denovolyzeR (version 0.2.0)

denovolyze: Evaluates burden of de novo variation against expectation

Description

Determines whether the test population carry more de novo variants than expected. Variants may be grouped by variant class (e.g. are there more LOF variants than expected, across the whole dataset?), or by gene (are there more variants of a given class in SCN2A?).

Usage

denovolyze(genes, classes, nsamples, groupBy = "class", includeGenes = "all", includeClasses = c("syn", "mis", "misD", "non", "stoploss", "startgain", "splice", "frameshift", "lof", "prot", "protD", "all"), geneId = "geneName", signifP = 3, roundExpected = 1, probTable = NULL, misD = NULL)
denovolyzeByClass(genes, classes, nsamples, groupBy = "class", includeGenes = "all", includeClasses = c("syn", "mis", "lof", "prot", "all"), geneId = "geneName", signifP = 3, roundExpected = 1, probTable = NULL)
denovolyzeByGene(genes, classes, nsamples, groupBy = "gene", includeGenes = "all", includeClasses = c("lof", "prot"), geneId = "geneName", signifP = 3, roundExpected = 1, probTable = NULL)

Arguments

genes

A vector of genes containing de novo variants.

classes

A vector of classes of de novo variants. Standard supported classes are "syn" (synonymous), "mis" (missense), "non" (nonsense), "splice" (splice), "frameshift" (frameshift) and "lof" (loss of function = non + splice + frameshift). Additional classes that are supported by the code, but are not included in the built-in probability tables, are "stoploss","startloss", "misD" (damaging missense). These labels may be used for user-supplied probability tables. If "misD" is present, then "mis" (in the input) implies non-damaging missense.

nsamples

Number of individuals considered in de novo analysis.

groupBy

Results can be tabulated by "gene", or by variant "class"

includeGenes

Genes to include in analysis. "all" or a vector of gene names.

includeClasses

Determines which variant classes are tabulated in output. In addition to the input classes, summaries can be produced for "prot" (protein-altering = mis + lof), "all", and "protD" (protein damaging = misD + lof, only available if misD included in user-specified probability table). If "misD" is present, then "mis" will return statistics for all missense. Non-damaging missense are not analysed separately.

geneId

Gene identifier used. One of "hgncID", "hgncSymbol", "enstID", "ensgID" or "geneName" (default, equals ensembl "external_gene_name")

signifP

Number of significant figures used to round p-values in output.

roundExpected

Number of decimal places used to round expected burdens in output.

probTable

Probability table. A user-defined table of probabilities can be provided here, to replace the probability table included in the package.

misD

If the user-specified probability table contains probabilities for a sub-category of missense variants (e.g. predicted to be damaging by an in silico algorithm), this column should be called misD, or the alternative name should be specified here.

Value

Returns a data frame

Functions

• denovolyzeByClass: denovolyzeByClass
• denovolyzeByGene: denovolyzeByGene

Details

Analyses can be restricted to a subset of genes, and/or a subset of variant classes

See vignette("denovolyzeR_intro") for more information.

Examples

### denovolyze

denovolyze(genes=autismDeNovos$gene,
           classes=autismDeNovos$class,
           nsamples=1078)

### denovolyzeByClass

denovolyzeByClass(genes=autismDeNovos$gene,
                  classes=autismDeNovos$class,
                  nsamples=1078)

# this convenience function is identical to:

denovolyze(genes=autismDeNovos$gene,
           classes=autismDeNovos$class,
           nsamples=1078,
           groupBy="class",
           includeClasses=c("syn","mis","lof","prot","all"),
           includeGenes="all"
           )

### denovolyzeByGene

denovolyzeByGene(genes=autismDeNovos$gene,
                 classes=autismDeNovos$class,
                 nsamples=1078)

# this is identical to:

denovolyze(genes=autismDeNovos$gene,
           classes=autismDeNovos$class,
           nsamples=1078,
           groupBy="gene",
           includeClasses=c("lof","prot"),
           includeGenes="all"
           )