MetaDeconfound: Metadeconfound

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

Metadeconfound

Description

Metadeconfound checks all feature <-> covariate combinations for counfounding effects of covariates on feature <-> effect correlation

Usage

MetaDeconfound(
  featureMat,
  metaMat,
  nnodes = 1,
  adjustMethod = "fdr",
  robustCutoff = 5,
  QCutoff = 0.1,
  DCutoff = 0,
  PHS_cutoff = 0.05,
  NA_imputation = "remove",
  logfile = NULL,
  intermediateOutput = NULL,
  startStop = NA,
  QValues = NA,
  DValues = NA,
  minQValues = NULL,
  deconfT = NULL,
  deconfF = NULL,
  doConfs = 0,
  doRanks = NA,
  randomVar = NA,
  fixedVar = NA,
  robustCutoffRho = NULL,
  typeCategorical = NULL,
  typeContinuous = NULL,
  logistic = FALSE,
  rawCounts = FALSE,
  returnLong = FALSE,
  collectMods = FALSE,
  ...
)

Arguments

featureMat	a data frame with row(sample ID) and column(feature such as metabolite or microbial OTU ) names, listing features for all samples
metaMat	a data frame with row(sample ID) and column(meta data such as age,BMI and all possible confounders) names listing metadata for all samples. first column should be case status with case=1 and control=0. All binary variables need to be in 0/1 syntax!
nnodes	number of nodes/cores to be used for parallel processing
adjustMethod	multiple testing p-value correction using one of the methods of p.adjust.methods
robustCutoff	minimal number of sample size for each covariate in order to have sufficient power for association testing
QCutoff	significance cutoff for q-value, DEFAULT = 0.1
DCutoff	effect size cutoff (either cliff's delta or spearman correlation test estimate), DEFAULT = 0
PHS_cutoff	PostHoc Significance cutoff
NA_imputation	Missing data treatment. "remove": remove NA containing data from analysis (default). "others": Impute NAs using methods from packages MICE or AMELIA (not yet implemented)
logfile	name of optional logging file
intermediateOutput	name base for optional intermediate files (Ps, Qs and Ds from final output)
startStop	vector of optional strings controlling which parts of the pipeline should be executed. ("naiveStop": only naive associations will be computed, no confounder analysis is done)
QValues	optional data.frame containing pre-computed multiple-testing corrected p-values for naive associations
DValues	optional data.frame containing pre-computed effect sizes for naive associations
minQValues	pessimistic qvalues, can be generated by ImportLongPrior. This dataframe of QValues is used to incorporate prior knowledge of potential associations between individual features and metadata by supplying QValues < QCutoff for these associations. All significant associations thus reported will be treated as potentially confounding influences.
deconfT	vector of metavariable names *always* to be included as potential confounder
deconfF	vector of metavariable names *never* to be included as potential confounder
doConfs	optional parameter for additional computation of confidence interval of linear models in the deconfounding step (0 = no , 1 = logging, 2 = strict)
doRanks	optional vector of metavariable names, that should be rank transformed when building linear models in the doconfounding step
randomVar	optional vector of metavariable names to be treated as random effect variables. These variabels will not be tested for naive associations and will not be included as potential confounders, but will be added as random effects "+ (1|variable)" into any models being built.
fixedVar	optional vector of metavariable names to be treated as fixed effect variables. These variabels will not be tested for naive associations and will not be included as potential confounders, but will be added as fixed effects "+ variable" into any models being built.
robustCutoffRho	optional robustness cutoff for continuous variables
typeCategorical	optional character vector of metavariable names to always be treated as categorical
typeContinuous	optional character vector of metavariable names to always be treated as continuous
logistic	optional logical parameter; DEFAULT = FALSE; Set TRUE to treat supplied features as binary instead of continuous
rawCounts	optional logical parameter; DEFAULT = FALSE; Set TRUE to treat supplied features as not normalized/rarefied counts; metadeconfoundR will compute total read count per sample and include this information in the modelling steps. WARNING: naive associations computed in first part of metadeconfoundR are reliant on normalized/rarefied data. Please split your analysis up into 2 parts as shown in the documentation when using this mode..
returnLong	DEFAULT = FALSE; Set TRUE to get output in one long format data.frame instead of list of four wide format data.frames
collectMods	DEFAULT = FALSE; Set TRUE to collect all model objects generated by Metadeconfound and return them in a nested list alongside the standard Ps/Qs/Ds/status output.
...	for additional arguments used internally (development/debugging)

Details

for more details and explanations please see the vignette.

Value

list with elements (or data.frame with columns, when returnLong = TRUE) Ds = effectsize, Ps = uncorrected p-value for naive association, Qs = multiple testing corrected p-value/fdr, and status = confounding status for all feature <=> covariate combinations with following categories: (NS = not significant, OK_sd = strictly deconfounded, OK_nc = no covariates, OK_d = doubtful, AD = ambiguously deconfounded, C: followed by comma separated covariate names = confounded by listed covariates)

Can be plotted using BuildHeatmap.

Examples

data(reduced_feature)
data(metaMatMetformin)

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

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