Viktor Petukhov

Impact of Segmentation Errors in Analysis of Spatial Transcriptomics Data.

This paper shows segmentation errors in spatial transcriptomics data confound downstream analysis and proposes a method to mitigate their impact.

Single-Cell Atlas Uncovers Layer 4 Heterogeneity and Functional Gradients in Rhesus Macaque Visual Cortex

The paper uses snRNA-seq to analyze layer 4 heterogeneity in rhesus macaque visual cortex, identifying distinct subtypes and linking them to neurodevelopmental disorders.

Gene panel selection for targeted spatial transcriptomics

This paper introduces gpsFISH, a method for selecting gene panels in spatial transcriptomics that outperforms existing methods by accounting for platform effects and incorporating cell type hierarchies.

Selective vulnerability of supragranular layer neurons in schizophrenia

The paper identifies selective vulnerability of supragranular layer neurons in schizophrenia through single nucleus RNA sequencing and histological analysis.

Reference-based cell type matching of spatial transcriptomics data

This study evaluates six algorithms for cell type matching in spatial transcriptomics data and presents consensus strategies for improved analysis.

Case-control analysis of single-cell RNA-seq studies

Cacoa packages statistical tests, exploration and plots for comparing groups of scRNA-seq samples, aimed at inter-individual variability, small cohorts and high-dimensional data.

Bayesian segmentation of spatially resolved transcriptomics data

This paper introduces Baysor, a Bayesian segmentation tool for spatial transcriptomics data that improves cell identification and reduces contamination across multiple protocols.

Wiring together large single-cell RNA-seq sample collections

The paper introduces Conos, a method for joint clustering of large single-cell RNA-seq datasets to identify recurrent cell subpopulations across multiple samples.

RNA velocity in single cells

Unspliced versus spliced mRNA in standard scRNA-seq gives RNA velocity, a per-cell vector predicting future state over hours, validated in neural crest and used to trace mouse hippocampal lineages.

Accurate estimation of molecular counts in droplet-based single-cell RNA-seq experiments

The paper introduces a pipeline for processing droplet-based single-cell RNA-seq data to improve molecular count estimation by correcting barcode biases and sequencing errors.