bio-metabolomics-xcms-preprocessing
XCMS Metabolomics Preprocessing
Requires Bioconductor 3.18+ with xcms 4.0+ and MSnbase 2.28+.
Load Raw Data
library(xcms)
library(MSnbase)
# Read mzML/mzXML files
raw_files <- list.files('raw_data', pattern = '\\.(mzML|mzXML)$', full.names = TRUE)
# Create OnDiskMSnExp object
raw_data <- readMSData(raw_files, mode = 'onDisk')
# Check data
raw_data
table(msLevel(raw_data))
More from gptomics/bioskills
bioskills
Installs 425 bioinformatics skills covering sequence analysis, RNA-seq, single-cell, variant calling, metagenomics, structural biology, and 56 more categories. Use when setting up bioinformatics capabilities or when a bioinformatics task requires specialized skills not yet installed.
104bio-single-cell-batch-integration
Integrate multiple scRNA-seq samples/batches using Harmony, scVI, Seurat anchors, and fastMNN. Remove technical variation while preserving biological differences. Use when integrating multiple scRNA-seq batches or datasets.
5bio-epitranscriptomics-merip-preprocessing
Align and QC MeRIP-seq IP and input samples for m6A analysis. Use when preparing MeRIP-seq data for peak calling or differential methylation analysis.
5bio-data-visualization-multipanel-figures
Combine multiple plots into publication-ready multi-panel figures using patchwork, cowplot, or matplotlib GridSpec with shared legends and panel labels. Use when combining multiple plots into publication figures.
5bio-data-visualization-specialized-omics-plots
Reusable plotting functions for common omics visualizations. Custom ggplot2/matplotlib implementations of volcano, MA, PCA, enrichment dotplots, boxplots, and survival curves. Use when creating volcano, MA, or enrichment plots.
5bio-read-qc-fastp-workflow
All-in-one read preprocessing with fastp including adapter trimming, quality filtering, deduplication, base correction, and HTML report generation. Use when preprocessing Illumina data and wanting a single fast tool instead of separate Cutadapt, Trimmomatic, and FastQC steps.
5