batch correction / integration

SeCAT (Sequence Consensus Amplicon Trimming): a simulation-informed Nextflow pipeline for cross-study harmonisation of 16S rRNA metabarcoding datasets

SeCAT is a Nextflow pipeline that harmonizes 16S rRNA metabarcoding datasets by identifying a consensus sequence window and testing its impact on community structure before merging datasets.

Diagnose Harmonisation (DHARM): An open-source tool for the harmonisation and analysis of batch effects in MRI-derived measures.

DHARM is an open-source tool that diagnoses, harmonizes, and evaluates batch effects in MRI data using standardized workflows and multivariate diagnostics.

Transcriptional contingency in the early hematopoiesis of inherited bone marrow failure syndromes

This study integrates and analyzes scRNA-seq data from hematopoietic stem and progenitor cells of patients with Fanconi anemia and Telomere Biology disorders, identifying distinct gene expression profiles and shared transcriptional programs.

Batch Effect Correction to Enhance Utility of a Publicly Available ALS Proteomics Dataset

The paper corrects batch effects in the Answer ALS proteomics dataset using BDCs and ComBat, improving data utility while preserving biological variability.

Denoising single-cell ATAC-seq data with Shannon entropy

This paper introduces a Shannon entropy-based method to filter high-quality nuclei in scATAC-seq data, improving signal-to-noise ratio and downstream analysis reliability.

multiomeR: a pipeline for reproducible analysis of large-scale single-nucleus Epi Multiome datasets

multiomeR provides a reproducible R pipeline for analyzing large-scale single-nucleus Epi Multiome datasets, improving efficiency and reducing memory usage compared to existing workflows.

Optimal transport reveals unresolved cells in single cell multi-omics

This paper introduces optimal transport to explicitly reveal cell correspondences in single-cell multi-omics, improving integration and label transfer by identifying and removing ambiguous cells.

Interactive Data Integrator for Uniform Data Processing in the MorPhiC Program

The paper introduces an interactive Data Integrator for uniform processing and integration of bulk RNA-seq datasets from the MorPhiC program and external sources.

IsoMatch enables population-scale transcript integration and reveals reference-dependent isoform novelty

IsoMatch integrates transcript models across references, showing how reference choice affects estimates of transcript novelty.

Mitigating genotype-driven bias improves atlas-scale single-cell DNA methylome profiling

The paper mitigates genotype-driven bias in single-cell DNA methylation analysis by masking variant positions, improving clustering and reducing spurious methylation differences in pooled datasets.

2,234 attempts to harmonize a multi-platform B-cell lymphoma bulk transcriptomic dataset: ComBat is superior by cross-batch prediction quality

The paper benchmarks 2,234 transcriptomic harmonization approaches on B-cell lymphoma data, finding ComBat excels in cross-batch prediction quality.

Characterizing and Mitigating Protocol-Dependent Gene Expression Bias in 3' and 5' Single-Cell RNA Sequencing

This study identifies and mitigates protocol-dependent gene expression bias between 3' and 5' scRNA-seq by focusing on a small set of biased genes rather than applying broad normalization.

AmbiDose: per-cell removal budgets for ambient RNA contamination

AmbiDose removes ambient RNA contamination in single-cell RNA sequencing by using cell-specific removal budgets and ambient profiles, achieving high specificity for same-species UMIs.

Imputation-Based Harmonization Mitigates Site Effects Without Data Leakage in Machine Learning Studies

The paper introduces MIRTH, an imputation-based harmonization method that mitigates site effects in neuroimaging data without data leakage, achieving high predictive accuracy in machine learning studies.

BatchVaria: variance-based evaluation of batch correction with provenance tracking

BatchVaria evaluates batch correction methods by comparing variance changes across configurations using TCGA breast cancer RNA-seq data.

CycloCross: Adversarial single-cell RNA-seq data translation across species

Translating cells between mouse, macaque and human scRNA-seq with a CycleGAN, without ortholog mapping, preserved developmental time structure, imputed missing states and produced more realistic synthetic cells than scGAN.

AtlasOT - The Fused Unbalanced Gromov-Wasserstein for Multimodal Integration of Disease Atlases

AtlasOT improves multimodal integration of disease atlases by addressing unpaired data constraints through a novel optimal transport framework.

An Enhanced Pipeline for Multi-Omic Integration Based on Topological Data Analysis

The paper introduces an enhanced pipeline for multi-omic integration using topological data analysis to rank genes by disease relevance, validated on TCGA breast cancer data.

ResolVI - addressing noise and bias in spatial transcriptomics

ResolVI improves spatial transcriptomics by correcting molecule misassignment and batch effects, enhancing cell state distinction and cross-dataset integration.

MoTRUST: adaptive semantic protection for mosaic single-cell integration and molecular recovery

MoTRUST integrates mosaic single-cell multi-omics data and recovers missing molecular information, achieving top performance in integration and recovery tasks.

OT-knn: a neighborhood-aware optimal transport framework for aligning spatial transcriptomics data

OT-knn aligns spatial transcriptomics data by integrating local neighborhood information within an optimal transport framework, achieving accurate alignment across diverse datasets and conditions.

An integrated, transcript-resolution atlas of prostate cancer progression unmasks associations with oncogenic pathways

A transcript-resolution prostate cancer atlas, built by uniformly reprocessing public RNA-seq cohorts, links isoform switches in cancer genes to oncogenic pathway activity and some to CRPC survival.

Benchmarking of bulk transcriptomic harmonization tools in a multi-platform B-cell lymphoma cohort identifies feature-specific quantile normalization and surrogate variable analysis as top-performing methods

Across 7,174 multi-platform lymphoma transcriptomes, only surrogate variable analysis and feature-specific quantile normalization corrected batch effects while keeping subtype and normal B-cell differences; top-scoring MNN erased biomarker correlations.

Empirical Estimation of Ambient Contamination in Combinatorial Single-Cell Methods Using Multi-Reference Mapping

The paper introduces methods to estimate and remove ambient contamination in scifi-ATAC data, improving the accuracy of chromatin profiles and biological discovery.

Ryder: Epigenome normalization using a two-tier model and internal reference regions

Ryder normalizes epigenomic bigWig tracks against stable internal reference regions such as invariant CTCF sites, correcting background and signal separately, recovering expected perturbation effects that depth-only normalization obscured.

LIGER2: Scalable Single-Cell Integration with On-Disk Datasets

LIGER2 improves single-cell data integration by enhancing scalability and efficiency while preserving biological variation.

SPARKLE: evidence-constrained correction of local RNA leakage in high-resolution spatial transcriptomics

SPARKLE corrects local RNA leakage in spatial transcriptomics by using spatial evidence within each sample, improving cell-type localization and communication inference.

Scalable, Generalizable, and Uncertainty-Aware Integration of Spatial Multi-Omics Across Diverse Modalities and Platforms with SCIGMA

SCIGMA integrates spatial multi-omics data across modalities and platforms using a deep learning framework with uncertainty-aware contrastive learning, outperforming existing methods in domain detection and modality preservation.

Image-guided alignment of consecutive multi-modal tissue slides

COAST aligns consecutive tissue sections using only spatial images, enabling unified multi-modal molecular data analysis without molecular markers or annotations.

On the use of variational autoencoders for biomedical data integration

The paper applies variational autoencoders to integrate biomedical data, explores model dynamics, and evaluates methods using synthetic and real datasets.