pagoda.top.aspects {scde} | R Documentation

## Score statistical significance of gene set and cluster overdispersion

### 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

[Package scde version 2.12.0 Index]