Gene expression and cancer classification

Tissue heterogeneity is prevalent in gene expression studies

The paper analyzes 2,692 gene expression datasets to show tissue heterogeneity affects 5-15% of samples, advocating for quality control methods to address this issue.

Multi-resolution characterization of molecular taxonomies in bulk and single-cell transcriptomics data

The paper introduces K2Taxonomer, an unsupervised algorithm for identifying molecular subgroups in bulk and single-cell transcriptomics data, and applies it to breast cancer TILs to find a shared transcriptional program associated with better prognosis.

Tensor Decomposition-based Unsupervised Feature Extraction Succeeded in Identification of Differentially Expressed Transcripts from Redundant de novo Transcriptome of Planarian

This study uses tensor decomposition-based unsupervised feature extraction to identify differentially expressed transcripts in planarian RNA-seq data with a highly redundant de novo transcriptome, outperforming conventional methods.

GenomicSuperSignature: interpretation of RNA-seq experiments through robust, efficient comparison to public databases

GenomicSuperSignature compares new RNA-seq datasets to public archives for rapid interpretation using precomputed axes of variation.

Generative neural networks separate common and specific transcriptional responses

This paper introduces SOPHIE, a method using generative neural networks to distinguish common from specific transcriptional responses across datasets.

iSubGen: Integrative Subtype Generation by Pairwise Similarity Assessment

iSubGen generates disease subtypes by integrating multiple data types, handling missing data and identifying clinically distinct patient groups.

Target-Decoy MineR for determining the biological relevance of variables in noisy data sets

The paper introduces Target-Decoy MineR to assess the biological relevance of variables in noisy datasets by calculating false-positive rates and biological relevance probabilities.

Integrating multi-omics data through deep learning for accurate cancer prognosis prediction

The paper develops a deep learning framework to integrate multi-omics data for cancer prognosis, showing improved risk prediction across 15 cancers and identifying prognostic markers in breast cancer.

noisyR: Enhancing biological signal in sequencing datasets by characterising random technical noise

noisyR reduces technical noise in sequencing data to enhance biological signal detection and improve downstream analysis consistency.

A short report on optimization of nucleic acid probes by DNA microarray synthesis and next generation sequencing

The paper was withdrawn by the authors and not intended for citation.

Evaluating Sample Augmentation in Microarray Datasets with Generative Models: A Comparative Pipeline and Insights in Tuberculosis.

The paper evaluates generative models for sample augmentation in microarray datasets, comparing VAE, GAN, and GMM in a tuberculosis dataset to improve biomarker discovery.

Evaluating the transcriptional fidelity of cancer models

The paper evaluates the transcriptional fidelity of cancer models using a machine learning tool called CancerCellNet, finding that genetically engineered mice and tumoroids generally show higher fidelity than other model types.

Benchmarking Association Analyses of Continuous Exposures with RNA-seq in Observational Studies

The paper benchmarks RNA-seq association analyses for continuous exposures using DESeq2, edgeR, limma, and linear regression, finding linear regression methods are faster and better at controlling false detections.

PAN: Personalized Annotation-based Networks for the Prediction of Breast Cancer Relapse

This paper introduces PAN, a method to create sample-specific gene networks using annotations, improving breast cancer relapse prediction compared to existing methods.

Finding associations in a heterogeneous setting: Statistical test for aberration enrichment

The paper introduces a statistical test for detecting heterogeneous effects in omics data and shows its ability to identify disease-relevant genes missed by traditional methods.

Gene Expression Imputation with Generative Adversarial Imputation Nets

This paper introduces GAIN-GTEx, a gene expression imputation method that outperforms existing approaches on RNA-Seq data from cancer types and diverse tissues.

Machine learning-based investigation of the cancer protein secretory pathway

The paper uses machine learning and differential expression analysis to identify secretory pathway genes associated with cancer characteristics, finding KIF20A and KIF23 as key genes across multiple cancer types.

An R Package for Divergence Analysis of Omics Data

The paper introduces an R package for divergence analysis of omics data, demonstrating its use with Cancer Genome Atlas data.

Predicting tissue-specific gene expression from whole blood transcriptome

This study predicts tissue-specific gene expression from whole blood transcriptome using GTEx data, finding that blood expression can predict expression in many tissues and improve disease prediction.

Privacy Preserving RNA-Model Validation Across Laboratories

SpinAdapt is an unsupervised RNA correction algorithm that enables transfer of molecular models between labs without requiring patient-level data, improving reproducibility and privacy.

Constrained standardization of count data from massive parallel sequencing

This paper evaluates and adapts the CONSTANd normalization method for use with count data from sequencing, showing its efficiency and ability to remove bias across different experimental conditions.

Comparison of sparse biclustering algorithms for gene expression datasets

This study compares sparse biclustering algorithms on simulated and real datasets, finding Bayesian algorithms with sparsity constraints perform well on simulated data but are slow, while post-processed non-negative matrix factorization algorithms show strong performance on real datasets.

A first perturbome of Pseudomonas aeruginosa: Identification of core genes related to multiple perturbations by a machine learning approach

The paper identifies 46 core genes in Pseudomonas aeruginosa PAO1 associated with multiple perturbations using a machine learning approach.

Improving Deconvolution Methods in Biology through Open Innovation Competitions: An Application to the Connectivity Map

This study evaluates deconvolution methods for gene expression data using an open innovation competition and finds that random forest regression outperforms other approaches in accuracy and reproducibility.

Pancancer survival analysis of cancer hallmark genes

This study analyzes the prognostic impact of cancer hallmark genes across 26 tumor types using RNA-seq and survival data from TCGA, identifying significant gene-expression associations with patient outcomes.

Correction for both common and rare cell types in blood is important to identify genes that correlate with age

The paper compares two models for correcting cell type composition in blood gene expression data, finding that accounting for more cell types reduces the number of age-associated genes and highlights platelet-related pathways.

Joint analysis of gene expression levels and histological images identifies genes associated with tissue morphology

The paper develops ImageCCA to link histological image features with gene expression, identifying gene-morphology associations in TCGA and GTEx data.

TNMplot.com: a web tool for the comparison of gene expression in normal, tumor and metastatic tissues

TNMplot.com compares gene expression across normal, tumor, and metastatic tissues using transcriptome data to identify differentially expressed genes and validate their significance in cancer.

Super-delta2: An Enhanced Differential Expression Analysis Procedure for Multi-Group Comparisons of RNA-seq Data

Super-delta2 is a differential expression analysis method for RNA-seq data that controls type I error and achieves high statistical power in multi-group comparisons.

Model-based Feature Selection and Clustering of RNA-seq Data for Unsupervised Subtype Discovery

The paper introduces FSCseq, a model-based clustering method for RNA-seq data that selects informative genes, accounts for normalization factors, and identifies cancer subtypes.