runNPARC: Non-parametric analysis of response curves

In Huber-group-EMBL/NPARC: Non-parametric analysis of response curves for thermal proteome profiling experiments

Non-parametric analysis of response curves

Description

Wrapper function for melting curve fitting and hypothesis testing.

Usage

runNPARC(x, y, id, groupsNull = NULL, groupsAlt,
  BPPARAM = BiocParallel::SerialParam(progressbar = TRUE),
  dfType = c("theoretical", "empirical"), control = getParams())

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.
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()
dfType	character value indicating the method for degrees of freedom computation for the F-test. Theoretical yields the text-book solution. Empirical yields estimates derived from the distribution moments of the RSS.
control	list of parameters used to control specific parts of the analyse

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 of null and alterantive model

Examples

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

Huber-group-EMBL/NPARC documentation built on May 11, 2023, 5:11 a.m.