scTenifoldKnk: scTenifoldKNK

Description

Predict gene perturbations

Usage

scTenifoldKnk(
  countMatrix,
  qc = TRUE,
  gKO = NULL,
  qc_mtThreshold = 0.1,
  qc_minLSize = 1000,
  qc_minCells = 25,
  nc_lambda = 0,
  nc_nNet = 10,
  nc_nCells = 500,
  nc_nComp = 3,
  nc_scaleScores = TRUE,
  nc_symmetric = FALSE,
  nc_q = 0.9,
  td_K = 3,
  td_maxIter = 1000,
  td_maxError = 1e-05,
  td_nDecimal = 3,
  ma_nDim = 2,
  nCores = parallel::detectCores()
)

Arguments

countMatrix: countMatrix
qc: A boolean value (TRUE/FALSE), if TRUE, a quality control is applied over the data.
gKO: gKO
qc_mtThreshold: A decimal value between 0 and 1. Defines the maximum ratio of mitochondrial reads (mithocondrial reads / library size) present in a cell to be included in the analysis. It's computed using the symbol genes starting with 'MT-' non-case sensitive.
qc_minLSize: An integer value. Defines the minimum library size required for a cell to be included in the analysis.
qc_minCells: An integer value. Defines the minimum number of cells a gene should be expressed in to be included in the analysis. By default, it's set to 25.
nc_lambda: A continuous value between 0 and 1. Defines the multiplicative value (1-lambda) to be applied over the weaker edge connecting two genes to maximize the adjacency matrix directionality.
nc_nNet: An integer value. The number of networks based on principal components regression to generate.
nc_nCells: An integer value. The number of cells to subsample each time to generate a network.
nc_nComp: An integer value. The number of principal components in PCA to generate the networks. Should be greater than 2 and lower than the total number of genes.
nc_scaleScores: A boolean value (TRUE/FALSE), if TRUE, the weights will be normalized such that the maximum absolute value is 1.
nc_symmetric: A boolean value (TRUE/FALSE), if TRUE, the weights matrix returned will be symmetric.
nc_q: A decimal value between 0 and 1. Defines the cut-off threshold of top q% relationships to be returned.
td_K: An integer value. Defines the number of rank-one tensors used to approximate the data using CANDECOMP/PARAFAC (CP) Tensor Decomposition.
td_maxIter: An integer value. Defines the maximum number of iterations if error stay above td_maxError.
td_maxError: A decimal value between 0 and 1. Defines the relative Frobenius norm error tolerance.
td_nDecimal: An integer value indicating the number of decimal places to be used.
ma_nDim: An integer value. Defines the number of dimensions of the low-dimensional feature space to be returned from the non-linear manifold alignment.
nCores: An integer value. Defines the number of cores to be used.

Author

Daniel Osorio <dcosorioh@tamu.edu>

Examples

Run this code

# Loading single-cell data
scRNAseq <- system.file("single-cell/example.csv", package = "scTenifoldKnk")
scRNAseq <- read.csv(scRNAseq, row.names = 1)

# Running scTenifoldKnk
scTenifoldKnk(countMatrix = scRNAseq, gKO = "G100", qc_minLSize = 0)

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