pagoda.gene.clusters: Determine de-novo gene clusters and associated overdispersion...

In hms-dbmi/scde: Single Cell Differential Expression

Determine de-novo gene clusters and associated overdispersion info

Description

Determine de-novo gene clusters, their weighted PCA lambda1 values, and random matrix expectation.

Usage

pagoda.gene.clusters(varinfo, trim = 3.1/ncol(varinfo$mat),
  n.clusters = 150, n.samples = 60, cor.method = "p",
  n.internal.shuffles = 0, n.starts = 10, n.cores = detectCores(),
  verbose = 0, plot = FALSE, show.random = FALSE, n.components = 1,
  method = "ward.D", secondary.correlation = FALSE,
  n.cells = ncol(varinfo$mat), old.results = NULL)

Arguments

varinfo	varinfo adjusted variance info from pagoda.varinfo() (or pagoda.subtract.aspect())
trim	additional Winsorization trim value to be used in determining clusters (to remove clusters that group outliers occurring in a given cell). Use higher values (5-15) if the resulting clusters group outlier patterns
n.clusters	number of clusters to be determined (recommended range is 100-200)
n.samples	number of randomly generated matrix samples to test the background distribution of lambda1 on
cor.method	correlation method ("pearson", "spearman") to be used as a distance measure for clustering
n.internal.shuffles	number of internal shuffles to perform (only if interested in set coherence, which is quite high for clusters by definition, disabled by default; set to 10-30 shuffles to estimate)
n.starts	number of wPCA EM algorithm starts at each iteration
n.cores	number of cores to use
verbose	verbosity level
plot	whether a plot showing distribution of random lambda1 values should be shown (along with the extreme value distribution fit)
show.random	whether the empirical random gene set values should be shown in addition to the Tracy-Widom analytical approximation
n.components	number of PC to calculate (can be increased if the number of clusters is small and some contain strong secondary patterns - rarely the case)
method	clustering method to be used in determining gene clusters
secondary.correlation	whether clustering should be performed on the correlation of the correlation matrix instead
n.cells	number of cells to use for the randomly generated cluster lambda1 model
old.results	optionally, pass old results just to plot the model without recalculating the stats

Value

a list containing the following fields:

• clusters a list of genes in each cluster values

• xf extreme value distribution fit for the standardized lambda1 of a randomly generated pattern

• tci index of a top cluster in each random iteration

• cl.goc weighted PCA info for each real gene cluster

• varm standardized lambda1 values for each randomly generated matrix cluster

• clvlm a linear model describing dependency of the cluster lambda1 on a Tracy-Widom lambda1 expectation

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)
clpca <- pagoda.gene.clusters(varinfo, trim=7.1/ncol(varinfo$mat), n.clusters=150, n.cores=10, plot=FALSE)

hms-dbmi/scde documentation built on April 19, 2023, 10:21 p.m.