv 10 trajectory.txt cell_select.txt 1 mouse

Input

(6) species - [human/mouse/rat]

Output

    TE_result_matrix.txt

4. Downstream analysis

(1) Reconstructing GRN

Usage

python makeGRN.py [cutoff for FDR]
python makeGRNsameNumberOfLinks.py [number of links]
python makeGRNbyTF.py [species] [cutoff for FDR]
python makeGRNbyTFsameNumberOfLinks.py [species] [number of links]
** Note that "TE_result_matrix.txt" should be in the same folder.

Example

python makeGRN.py 0.01
python makeGRNsameNumberOfLinks.py 1000
python makeGRNbyTF.py human 0.01
python makeGRNbyTFsameNumberOfLinks.py human 1000

Output file

TE_result_matrix.fdr0.01.sif
TE_result_matrix.NumberOfLinks1000.sif
TE_result_matrix.byGRN.fdr0.01.sif
TE_result_matrix.byGRN.NumberOflinks1000.sif

Parameter

[cutoff for fdr] - A cutoff value for FDR by z-test
[number of links] - The number of links of the GRN
[species] - User can choose human or mouse

(2) Trimming indirect edges

Usage

python trim_indirect.py [name of GRN] [cutoff]

Example

python trim_indirect.py TE_result_matrix.fdr0.01.sif 0

Output file

TE_result_matrix.fdr0.01.trimIndirect0.0.sif

Parameter

[cutoff] - A cutoff value for trimming indirect edges. Recommended range is -0.1 to 0.1

(3) Counting out-degree of a given GRN

Usage

python countOutdegree.py [name of GRN]

Example

python countOutdegree.py TE_result_matrix.fdr0.01.sif

Output file

TE_result_matrix.fdr0.01.sif.outdegree.txt