dRegulation: Evaluates gene differential regulation based on manifold...

In cailab-tamu/PCrTdMa: Construct and Compare scGRN from Single-Cell Transcriptomic Data

Evaluates gene differential regulation based on manifold alignment distances.

Description

Using the output of the non-linear manifold alignment, this function computes the Euclidean distance between the coordinates for the same gene in both conditions. Calculated distances are then transformed using Box-Cox power transformation, and standardized to ensure normality. P-values are assigned following the chi-square distribution over the fold-change of the squared distance computed with respect to the expectation.

Usage

dRegulation(manifoldOutput)

Arguments

manifoldOutput

A matrix. The output of the non-linear manifold alignment, a labeled matrix with two times the number of shared genes as rows (X_ genes followed by Y_ genes in the same order) and d number of columns.

Value

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.

References

• Benjamini, Y., and Yekutieli, D. (2001). The control of the false discovery rate in multiple testing under dependency. Annals of Statistics, 29, 1165-1188. doi: 10.1214/aos/1013699998.

Examples

library(scTenifoldNet)

# Simulating of a dataset following a negative binomial distribution with high sparcity (~67%)
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))

# Performing Single cell quality control
qcOutput <- scQC(
  X = X,
  minLibSize = 30,
  removeOutlierCells = TRUE,
  minPCT = 0.05,
  maxMTratio = 0.1
)

# Computing 3 single-cell gene regulatory networks each one from a subsample of 500 cells
xNetworks <- makeNetworks(X = qcOutput,
                         nNet = 3, 
                         nCells = 500, 
                         nComp = 3, 
                         scaleScores = TRUE, 
                         symmetric = FALSE, 
                         q = 0.95
                         )

# Computing a K = 3 CANDECOMP/PARAFAC (CP) Tensor Decomposition 
tdOutput <- tensorDecomposition(xNetworks, K = 3, maxError = 1e5, maxIter = 1e3)

## Not run: 
# Computing the alignment
# For this example, we are using the same input, the match should be perfect. 
maOutput <- manifoldAlignment(tdOutput$X, tdOutput$X)

# Evaluating the difference in regulation
dcOutput <- dRegulation(maOutput, minFC = 0)
head(dcOutput)

# Plotting
# If FDR < 0.05, the gene will be colored in red.
geneColor <- ifelse(dcOutput$p.adj < 0.05, 'red', 'black')
qqnorm(dcOutput$Z, main = 'Standardized Distance', pch = 16, col = geneColor)
qqline(dcOutput$Z)

## End(Not run)

cailab-tamu/PCrTdMa documentation built on Aug. 6, 2022, 8:11 p.m.