readme.md

In rmflight/metabolomicsUtilities: Provides useful functions for working with metabolomics data

Metabolomics Utilities

Provides a set of utilities useful for general working with metabolomics data.

Installation

To install this package, use devtools.

git clone https://gitlab.cesb.uky.edu/rmflight/metabolomicsUtilities
cd metabolomicsUtilities
R
BiocInstaller::biocLite("IRanges")
devtools::install()

Log Transform

Often we want to log-transform our data, and add a small constant to the data to avoid Inf or NA values. The log_with_min function provides that.

# read data into a matrix or vector
x <- data()
log_x <- log_with_min(x)

The default is to add a value that is 10exp-3 smaller than the smallest non-zero value in the data.

M/Z Values Within M/Z Ranges

If one has m/z ranges that you want to determine that m/z values are within (like our HPD sites), this is very useful. We make use of the IRanges package from Bioconductor. Lets imagine you've generated a matrix where the first column is the start of the m/z range, and the second is the end of the m/z range.

# read in hpd sites from a set of json files
hpd_site_files <- dir("json_loc")
hpd_sites <- lapply(hpd_site_files, function(in_file){
  jsonlite::fromJSON(in_file)$stitch_regions
})

hpd_sites <- do.call(rbind, hpd_sites)

hpd_regions <- mz_to_iranges(hpd_sites)

raw_data <- read.table("raw_data_file")

raw_mz <- mz_to_iranges(raw_data$mz)

is_hpd <- raw_mz %within% hpd_regions

Note that in both cases, the mz gets multiplied by 10,000, because the IRanges expects integer based ranges. This factor of 10,000 should be enough to preserve the correct number of digits to detect intersections.

rmflight/metabolomicsUtilities documentation built on Oct. 28, 2023, 6:41 p.m.