fromGenes2MLData: Obtaining ML features for your genes of interest

In juanbot/G2PML:

Description

fromGenes2MLData obtains the genetic properties (transcriptomic, coexpression, genetic constraint.. etc) for a given set of gene symbols

Usage

fromGenes2MLData(genes, addcontrols = T, which.controls = "allghosh",
  condition = NULL, vars = NULL, filter = c("DPI", "DSI", "ESTcount",
  "constitutiveexons"), ...)

Arguments

genes	chr vector. Gene symbols of your disease genes - can be returned from getGenesFromPanelApp.
addcontrols	lgl scalar. Do you want to add a set of control genes?
which.controls	chr scalar. One of "allghosh", "allgenome", "clustering", "gauss", "gausskfold" specifying the set of control genes you would like to use.
condition	chr vector. Vector of length genes describing which are "Disease" and "Nondisease".
vars	chr vector. Names of features you would like to include.
filter	chr vector. Names of features you would like to exclude.
...	additional arguments for clustering, only used when which.controls is "clustering".

Value

df with features of input genes formatted for ML.

Examples

genes = getGenesFromPanelApp(disorder="Neurology and neurodevelopmental disorders",
  panel="Parkinson Disease and Complex Parkinsonism", color = "green")
genedata = fromGenes2MLData(genes=genes, which.controls="allgenome")

juanbot/G2PML documentation built on Aug. 1, 2020, 5:07 a.m.