View source: R/MetaDeconfound.R
MetaDeconfound | R Documentation |
MetaDeconfound checks all feature <-> covariate combinations for counfounding effects of covariates on feature <-> effect correlation
MetaDeconfound(
featureMat,
metaMat,
nnodes = 1,
adjustMethod = "fdr",
robustCutoff = 5,
QCutoff = 0.1,
DCutoff = 0,
PHS_cutoff = 0.05,
logfile = NULL,
logLevel = "INFO",
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,
...
)
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 |
logfile |
name of optional logging file. |
logLevel |
logging verbosity, possible levels: TRACE, DEBUG, INFO, WARN, ERROR, FATAL, DEFAULT = INFO |
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 variables 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. Any associations reducible to the supplied random effect(s) will be labeled as "NS". Note: Ps, Qs, Ds are computed independently and thereby not changed through inclusion of random effects. |
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. Any associations reducible to the supplied fixed effect(s) will be labeled as "NS". Note: Ps, Qs, Ds are computed independently and thereby not changed through inclusion of fixed effects. |
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) |
for more details and explanations please see the vignette.
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.
data(reduced_feature)
data(metaMatMetformin)
example_output <- MetaDeconfound(featureMat = reduced_feature,
metaMat = metaMatMetformin,
logLevel = "ERROR")
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