SynMut

CATALYST provides tools for preprocessing of and differential discovery in cytometry data such as FACS, CyTOF, and IMC. Preprocessing includes i) normalization using bead standards, ii) single-cell deconvolution, and iii) bead-based compensation. For differential discovery, the package provides a number of convenient functions for data processing (e.g., clustering, dimension reduction), as well as a suite of visualizations for exploratory data analysis and exploration of results from differential abundance (DA) and state (DS) analysis in order to identify differences in composition and expression profiles at the subpopulation-level, respectively.

A preprocessing pipeline for single cell RNA-seq/ATAC-seq data that starts from the fastq files and produces a feature count matrix with associated quality control information. It can process fastq data generated by CEL-seq, MARS-seq, Drop-seq, Chromium 10x and SMART-seq protocols.

Provides a reproducible and modular workflow for absolute microbial quantification using spike-in controls. Supports both single spike-in taxa and synthetic microbial communities with user-defined spike-in volumes and genome copy numbers. Compatible with 'phyloseq' and 'TreeSummarizedExperiment' (TSE) data structures. The package implements methods for spike-in validation, preprocessing, scaling factor estimation, absolute abundance conversion, bias correction, and normalization. Facilitates downstream statistical analyses with 'DESeq2', 'edgeR', and other Bioconductor-compatible methods. Visualization tools are provided via 'ggplot2', 'ggtree', and related packages. Includes detailed vignettes, case studies, and function-level documentation to guide users through experimental design, quantification, and interpretation.

msqrob2 provides a robust linear mixed model framework for assessing differential abundance in MS-based Quantitative proteomics experiments. Our workflows can start from raw peptide intensities or summarised protein expression values. The model parameter estimates can be stabilized by ridge regression, empirical Bayes variance estimation and robust M-estimation. msqrob2's hurde workflow can handle missing data without having to rely on hard-to-verify imputation assumptions, and, outcompetes state-of-the-art methods with and without imputation for both high and low missingness. It builds on QFeature infrastructure for quantitative mass spectrometry data to store the model results together with the raw data and preprocessed data.

Low- and high-level wrappers for Gemma's RESTful API. They enable access to curated expression and differential expression data from over 10,000 published studies. Gemma is a web site, database and a set of tools for the meta-analysis, re-use and sharing of genomics data, currently primarily targeted at the analysis of gene expression profiles.

This package serves as an upstream pipeline for pre-processing sequencing-based spatial transcriptomics data. Functions includes FASTQ trimming, BAM file reformatting, index building, spatial barcode detection, demultiplexing, gene count matrix generation with UMI deduplication, QC, and revelant visualization. Config is an essential input for most of the functions which aims to improve reproducibility.