qc.rlsc: QC based robust LOESS signal correction (QC-RLSC)

In qcrlscR: Quality Control–based Robust LOESS Signal Correction

QC based robust LOESS signal correction (QC-RLSC)

Description

QC based robust LOESS (locally estimated scatterplot smoothing) signal correction (QC-RLSC)

Usage

qc.rlsc(x, y, method = c("subtract", "divide"), opti = TRUE, ...)

Arguments

x	A data frame with samples (row) and variables (column).
y	A vector with string of "qc" and "sample".
method	Data scaling method.
opti	A logical value indicating whether or not optimise 'span'
...	Other parameter for 'loess'.

Details

This function includes only information of sample types (QC or Sample) for signal correction. It does not require batch information. User may use batch elimination routine such as batch.shift() in this package or others to remove batch effects after signal correction.

If data matrix has missing values, user should filter the data based on missing values percentage. No missing values imputation is needed.

An option is also provided to optimise LOESS's span in a range between 0.05 to 0.95. The R codes are modified from https://bit.ly/3zBo3Qn.

Value

A corrected data frame.

References

Dunn et al. Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry. Nature Protocols 6, 1060–1083 (2011)

See Also

Other QC-RLSC function: qc.rlsc.wrap()

Examples

names(man_qc)
data <- man_qc$data
meta <- man_qc$meta

cls.qc <- factor(meta$sample_type)
cls.bl <- factor(meta$batch)


## apply QC-RLSC with optimisation of 'span'
res_1 <- qc.rlsc(data, cls.qc, method = "subtract", opti = TRUE)

## apply QC-RLSC without optimisation of 'span'
res_2 <- qc.rlsc(data, cls.qc, method = "subtract", opti = FALSE)

qcrlscR documentation built on June 8, 2025, 11:21 a.m.