pagoda.top.aspects: Score statistical significance of gene set and cluster...

In scde: Single Cell Differential Expression

Description

Evaluates statistical significance of the gene set and cluster lambda1 values, returning either a text table of Z scores, etc, a structure containing normalized values of significant aspects, or a set of genes underlying the significant aspects.

Usage

pagoda.top.aspects(pwpca, clpca = NULL, n.cells = NULL,
  z.score = qnorm(0.05/2, lower.tail = FALSE), return.table = FALSE,
  return.genes = FALSE, plot = FALSE, adjust.scores = TRUE,
  score.alpha = 0.05, use.oe.scale = FALSE, effective.cells.start = NULL)

Arguments

pwpca	output of pagoda.pathway.wPCA()
clpca	output of pagoda.gene.clusters() (optional)
n.cells	effective number of cells (if not provided, will be determined using pagoda.effective.cells())
z.score	Z score to be used as a cutoff for statistically significant patterns (defaults to 0.05 P-value
return.table	whether a text table showing
return.genes	whether a set of genes driving significant aspects should be returned
plot	whether to plot the cv/n vs. dataset size scatter showing significance models
adjust.scores	whether the normalization of the aspect patterns should be based on the adjusted Z scores - qnorm(0.05/2, lower.tail = FALSE)
score.alpha	significance level of the confidence interval for determining upper/lower bounds
use.oe.scale	whether the variance of the returned aspect patterns should be normalized using observed/expected value instead of the default chi-squared derived variance corresponding to overdispersion Z score
effective.cells.start	starting value for the pagoda.effective.cells() call

Value

if return.table = FALSE and return.genes = FALSE (default) returns a list structure containing the following items:

• xv a matrix of normalized aspect patterns (rows- significant aspects, columns- cells

• xvw corresponding weight matrix

• gw set of genes driving the significant aspects

• df text table with the significance testing results

Examples

data(pollen)
cd <- clean.counts(pollen)

knn <- knn.error.models(cd, k=ncol(cd)/4, n.cores=10, min.count.threshold=2, min.nonfailed=5, max.model.plots=10)
varinfo <- pagoda.varnorm(knn, counts = cd, trim = 3/ncol(cd), max.adj.var = 5, n.cores = 1, plot = FALSE)
pwpca <- pagoda.pathway.wPCA(varinfo, go.env, n.components=1, n.cores=10, n.internal.shuffles=50)
tam <- pagoda.top.aspects(pwpca, return.table = TRUE, plot=FALSE, z.score=1.96)  # top aspects based on GO only

scde documentation built on Nov. 8, 2020, 6:19 p.m.