matrix factorization

Integrative analysis of Dupuytren’s disease identifies novel risk locus and reveals a shared genetic etiology with BMI

This study identifies 43 tissue-specific gene associations with Dupuytrens disease risk and finds significant genetic correlations with BMI, type II diabetes, and lipid traits.

Estimating variance components in population scale family trees

The paper introduces Sci-LMM, a framework for analyzing population-scale family trees, and estimates heritability of longevity and reproductive fitness in a large historical pedigree.

Panoramic stitching of heterogeneous single-cell transcriptomic data

Scanorama matches mutual nearest neighbors across all scRNA-seq data set pairs, rapidly merging 105,476 cells from 26 experiments while keeping data set-specific cell types separate.

deGSM: memory scalable construction of large scale de Bruijn Graph

deGSM constructs large de Bruijn graphs efficiently in limited memory, handling terabase-pair datasets on standard hardware.

PSL-Recommender: Protein Subcellular Localization Prediction using Recommender System

PSL-Recommender improves protein subcellular localization prediction using a recommender system, outperforming existing methods on multiple datasets.

Stabilized Independent Component Analysis outperforms other methods in finding reproducible signals in tumoral transcriptomes

The paper introduces a stabilized ICA method that improves reproducibility and interpretability of transcriptomic signals across cancer datasets, and applies it to colorectal cancer.

Probabilistic Count Matrix Factorization for Single Cell Expression Data Analysis

The paper introduces a probabilistic Count Matrix Factorization method for single-cell expression data analysis, showing improved clustering performance compared to existing methods.

Uncovering hypergraphs of cell-cell interaction from single cell RNA-sequencing data

scTensor stacks ligand-receptor coexpression across cell-type pairs into a tensor and applies non-negative Tucker decomposition, recovering many-to-many cell-cell interactions that a label-permutation test missed in simulation.

Predicting clinically promising therapeutic hypotheses using tensor factorization

The paper uses tensor factorization to predict successful therapeutic target-indication pairs by analyzing clinical trial data and comparing performance with other machine learning models.

McImpute: Matrix completion based imputation for single cell RNA-seq data

McImpute improves single cell RNA-seq data by imputing dropouts, enhancing clustering, differential expression, and visualization.

SLM-Transform: A Method for Memory-Efficient Indexing of Spectra for Database Search in LC-MS/MS Proteomics

SLM-Transform improves memory efficiency for peptide indexing in LC-MS/MS proteomics by limiting indexed ions and enabling constant-time queries.

Column subset selection for single-cell RNA-Seq clustering

This paper introduces a deterministic column subset selection method for scRNA-Seq clustering, showing it performs similarly to full data clustering while avoiding some limitations of PCA and thresholding methods.

Penalized matrix decomposition for denoising, compression, and improved demixing of functional imaging data

~2-4x SNR gains and 20-300x compression on calcium and voltage imaging, from a patch-wise penalized matrix decomposition that splits signal from temporally uncorrelated noise without hyperparameter tuning.

CaImAn: An open source tool for scalable Calcium Imaging data Analysis

CaImAn, an open-source library, automates motion correction, source extraction, deconvolution and cross-session registration for calcium imaging, detecting neurons near human level (F1 0.75 batch, 0.76 online).

Drug-Target Interaction prediction using Multi Graph Regularized Nuclear Norm Minimization

This paper introduces a method to predict drug-target interactions using multi graph regularized nuclear norm minimization, showing improved performance over existing methods.

Mechanistic insights of the attenuation of quorum-sensing-dependent virulence factors of Pseudomonas aeruginosa: Molecular modeling of the interaction of taxifolin with transcriptional regulator LasR

This study investigates how taxifolin interacts with the LasR transcriptional regulator of Pseudomonas aeruginosa using molecular docking, dynamics simulations, and machine learning to identify two binding modes.

Eigenvector centrality mapping for ultrahigh resolution fMRI data of the human brain

This paper introduces two new implementations of eigenvector centrality mapping for fMRI data to address computational and interpretational limitations of existing methods.

Visualizing and interpreting single-cell gene expression datasets with Similarity Weighted Nonnegative Embedding

The paper introduces SWNE, a method for visualizing single-cell gene expression data that improves local and global structure preservation compared to t-SNE.

Chromatin interactions and expression quantitative trait loci reveal genetic drivers of multimorbidities

The paper identifies genetic drivers of multimorbidities by analyzing chromatin interactions and eQTL data to uncover regulatory interactions across multiple phenotypes.

Clustering-based optimization method of reference set selection for improved CNV callers performance

This paper evaluates reference set selection methods for CNV callers using WES data and finds that clustering-based approaches improve detection performance and efficiency.

Molecular atlas of postnatal mouse heart development

This study provides a molecular atlas of postnatal mouse heart development by integrating transcriptomics, proteomics, and metabolomics data to identify metabolism-related mechanisms linked to the loss of cardiac regeneration.

Mass-spectrometry of single mammalian cells quantifies proteome heterogeneity during cell differentiation

SCoPE-MS combines acoustic cell lysis with isobaric tandem-mass-tag carrier channels to quantify over a thousand proteins in single mammalian cells, resolving cancer cell types and stem cell differentiation.

A probabilistic approach to discovering dynamic full-brain functional connectivity patterns

This paper introduces HTFA, a Bayesian hierarchical matrix factorization model, to discover dynamic full-brain functional connectivity patterns from fMRI data and shows it can decode cognitive states during storytelling and TV watching.

Protease target prediction via matrix factorization

The paper presents a matrix factorization approach for protease target prediction that integrates diverse biological data and outperforms existing methods.

Genetic regulatory mechanisms of smooth muscle cells map to coronary artery disease risk loci

The paper identifies five genes modulating coronary artery disease risk through smooth muscle cells by integrating eQTL and GWAS data with transcriptomic and epigenomic analyses.

Zero-preserving imputation of scRNA-seq data using low-rank approximation

Truncated SVD plus a per-gene threshold at the most negative value fills dropouts while leaving biological zeros alone, and runs on 50,000 cells in about 40 minutes.

Enter the matrix: factorization uncovers knowledge from omics

This paper reviews matrix factorization applications for systems-level omics analysis, highlighting their ability to uncover biological knowledge from high-dimensional data.

Functional gradients of the cerebellum: a fundamental movement-to-thought principle

The paper identifies two functional gradients in the cerebellum using diffusion map embedding on resting-state data, revealing a progression from motor to transmodal regions and task-unfocused to task-focused processing.

Predicting cell-cycle expressed genes identifies canonical and non-canonical regulators of time-specific expression in Saccharomyces cerevisiae

The paper predicts cell-cycle expressed genes in S. cerevisiae using regulatory data, identifying both canonical and non-canonical regulators of time-specific expression.

Combination of novel and public RNA-seq datasets to generate an mRNA expression atlas for the domestic chicken

The paper integrates public and new RNA-seq data to create a chicken mRNA expression atlas, revealing tissue-specific gene clusters and developmental patterns.