scTenifoldKnk is an R package for performing virtual knockout experiments on single-cell gene regulatory networks (scGRNs). It uses single-cell RNA-seq (scRNA-seq) data from wild-type (WT) control samples to construct a scGRN, then simulates a gene knockout by zeroing the target gene's outdegree edges in the adjacency matrix. The resulting knocked-out scGRN is compared with the WT scGRN to identify differentially regulated genes, or virtual-knockout perturbed genes, which reveal the functional impact of the knocked-out gene in the analyzed cell population.

Implementations in other languages are also available:

• Python: scTenifoldpy
• MATLAB: scGEAToolbox

scTenifoldKnk is available on CRAN:

install.packages("scTenifoldKnk")

To install the development version from GitHub:

# install.packages("remotes")
remotes::install_github("cailab-tamu/scTenifoldKnk")

The scTenifoldKnk() function orchestrates a virtual knockout pipeline built on top of the scTenifoldNet framework. Each step reports progress to the console via the cli package.

Individual functions are exported and fully documented, allowing users to run or modify any step independently.

The required input is a raw counts matrix with genes as rows and cells (barcodes) as columns. Data should be unnormalized when qc = TRUE (the default). The modular design allows users to substitute custom preprocessing at any step.

scTenifoldKnk() returns a list with three elements:

• tensorNetworks — Weight-averaged denoised gene regulatory networks after CP tensor decomposition, containing:
  • WT: The network for the wild-type condition (sparse matrix of class dgCMatrix).
  • KO: The network for the knocked-out condition (sparse matrix of class dgCMatrix).
• manifoldAlignment — A data frame of low-dimensional features from the non-linear manifold alignment, with 2 × n genes rows and d columns (default d = 2).
• diffRegulation — A data frame with six columns:
  • gene: Gene identifier.
  • distance: Euclidean distance between the gene's coordinates in the two conditions.
  • Z: Z-score after Box-Cox power transformation.
  • FC: Fold change with respect to the expectation.
  • p.value: P-value from the chi-square distribution with one degree of freedom.
  • p.adj: Adjusted p-value (Benjamini & Hochberg FDR correction).

Running time is largely determined by the network construction step and scales with the number of cells and genes. Representative benchmarks:

We create a sparse count matrix of 2,000 cells and 100 genes drawn from a negative binomial distribution (~67 % zeros). The last ten genes are prefixed with mt- to simulate mitochondrial genes.

library(scTenifoldKnk)

nCells <- 2000
nGenes <- 100
set.seed(1)
X <- rnbinom(n = nGenes * nCells, size = 20, prob = 0.98)
X <- round(X)
X <- matrix(X, ncol = nCells)
rownames(X) <- c(paste0('ng', 1:90), paste0('mt-', 1:10))

output <- scTenifoldKnk(
  countMatrix   = X,
  gKO           = "ng10",
  nc_nNet       = 10,
  nc_nCells     = 500,
  td_K          = 3,
  qc_minLibSize = 30
)

# Structure of the output
str(output)

# Accessing the WT and KO gene regulatory networks
dim(output$tensorNetworks$WT)
dim(output$tensorNetworks$KO)

# Accessing the manifold alignment result
head(output$manifoldAlignment)

# Differential regulation results — top perturbed genes
head(output$diffRegulation, n = 10)

# Plotting the KO-centered subnetwork
plotKO(output, gKO = "ng10")

See also: plotKO() — Frequently Asked Questions

Osorio, D., Zhong, Y., Li, G., Xu, Q., Yang, Y., Tian, Y., Chapkin, R., Huang, J. Z., & Cai, J. J. (2022). scTenifoldKnk: An Efficient Virtual Knockout Tool for Gene Function Predictions via Single-Cell Gene Regulatory Network Perturbation. Patterns, 3(3), 100434. doi:10.1016/j.patter.2022.100434

BibTeX:

@Article{osorio2022sctenifoldknk,
  title   = {scTenifoldKnk: An Efficient Virtual Knockout Tool for Gene Function
             Predictions via Single-Cell Gene Regulatory Network Perturbation},
  author  = {Daniel Osorio and Yan Zhong and Guanxun Li and Qian Xu and
             Yongjian Yang and Yanan Tian and Robert Chapkin and
             Jianhua Z. Huang and James J. Cai},
  journal = {Patterns},
  year    = {2022},
  volume  = {3},
  number  = {3},
  pages   = {100434},
  issn    = {2666-3899},
  doi     = {10.1016/j.patter.2022.100434},
}

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