# 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
```r
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](https://github.com/yufree/thermoflask) for Thermo data or [ProteoWizard](https://proteowizard.sourceforge.io/download.html) 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')
```
