pagoda.pathway.wPCA {scde} | R Documentation

## Run weighted PCA analysis on pre-annotated gene sets

### Description

For each valid gene set (having appropriate number of genes) in the provided environment (setenv), the method will run weighted PCA analysis, along with analogous analyses of random gene sets of the same size, or shuffled expression magnitudes for the same gene set.

### Usage

    pagoda.pathway.wPCA(varinfo, setenv, n.components = 2,
      n.cores = detectCores(), min.pathway.size = 10, max.pathway.size = 1000,
      n.randomizations = 10, n.internal.shuffles = 0, n.starts = 10,
      center = TRUE, batch.center = TRUE, proper.gene.names = NULL,
      verbose = 0)

### Arguments

`varinfo` | adjusted variance info from pagoda.varinfo() (or pagoda.subtract.aspect())
`setenv` | environment listing gene sets (contains variables with names corresponding to gene set name, and values being vectors of gene names within each gene set)
`n.components` | number of principal components to determine for each gene set
`n.cores` | number of cores to use
`min.pathway.size` | minimum number of observed genes that should be contained in a valid gene set
`max.pathway.size` | maximum number of observed genes in a valid gene set
`n.randomizations` | number of random gene sets (of the same size) to be evaluated in parallel with each gene set (can be kept at 5 or 10, but should be increased to 50-100 if the significance of pathway overdispersion will be determined relative to random gene set models)
`n.internal.shuffles` | number of internal (independent row shuffles) randomizations of expression data that should be evaluated for each gene set (needed only if one is interested in gene set coherence P values, disabled by default; set to 10-30 to estimate)
`n.starts` | number of random starts for the EM method in each evaluation
`center` | whether the expression matrix should be recentered
`batch.center` | whether batch-specific centering should be used
`proper.gene.names` | alternative vector of gene names (replacing rownames(varinfo$mat)) to be used in cases when the provided setenv uses different gene names
`verbose` | verbosity level

### Value

a list of weighted PCA info for each valid gene set

### 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)
    # create go environment
    library(org.Hs.eg.db)
    # translate gene names to ids
    ids <- unlist(lapply(mget(rownames(cd), org.Hs.egALIAS2EG, ifnotfound = NA), function(x) x[1]))
    rids <- names(ids); names(rids) <- ids
    go.env <- lapply(mget(ls(org.Hs.egGO2ALLEGS), org.Hs.egGO2ALLEGS), function(x) as.character(na.omit(rids[x])))
    # clean GOs
    go.env <- clean.gos(go.env)
    # convert to an environment
    go.env <- list2env(go.env)
    pwpca <- pagoda.pathway.wPCA(varinfo, go.env, n.components=1, n.cores=10, n.internal.shuffles=50)

[Package scde version 2.20.0 Index]