Gene expression and cancer classification

NEMO: Cancer subtyping by integration of partial multi-omic data

NEMO is a multi-omics clustering algorithm that works with partial datasets, outperforming existing methods on full and imputed data, and is applied to AML patient data.

Fast and robust method for drug response biomarker identification and sample stratification.

This paper introduces a fast and robust method to identify drug response biomarkers and stratify samples, using transcriptomics data and Bayesian network analysis.

Computational deconvolution of gene expression in leukemic cell hierarchies

This paper develops a computational method to deconvolve gene expression in leukemic cell hierarchies and identifies markers for AML-LSC.

Differential variation and expression analysis

The paper introduces an empirical Bayes approach called L2N for detecting differential expression and variation, implemented in the DVX R package with a user-friendly interface.

Selecting precise reference normal tissue samples for cancer research using a deep learning approach

The paper evaluates using GTEx normal tissue samples as references for cancer research and finds that a deep learning approach can effectively select appropriate normal samples for disease signature creation.

Bi-clustering based biological and clinical characterization of colorectal cancer in complementary to CMS classification

This paper introduces a bi-clustering framework to characterize colorectal cancer subtypes and identify prognostic markers and drug resistance mechanisms beyond CMS classification.

Prediction Interval Ranking Score: Identification of Invariant Expression from Time Series

The paper introduces PIRS, a method to identify invariant genes from time series data by modeling the time component, improving reference gene selection for gene expression analysis.

OUTRIDER: A statistical method for detecting aberrantly expressed genes in RNA sequencing data

OUTRIDER detects aberrantly expressed genes in RNA-seq data by modeling co-variation and using significance-based thresholds to identify outliers.

PhenotypeXpression: sub-classification of disease states using public gene expression data and literature

PhenotypeXpression identifies disease subtypes by clustering gene expression data and analyzing literature to create phenotypic profiles.

Tensor decomposition--based unsupervised feature extraction for integrated analysis of TCGA data on microRNA expression and promoter methylation of genes in ovarian cancer

This study uses tensor decomposition to identify miRNAs and genes associated with ovarian cancer by integrating miRNA expression and promoter methylation data from TCGA.

Molecular characterization of breast and lung tumors by integration of multiple data types with sparse-factor analysis

This paper introduces funcSFA to identify driver biological processes in tumors by integrating multiple data types and functional annotation, finding common and novel processes in breast and lung cancers.

Clust: automatic extraction of optimal co-expressed gene clusters from gene expression data

Clust improves co-expression gene clustering by aligning with biological expectations, outperforming existing methods across multiple datasets.

Heavy-tailed prior distributions for sequence count data: removing the noise and preserving large differences

apeglm replaces DESeq2's Normal prior on log fold changes with an adaptive Cauchy prior, reducing variance for low-count genes while preserving true large effect sizes.

A relative comparison between Hidden Markov- and Log-Linear- based models for differential expression analysis in a real time course RNA sequencing data

The paper compares EBSeq-HMM and multiDE models for differential expression analysis in time course RNA-seq data, finding multiDE identifies more benchmark DEGs and agrees better with them.

Trendy: Segmented regression analysis of expression dynamics for high-throughput ordered profiling experiments

Trendy uses segmented regression to analyze gene expression dynamics in ordered experiments, identifying gene-specific patterns and overall transcriptome changes.

C3: An R package for cross-species compendium-based cell-type identification

C3 is an R package that enables cross-species cell-type identification by comparing gene expression profiles against a compendium, using a cross-species gene set analysis framework to correct for homologous gene mapping biases.

Side-By-Side Analysis Of Alternative Approaches On Multi-Level RNA-Seq Data

The paper evaluates and improves RNA-seq data quality checks and differential expression analysis across multi-level designs in D. melanogaster and H. sapiens.

Efficient feature selection on gene expression data: Which algorithm to use?

This study compares three feature selection algorithms for gene expression data, finding that gOMP outperforms LASSO and matches FBED in performance while being more computationally efficient.

Valection: Design Optimization for Validation and Verification Studies

Valection optimizes the selection of verification candidates to estimate error profiles in genomics studies by evaluating different strategies on synthetic data.

Consensus outlier detection in survival analysis using the rank product test

The paper proposes a consensus outlier detection method using the Rank Product test for survival analysis, showing its potential for identifying outliers and biomarkers in clinical datasets.

Statistical Inference Relief (STIR) feature selection

The paper introduces STIR, a statistical inferential framework for Relief-based feature selection that accounts for variance in nearest neighbor distances to assess feature importance and significance in high-dimensional data.

Prediction of early breast cancer patient survival using ensembles of hypoxia signatures

The paper evaluates machine-learning ensembles of preprocessing methods to improve survival prediction in early breast cancer using hypoxia signatures.

RNA-seq analyses of molecular abundance (RoMA) for detecting differential gene expression

RoMA improves differential gene expression detection in RNA-seq by integrating mRNA abundance and raw counts, showing high accuracy and robustness in simulated and real data.

QUBIC2: A novel biclustering algorithm for large-scale bulk RNA-sequencing and single-cell RNA-sequencing data analysis

QUBIC2 improves biclustering for bulk and single-cell RNA-Seq by addressing sparsity and transcriptional regulation, outperforming existing tools in functional module detection and cell type classification.

clusterExperiment and RSEC: A Bioconductor package and framework for clustering of single-cell and other large gene expression datasets

The paper introduces clusterExperiment, a Bioconductor package for clustering large gene expression datasets using Resampling-based Sequential Ensemble Clustering (RSEC) to identify biologically meaningful subtypes.

A deep learning framework for imputing missing values in genomic data

This paper introduces a deep learning framework called DAPL for imputing missing values in genomic data, showing improved performance and reduced computational cost compared to traditional methods.

BicGO: a new biclustering algorithm based on global optimization

BicGO is a biclustering algorithm that accurately identifies complex biclusters in large datasets using global optimization, outperforming existing tools on both simulated and real data.

Min-Redundancy and Max-Relevance Multi-view Feature Selection for Predicting Ovarian Cancer Survival using Multi-omics Data

This study develops a multi-view feature selection method to predict ovarian cancer survival using multi-omics data and shows it outperforms single-view and baseline fusion approaches.

Sparse network-based regularization for the analysis of patientomics high-dimensional survival data

This paper introduces sparse network-based regularization for survival analysis, improving predictive performance and interpretability in breast cancer transcriptomic data.

Evaluation of RNA-seq normalization methods using challenging datasets

This study evaluates RNA-seq normalization methods on challenging datasets, revealing shortcomings in common approaches and identifying a more effective strategy.