Lior S. Pachter

clrcycle is a dimensionality reduction method for cyclic data that captures phase and magnitude in biological systems, showing effectiveness in circadian and cell-cycle analysis.

The paper develops a model to distinguish biological from technical spatial variability in spatial transcriptomics data and introduces the stipple software for analysis.

The paper introduces a framework to determine cis or trans regulation contributions to gene expression differences between strains or species and applies it to yeast, human-chimpanzee hybrid, and mouse data.

This paper introduces concordex, a method to identify spatially homogeneous regions in tissues by decomposing gene expression patterns into cell-type and spatial variation, demonstrating its effectiveness across datasets.

fastQpick generates bootstrap replicates of FASTQ reads for uncertainty quantification in sequencing data.

The paper uses hybrid crosses to analyze regulatory variation in mouse tissues, finding cell-type-specific trans-acting effects and highlighting the limitations of bulk tissue analysis.

The paper adapts kallisto-bustools for bacterial scRNA-seq to enable uniform preprocessing and analysis of microbial single-cell transcriptomics.

This paper extends contrastive PCA by introducing spatial and functional variants, applying them to analyze gene expression in cancer and immune response to vaccination.

The paper derives a spectral criterion for the evolution of cooperation, showing that cooperation evolves when the leading eigenvalue of an interaction operator exceeds the benefit-to-cost ratio.

This study compares Seurat and Scanpy, finding significant differences in their scRNA-seq analysis outputs, comparable to low sequencing depth or cell coverage.

CellSweep removes technical contamination from single-cell genomics data, improving accuracy in downstream analysis.

The paper introduces edgePython, a Python version of edgeR for differential expression analysis in single-cell genomics, adding a negative binomial-gamma mixed model and empirical Bayes shrinkage.

This paper develops a machine-automated approach to extract results from scientific papers and evaluates it using the eLife corpus, highlighting the need for machine-readable scientific dissemination.

The paper introduces accumap, a statistical inference method that improves spatial transcriptomics array reconstruction compared to UMAP.

The paper characterizes transcriptomes across 8 tissues in 8 mouse strains to identify expression variation linked to genetic differences and phenotypic traits.

The paper develops lr-kallisto, a tool for accurate long-read transcriptome quantification using ONT data, showing improved results with exome capture.

This study estimates biophysical parameters of gene expression in multiple vertebrate species and finds coordinated evolution under evolutionary systems drift and additional constraints on transcriptional bursting.

The paper introduces k-spaces, a probabilistic framework for simultaneous dimension reduction, clustering, and latent space learning, demonstrating its application in genomic settings.

The paper introduces ember, an entropy-based framework to assess gene specificity in single-cell RNA sequencing data, revealing unexpected patterns in gene expression across tissues and mouse strains.

This paper introduces rho PCA, a method that improves upon contrastive PCA by approximating the Rayleigh quotient, offering better accuracy and efficiency for dimension reduction in genomics data.

The paper develops a biophysical model to jointly analyze single-cell RNA and protein data, demonstrating its utility with simulated and 10x multiomic data.

varseek improves variant detection in challenging cases like insertions, deletions, and low-coverage data using a reference-based approach.

This paper proposes a model to normalize scRNA-seq data by distinguishing between biological and technical extrinsic noise, showing that extrinsic noise explains baseline overdispersion.

The paper introduces a pseudoassembly method using colored de Bruijn graphs to identify cell-type specific de novo variants from single-cell RNA-seq in a mouse melanoma model.

The paper detects viral sequences in single-cell data to identify novel viruses affecting host gene expression in rhesus macaques.

This study uses spline regression to analyze longitudinal RNA-seq data and identifies isoform switching events that are missed in traditional gene-level analyses.

SpatialFeatureExperiment provides a Bioconductor package for handling spatial-omics data, integrating spatial analysis with single-cell omics workflows.

This paper introduces Chronocell, a method for trajectory inference using process time modeling, showing it can interpolate between trajectory inference and clustering and assess dataset suitability.

This paper proposes a biophysical model for analyzing ATAC-seq data and evaluates its benefits for integrating multiome data with single-cell RNA-seq.

The paper describes using kallisto, bustools, and kb-python to quantify RNA-seq data from bulk, single-cell, and single-nucleus samples.