MSPrep
======
### Introduction
`MSPrep` provides a convenient set of functionalities used in the pre-analytic
processing pipeline for mass spectrometry based metabolomics data. Functions are
included for the following processes commonly performed prior to analysis of
such data:
1. Summarization of technical replicates (if available)
2. Filtering of metabolites
3. Imputation of missing values
4. Transformation, normalization, and batch correction
Original manuscript published in
[Bioinformatics](https://academic.oup.com/bioinformatics/article/30/1/133/236721),
and package is hosted by [Bioconductor](https://bioconductor.org/packages/release/bioc/html/MSPrep.html).
Additional helpful links:
1. [Vignette providing detailed instructions with examples](https://bioconductor.org/packages/release/bioc/vignettes/MSPrep/inst/doc/using_MSPrep.html)
2. [Reference Manual describing function usage](https://bioconductor.org/packages/release/bioc/manuals/MSPrep/man/MSPrep.pdf)
### Installation
Install via Bioconductor:
if (!requireNamespace("BiocManager", quietly=TRUE))
install.packages("BiocManager")
BiocManager::install("MSPrep")
Install via Github:
if (!require("devtools")) install.packages("devtools")
devtools::install_github("KechrisLab/MSPrep")
### Examples
Two examples are provided below. For more detailed information see the
package Vignette which can be accessed [via Bioconductor](https://bioconductor.org/packages/release/bioc/vignettes/MSPrep/inst/doc/using_MSPrep.html)
or by using the following R command following package installation:
```s
vignette("using_MSPrep", package = "MSPrep")
```
The following code loads the example data set, `MSQuant`, summarizes its
technical replicates, filters metabolites by only keeping those which are
present in 80% of samples, imputes missing values using k-nearest neighbors,
applies a log base ten transformation, and finally normalizes and batch corrects
the data set using quantile normalization and ComBat batch correction. Data is
then returned as a `data.frame`.
```s
library(MSPrep)
data(msquant)
preparedDF <- msPrepare(msquant,
minPropPresent = 1/3,
missingValue = 1,
filterPercent = 0.8,
imputeMethod = "knn",
transform = "log10",
normalizeMethod = "quantile + ComBat",
covariatesOfInterest = c("spike"),
compVars = c("mz", "rt"),
sampleVars = c("spike", "batch", "replicate",
"subject_id"),
colExtraText = "Neutral_Operator_Dif_Pos_",
separator = "_")
```
The second example uses the data set `COPD_131`. The raw data set can be found [here, at Metabolomics Workbench.](https://www.metabolomicsworkbench.org/data/DRCCMetadata.php?Mode=Project&ProjectID=PR000438). The code loads the data set,
summarizes its
technical replicates, filters metabolites by only keeping those which are
present in 80% of samples, imputes missing values using BPCA imputation,
and finally normalizes the data set using median normalization. Data is then
returned as a `SummarizedExperiment` by setting the argument
`returnToSE = TRUE`.
```s
library(MSPrep)
data(COPD_131)
preparedSE <- msPrepare(COPD_131,
minPropPresent = 1/3,
filterPercent = 0.8,
missingValue = 0,
imputeMethod = "bpca",
nPcs = 3,
normalizeMethod = "median",
transform = "none",
compVars = c("Mass", "Retention.Time",
"Compound.Name"),
sampleVars = c("subject_id", "replicate"),
colExtraText = "X",
separator = "_",
returnToSE = TRUE)
```
### Bug Reports
Report bugs as issues on the [GitHub repository new
issue](https://github.com/KechrisLab/MSPrep/issues/new)
