qcrlscR: Quality Control–based Robust LOESS Signal Correction

Description

qcrlscR implements quality control–based robust LOESS signal correction for metabolomics data analysis.

Main functions

The qcrlscR provides functions for metabolomics data signal correction. It allows users to optimise span for LOESS in the range of 0.05 and 0.95. This package also provides simple functions for missing value filtering. If the data set used for signal correction has large portion of missing values, users need to filter the data based on percentage of missing values. A straightforward batch shifting function is provided for batch elimination if the batch effects are still present after QC-SLRC process. User can use other R packages's PCA plot (un-supervised) or PLS and LDA plots (supervised) to assess the goodness of signal correction.

Package context

This package does not use "tidyverse" and only uses basic R for simplicity and easy maintenance. A vignette (in R and PDF format) is located in ⁠\qcrlsc\examples⁠.

Author(s)

Maintainer: Wanchang Lin wanchanglin@hotmail.com

Authors:

• Warwick Dunn

See Also

Useful links:

• https://github.com/wanchanglin/qcrlscR

• Report bugs at https://github.com/wanchanglin/qcrlscR/issues

Batch shifting

Description

Remove batch effect withing each block.

Usage

batch.shift(x, y, method = "mean", overall_average = TRUE)

Arguments

Value

a shifted data matrix.

References

Silvia Wagner, et.al, Tools in Metabonomics: An Integrated Validation Approach for LC-MS Metabolic Profiling of Mercapturic Acids in Human Urine Anal. Chem., 2007, 79 (7), pp 2918-2926, DOI: 10.1021/ac062153w

Examples

names(man_qc)
data <- man_qc$data
meta <- man_qc$meta
## batch shifting
cls.bl <- factor(meta$batch)
res <- batch.shift(data, cls.bl, overall_average = TRUE)

man_qc: test data for QC-RLSC

Description

This HPLC data set includes 4 batches with missing values.

Usage

man_qc

Format

A list with data matrix and meta data:

data

A data frame with 462 replicates (row) and 656 features (column)

meta

A data frame with 2 columns:

• batch: 4 batches

• sample_type: QC and Sample

Examples

man_qc
t(sapply(man_qc, dim))
## Select data matrix and meta data
data <- man_qc$data
meta <- man_qc$meta
## Select batches and data types
cls.qc <- factor(meta$sample_type)
cls.bl <- factor(meta$batch)

Filtering variable based on the percentage of missing values

Description

This function calculates the percentage of missing values and keeps those features with missing values percentage less than the designed threshold.

Usage

mv.filter(x, thres = 0.3)

Arguments

Value

a list of with contents:

• dat the filtered data matrix

• idx a logical vector of index for keeping features.

See Also

Other missing value processing: mv.filter.qc(), mv.perc()

Examples

names(man_qc)
data <- man_qc$data
meta <- man_qc$meta
## check missing value rates
tail(sort(mv.perc(data)), 20)
## missing values filtering
tmp <- mv.filter(data, thres = 0.15)
data_f <- tmp$dat
## compare
dim(data_f)
dim(data)

Data filtering based on "qc" missing values

Description

Data filtering based on "qc" missing values

Usage

mv.filter.qc(x, y, thres = 0.3)

Arguments

Value

a list of with contents:

• dat the filtered data matrix

• idx a logical vector of index for keeping features.

See Also

Other missing value processing: mv.filter(), mv.perc()

Examples

names(man_qc)
data <- man_qc$data
meta <- man_qc$meta
## check missing value rates
tail(sort(mv.perc(data)), 20)
## missing values filtering based on QC
cls.qc <- factor(meta$sample_type)
tmp <- mv.filter.qc(data, cls.qc, thres = 0.15)
data_f <- tmp$dat
## compare
dim(data_f)
dim(data)

Missing value percentage

Description

Calculate missing value percentage.

Usage

mv.perc(x)

Arguments

Value

missing value percentage.

See Also

Other missing value processing: mv.filter(), mv.filter.qc()

Examples

names(man_qc)
data <- man_qc$data
meta <- man_qc$meta
## check missing value rates
tail(sort(mv.perc(data)), 20)

Univariate outlier detection

Description

Perform outlier detection using univariate method.

Usage

outl.det.u(x, method = c("percentile", "median"))

Arguments

Details

• median: the absolute difference between the observation and the sample median is larger than 2 times of the Median Absolute Deviation divided by 0.6745.

• percentile: either smaller than the 1st quartile minus 1.5 times of IQR, or larger than the 3rd quartile plus 1.5 times of IQR.

Value

a logical vector.

References

Wilcox R R, Fundamentals of Modern Statistical Methods: Substantially Improving Power and Accuracy, Springer 2010 (2nd edition), pages 31-35.

Examples

x <- c(2, 3, 4, 5, 6, 7, NA, 9, 50, 50)
outl.det.u(x, "percentile")

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

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)

Wrapper function for QC-RLSC

Description

Wrapper function for QC-RLSC

Usage

qc.rlsc.wrap(
  dat,
  cls.qc,
  cls.bl,
  method = c("subtract", "divide"),
  intra = FALSE,
  opti = TRUE,
  log10 = TRUE,
  outl = TRUE,
  shift = TRUE,
  ...
)

Arguments

Value

A corrected data frame.

See Also

Other QC-RLSC function: qc.rlsc()

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 wrapper function
method <- "divide"     # "subtract"
intra <- TRUE
opti <- TRUE
log10 <- TRUE
outl <- TRUE
shift <- TRUE


res <- qc.rlsc.wrap(data, cls.qc, cls.bl, method, intra, opti, log10,
                    outl, shift)