Gene expression and cancer classification

pylimma: a faithful, AnnData-native Python port of R limma for differential expression analysis

pylimma provides a Python port of the R limma package for differential expression analysis, validated across multiple datasets and showing high numerical agreement.

Gene-specific exponent-corrected normalization for library size in bulk RNA-seq

The paper introduces gecco, a gene-specific normalization method for bulk RNA-seq that reduces library size bias while preserving biological signal, improving differential expression and pathway analysis.

Impacts of batch effects on the performance of machine learning classifiers across multiple studies

This paper examines how batch effects impact machine learning classifier performance across multiple studies and compares adjustment methods.

Can a Tissue-derived Progression Signature Accurately Predict Colorectal Cancer Stage Transitions in Blood?

This study develops a machine learning framework to predict CRC stage transitions using tissue-derived transcriptomic signatures and evaluates their performance in blood-based datasets.

GR-SAFS: A Graph-Regularized Stacking Framework with Adaptive Feature Selection for High-Dimensional Prognostic Biomarker Discovery

GR-SAFS identifies a 20-gene prognostic signature from TCGA-LUAD data with high concordance index, outperforming other methods in external validation.

vDeepInsight: an injective three-dimensional voxel carrier for tabular-feature neighborhood learning

vDeepInsight introduces a 3D voxel carrier for tabular data that improves feature neighborhood representation, leading to better performance in gene expression tasks.

Complex-valued representations of time-series gene expression profiles for network analysis

This study introduces a complex-valued representation for time-series gene expression data, showing it captures distinct gene-gene similarities and temporal programs comparable to conventional methods.

TifBERT: a self-supervised foundation model for normalization-robust bulk RNA-seq representation learning

TifBERT is a self-supervised foundation model for bulk RNA-seq that learns normalization-robust representations without expression discretization or external embeddings, achieving high accuracy and capturing pathway-level biology.

When batch correction corrupts gene expression: uncovering distortions in correlation structures

The paper identifies how batch correction methods can distort gene-gene correlations and introduces a metric to quantify this issue.

Multivariate integration of histological images and gene expression data: a comparative review

This paper reviews multivariate methods for integrating histological images and gene expression data, showing they capture distinct yet complementary aspects of the underlying information.

ROTS 2.0: A reproducibility-driven framework for robust statistical modeling across diverse high-throughput omics study designs

ROTS 2.0 improves statistical modeling in omics studies by optimizing reproducibility across various experimental designs and provides open-source implementations in R and Python.

In Silico Generation of Gene Expression profiles usingDiffusion Models

This paper introduces diffusion models for generating synthetic gene expression data and shows they outperform existing methods in predictive performance.

Multi-Algorithm Machine Learning Benchmarking for Pan-Cancer Classification from Tumour-Educated Platelet RNA Sequencing

The paper benchmarks machine learning classifiers on TEP RNA-seq data to classify cancer types, finding Multinomial Logistic Regression achieves the highest performance.

An Exponential Scale Mixture Model for Metatranscriptomics Data with Application to Inflammatory Bowel Disease

The paper introduces a scale mixture model for metatranscriptomics data to improve differential expression analysis in inflammatory bowel disease, identifying new candidate genes not found by Gaussian methods.

PXN Unlocks the Power of Public Gene Expression Data Through Cross-Technology Integration

PXN improves cross-platform gene expression data integration by removing technology-specific biases and enhancing differential expression analysis.

Solid Tumors Pan Cancer Transcriptome: Tissue/Cancer specific expression groups at the Isoform-Level

The paper introduces STPCaT, an isoform-centric analysis of solid tumors, revealing cancer-specific and normal-specific isoform groups and identifying new cancer-testis antigens with diagnostic potential.

TumorArchetypeR: A modular framework to derive signature-based tumor subtypes

TumorArchetypeR develops a modular framework to derive signature-based tumor subtypes, resolving fibrotic tumor heterogeneity and identifying clinically relevant subtypes with robust classifiers.

An assessment of normalization and differential expression methods for miRNA-seq analysis using a realistic benchmark dataset

The paper benchmarks normalization and differential expression methods for miRNA-seq using a realistic dataset, finding RPM normalization and edgeR perform best.

SurvMarker: An R Package for Identifying Survival-Associated Molecular Features Using PCA-Based Weighted Scores

SurvMarker identifies survival-associated molecular features using PCA-based weighted scoring and provides an R package with documentation and examples.

Tumor cell specific total mRNA expression informed neural networks predicts cancer progression

TmSNet predicts tumor cell-specific total mRNA expression from multi-omic data, achieving high accuracy across cancer types and external cohorts.

Cross-assay RNA modeling reveals cancer biomarkers

This paper integrates multiple transcriptomics assays to identify cancer biomarkers, finding GBP4 as a predictor of recurrence and immune remodeling.

Gene coordination patterns across 8,314 tumors reveal a spectral point of no return in cancer progression

The paper identifies a universal molecular point of no return in cancer progression by analyzing gene coordination patterns across 8,314 tumors, revealing shared survival predictors across multiple cancer types.

Clustering Digestive Tract Tumors Using Transcriptomic and Mutation Data

The paper develops a method called Newmanization to reduce tissue signals in transcriptomic data and applies it to digestive tract cancer samples, showing improved clustering and separation compared to mutation data.

A Bayesian approach for identifying similar transcript dynamics using curve registration

This paper introduces a Bayesian method to align transcript dynamics across different time scales and species, enabling comparative analysis of gene expression patterns.

Generative augmentation recovers prognostic disease signatures from underpowered transcriptomic datasets

GeneLift recovers prognostic gene signatures from underpowered transcriptomic datasets by augmenting data with Gaussian mixture modeling and validating results with EvidenceClock.

Housekeeping Gene Expression Normalization in Transcriptomics Mitigates Data Leakage in Machine Learning Models

The paper identifies stable housekeeping genes to improve machine learning model performance by reducing data leakage in transcriptomic data from kidney transplant recipients.

RNABag: A Generalizable Transcriptome Foundation Model for Precision Oncology across Biopsy Modalities

RNABag is a transcriptome foundation model that generalizes across biopsy modalities, achieving superior performance in cancer detection and patient stratification.

X-Plat: A polynomial regression based tool for cross-platform transformation of expression and methylation data

X-Plat transforms expression and methylation data between microarray and sequencing platforms using polynomial regression, achieving lower RMSE than existing methods across multiple species.

Extending differential gene expression testing to handle genome aneuploidy in cancer

DeConveil extends differential gene expression analysis to account for genome aneuploidy by integrating CNV data, improving gene classification and biomarker discovery in cancer.

Shape-constrained, changepoint additive models for time series omics data with cpam

The paper introduces cpam, a time series analysis method for omics data that improves detection of temporal changes and changepoints, outperforming existing methods in false discovery rate control and power.