Alexander J. Tarashansky

A Cross-Species Generative Cell Atlas Across 1.5 Billion Years of Evolution: The TranscriptFormer Single-cell Model

The paper introduces TranscriptFormer, a generative model trained on 112 million cells across 12 species, demonstrating cross-species cell type classification and disease state prediction.

Tutorial: guidelines for manual cell type annotation of single-cell multi-omics datasets using interactive software

The paper provides a framework and interactive software tools for manually annotating cell types in single-cell multi-omics datasets, demonstrating how different modalities can be combined to improve annotation accuracy.

Mouse lemur transcriptomic atlas informs primate genes, mutations, physiology, and disease

This paper uses a mouse lemur transcriptomic atlas to identify new genes, splice junctions, and disease-related features, highlighting their relevance to primate biology and human disease.

Tabula Microcebus: A transcriptomic cell atlas of mouse lemur, an emerging primate model organism

The paper creates a comprehensive cell atlas of mouse lemurs using single-cell RNA sequencing, identifying over 750 cell types and comparing their molecular profiles to humans, mice, and macaques.

Profiling cellular diversity in sponges informs animal cell type and nervous system evolution

The paper identifies 18 cell types in sponges, including neuroid cells with synaptic gene expression, suggesting early neuro-immune system evolution.

A cell atlas of the fly kidney

The paper characterizes cell types in the fly kidney using snRNA-seq and identifies new clusters, markers, and signaling networks, linking them to mammalian and planarian kidney structures.

Mapping single-cell atlases throughout Metazoa unravels cell type evolution

The paper uses SAMap to compare single-cell atlases across species, identifying homologous cell types and revealing ancient cell families in animal evolution.

Single-cell analysis of Schistosoma mansoni reveals a conserved genetic program controlling germline stem cell fate

Self-assembling Manifolds in Single-cell RNA Sequencing Data

The paper introduces the self-assembling manifold algorithm to improve gene relevance quantification and dimensionality reduction in single-cell RNA sequencing data, showing it outperforms existing methods in identifying stem cell populations and across multiple datasets.