sfi

Introduction

The sfi package provides features extraction tools for single file injections(sfi) mode liquid chromatography-mass spectrometry (LC-MS) data. The SFI technique enables the analysis of numerous samples (e.g., up to 1000 per day) by acquiring data from multiple injections within a single analytical run, significantly reducing the time compared to traditional serial LC-MS injections.

Experimental Desgin

The SFI method (see demo figure) operates by:

• Injecting a pooled sample multiple times initially to establish reference chromatographic profiles.

• Subsequently injecting individual samples repeatedly at fixed, short time intervals under a continuous isocratic elution. Each sample undergoes a fixed chromatographic separation.

• This process generates a single, continuous data file containing the interleaved data from all injected samples.

Installation

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

# Install from Bioconductor (when available)
BiocManager::install("sfi")

# Or install the development version from GitHub
# remotes::install_github("yufree/sfi")

Basic Usage

Loading the Package

library(sfi)

Loading raw data

You need to convert the raw data into mzML file. You might try ThermoFlask for Thermo data or ProteoWizard for other vendor file. getmzml function will load the mzML file and return a matrix object for peak picking.

path <- 'sfi.mzML'
peak <- getmzml(path)

Feature extraction

You could use demo data in the package.

# load demo data
data(sfi)
# perform peaks picking
peaklist <-  find_2d_peaks(mz=sfi$mz,rt=sfi$rt,intensity=sfi$intensity)

Feature alignment

The getsfm() function will extract sample features from one injections file.

mz <- peaklist$mz
rt <- peaklist$rt
intensity <- peaklist$intensity
# injection interval
idelta <- 80
# time windows for a full separation
windows <- 900
# sample numbers in the files
n <- 100
# retention time shift in seconds
deltart <- 10
# min peak number in pooled qc samples 
minn <- 6
# align peaks from sfi
ndf <- getsfm(mz,rt,intensity,idelta=idelta,windows=windows,n=100,deltart = 10,minn = 6)
# generate feature list
ndf$peakid <- paste0(round(ndf$qcmz,4),'@',round(ndf$qcrt))
nndf <- ndf[,c('sampleidx','intensity','peakid')]
library(data.table)
feature <- dcast(setDT(nndf), ... ~ sampleidx, value.var = "intensity",fun.aggregate = sum)

Save feature list as csv file.

# save feature as csv file
fwrite(feature,'featurelist.csv')