ImportLongPrior: ImportLongPrior

In TillBirkner/metadeconfoundR: Check Multiple Covariates for Potenial Confounding Effects

ImportLongPrior

Description

ImportLongPrior imports prior knowledge of associations between individual features and metadata in form of a long-format dataframe.

Usage

ImportLongPrior(longPrior, featureMat, metaMat)

Arguments

longPrior	long-format dataframe as generated by Metadeconfound(returnLong = TRUE). Must contain at least one column containing feature names and one column containing associated metadata names, called "feature" and "metaVariable", respectively. Only associations between features and metadata present in featureMat and metaMat will be returned. Additionally, "Qs" and "status" (as produced by MetaDeconfound)columns can be supplied and will be parsed as well. If only "feature" and "metaVariable" columns are supplied, all listed associations are assumed to be significant. If "status" is supplied, only non-"NS" labeled associations will be kept.
featureMat	omics features to be analyzed by MetaDeconfound
metaMat	metadata to be analyzed by MetaDeconfound

Details

This function is meant to facilitate incorporation of prior knowledge about associations between measured omics features and available metadata both from earlier metadeconfoundR runs by supplying the long-format Metadeconfound(returnLong = TRUE) output directly or by supplying a simple list of known associations from other studies.

Value

wide-format dataframe that can be used as minQValues parameter in MetaDeconfound

Examples

data(reduced_feature)
data(metaMatMetformin)


# note that this example is only to demonstrate the process of integrating
 # prior knowledge into a MetaDeconfound() analysis. Using the output of a
 # MetaDeconfound() run as minQValues input for a second run with the exact
 # same features and metadata will not lead to any new insights since the set
 # of QValues calculated by MetaDeconfound() and the set supplied using the
 # minQValues parameter are identical in this case.

example_output <- MetaDeconfound(featureMat = reduced_feature,
                                  metaMat = metaMatMetformin,
                                  returnLong = TRUE)

minQValues <- ImportLongPrior(longPrior = example_output,
                                featureMat = reduced_feature,
                                metaMat = metaMatMetformin)

example_output2 <- MetaDeconfound(featureMat = reduced_feature,
                                  metaMat = metaMatMetformin,
                                  minQValues = minQValues)

TillBirkner/metadeconfoundR documentation built on Sept. 3, 2023, 9:11 a.m.