dRegulation {scTenifoldNet} | R Documentation |
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 |
Value
A data frame with 6 columns as follows:
-
gene
A character vector with the gene id identified from themanifoldAlignment
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 asP[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)