A tool for reconstructing Transfer Entropy-based causal gene NETwork from pseudo-time ordered single cell transcriptomic and epigenetic data
python3
openmpi (>4.0)
JPype
Nucleic Acids Research, gkaa1014, https://doi.org/10.1093/nar/gkaa1014
https://github.com/neocaleb/TENET
1. Run TENETPLUS from TF to target and peak source once using expression data in a csv file and pseudotime result in a text file after make result matrix split matrix
./TENET_Plus [merged_expression_file_name] [number_of_threads] [trajectory_file_name] [cell_select_file_name] [history_length] [species] [options]
options : 0: TENET_TF(Only RNA), 1: TENET_Plus(RNA + ATAC) all matrix, 2: TENET_Plus only rowTF colGN, 3: TENET_Plus only rowTF colPK, 4: TENET_Plus only rowPeak(cis-peaksource)
./TENET_Plus merged_expression_data.csv 10 trajectory.txt cell_select.txt 1 human 1
TE_result_matrix_rowTF_colPK.txt
TE_result_matrix_rowTF_colGN.txt
TE_result_matrix_rowPeak_colGN.txt
TE_result_matrix.txt
python make_GRN_new.py [AB matrix] [cut-off]
python make_GRN_new.py TE_result_matrix_rowTF_colGN.txt 0.01
python make_GRN_new.py TE_result_matrix_rowTF_colPK.txt 0.01
TE_result_matrix_rowTF_colPK.fdr0.01.sif
TE_result_matrix_rowTF_colGN.fdr0.01.sif
TE_result_matrix_rowPeak_colGN.fdr0.01.sif
python trim_indirect.py [AB matrix] [cutoff]
python trim_indirect.py TE_result_matrix_rowTF_colGN.fdr0.01.sif -0.01
TE_result_matrix_rowTF_colGN.fdr0.01.trimIndirect-0.01.sif
[cutoff] - A cutoff value for trimming indirect edges. Recommended range is -0.1 to 0.1
python countOutdegree.py [name of GRN]
python countOutdegree.py TE_result_matrix_rowTF_colPK.fdr0.01.sif
python countOutdegree.py TE_result_matrix_rowTF_colGN.fdr0.01.trimIndirect-0.01.sif
TE_result_matrix_rowTF_colPK.fdr0.01.sif.outdegree.txt
TE_result_matrix_rowTF_colGN.fdr0.01.trimIndirect-0.01.sif.outdegree.txt
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent