nparcFit: Fit null and alternative models for Non-parametric analysis...

In NPARC: Non-parametric analysis of response curves for thermal proteome profiling experiments

Description

Fit melting curve and return model metrics as well as predictions for the null and alternative models.

Usage

NPARCfit(x, y, id, control = getParams(), groupsNull = NULL, groupsAlt,
  BPPARAM = BiocParallel::SerialParam(progressbar = TRUE),
  returnModels = FALSE)

Arguments

x	numeric vector of the independent variables (typically temperature)
y	numeric vector of the dependent variables (typically relative abundance measurements)
id	character vector with the protein ID to which each each data point belongs.
control	list of parameters used to control specific parts of the analyse
groupsNull	one or more vectors with grouping variables for the null models. See details.
groupsAlt	one or more vectors with grouping variables for the alternative models. See details.
BPPARAM	BiocParallel parameter object to invoke curve fitting in parallel. Default: BiocParallel::SerialParam()
returnModels	boolean value. If true, the fitted models are returned together with the test results

Details

groupsNull or groupsAlt can either be a single vector each, or data.frames of the same length as x and y with one column per factor

Value

data frame with fitted model parameters and additional columns listing e.g. residuals sum of squares

Examples

data(stauro_TPP_data_tidy)
df <- dplyr::filter(stauro_TPP_data_tidy, grepl("CDK|GTP|CRK", uniqueID))
testResults <- NPARCfit(x = df$temperature,
                     y = df$relAbundance,
                     id = df$uniqueID,
                     groupsAlt = df$compoundConcentration)

NPARC documentation built on Nov. 8, 2020, 8:05 p.m.