A R/MATLAB package to perform virtual knockout experiments on single-cell gene regulatory networks. scTenifoldKnk is a machine learning workflow that performs virtual knockout experiments using single-cell RNA sequencing (scRNAseq) data from wild-type (WT) control samples as input. Constructs a single-cell gene regulatory network (scGRN) and knocks out a target gene from the adjacency matrix of the WT scGRN by setting the gene’s outdegree edges to zero. scTenifoldKnk then compares the knocked out scGRN with the WT scGRN to identify differentially regulated genes, called virtual-knockout perturbed genes, which are used to assess the impact of the gene knockout and reveal the gene’s function in the analyzed cells.
This package is under active development, you can install scTenifoldKnk, using the following command:
library(remotes) install_github('cailab-tamu/scTenifoldKnk') library(scTenifoldKnk)
|Code| Function | |:-|:-| |scTenifoldKnk|Perform virtual knockout experiments on single-cell gene regulatory networks|
The required input for scTenifoldKnk is an expression matrix with genes in the rows and cells (barcodes) in the columns. Data is expected to be not normalized.
The running time of scTenifoldKnk is largely dependent on how long it takes to construct scGRNs from subsampled expression matrices. Time increases proportional to the number of cells and genes in the dataset used as input. Below is a table of running times under different scenarios:
| Number of Cells | Number of Genes | Running Time | |-----------------|-----------------|--------------| | 300 | 1000 | 3.45 min | | 1000 | 1000 | 4.25 min | | 1000 | 5000 | 171.88 min (2 h 51.6 min) | | 2500 | 5000 | 175.29 min (2 h 55.3 min) | | 5000 | 5000 | 188.88 min (3 h 8.9 min) | | 5000 | 7500 | 189.51 min (3 h 9.5 min) | | 7500 | 5000 | 615.45 min (10 h 15.5 min) | | 7500 | 7500 | 616.12 min (10 h 16.1 min) |
The output of scTenifoldKnk is a list with 3 slots as follows: * tensorNetworks: The computed weight-averaged denoised gene regulatory networks after CANDECOMP/PARAFAC (CP) tensor decomposition. It includes two slots with: * X: The constructed network for the X sample. * Y: The constructed network for the Y sample. * manifoldAlignment: The generated low-dimensional features result of the non-linear manifold alignment. It is a data frame with 2 times the number of genes in the rows and d (default= 2) dimensions in the columns * diffRegulation: The results of the differential regulation analysis. It is a data frame with 6 columns as follows: * gene: A character vector with the gene id identified from the manifoldAlignment output. * distance: A numeric vector of the Euclidean distance computed between the coordinates of the same gene in both conditions. * Z: A numeric vector of the Z-scores computed after Box-Cox power transformation. * FC: A numeric vector of the FC computed with respect to the expectation. * p.value: A numeric vector of the p-values associated to the fold-changes, probabilities are asigned as P[X > x] using the Chi-square distribution with one degree of freedom. * p.adj: A numeric vector of adjusted p-values using Benjamini & Hochberg (1995) FDR correction.
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