removeFeatures: Feature Filtering methods

In HimesGroup/qmtools: Quantitative Metabolomics Data Processing Tools

Feature Filtering methods

Description

Removes Features based on missing values, QC and blank samples. See the details below.

Usage

## S4 method for signature 'ANY'
removeFeatures(x, method = c("missing", "blankratio", "rsd", "icc"), ...)

## S4 method for signature 'SummarizedExperiment'
removeFeatures(x, method = c("missing", "blankratio", "rsd", "icc"), i, ...)

Arguments

x	A matrix-like object or SummarizedExperiment object.
method	A string specifying which filtering method to use.
...	Arguments passed to a specific filtering method.
i	A string or integer value specifying which assay values to use when x is a SummarizedExperiment object.

Details

The method argument can be one of "missing", "blankratio", "rsd", "icc".

• "missing" removes features based on proportions of missing values. Users can specify one or more groups in samples. For multiple groups, a feature is retained if there is at least one group with a proportion of non-missing values above a cut-off.

• For "blankratio", QC/blank intensity ratios are calculated for features present at the blank samples. Features with a ratio below a cut-off will be discarded.

• "rsd" calculates a relative standard deviation (also known as coefficient of variation) for each feature using QC samples. Features with a RSD above a cut-off will be removed.

• "icc" calculates an intraclass correlation coefficient (ICC) for each feature using both biological and QC samples to identify how much of the total variation is explained by biological variability, as described in Schiffman, C et al (2019). Features with an ICC below a cut-off will be removed.

Value

A matrix or SummarizedExperiment object.

References

Schiffman, C., Petrick, L., Perttula, K. et al. Filtering procedures for untargeted LC-MS metabolomics data. BMC Bioinformatics 20, 334 (2019). https://doi.org/10.1186/s12859-019-2871-9

See Also

See removeMiss, removeBlankRatio, removeRSD, and removeICC for the underlying functions that do work.

Examples

data(faahko_se)

g <- colData(faahko_se)$sample_group

## SummarizedExperiment object
se <- removeFeatures(faahko_se, i = "raw", method = "missing",
                     group = g, cut = 0.9)

## Matrix
m <- assay(faahko_se, i = "raw")
removeFeatures(m, method = "missing", group = g, levels = "WT", cut = 0.9)

HimesGroup/qmtools documentation built on April 16, 2023, 8 p.m.