Northstar enables automatic classification of known and novel cell types from tumor samples

Abstract

Cell atlases are revolutionizing our understanding of tissue and disease heterogeneity, yet most single-cell transcriptomic analyses on tumors are not leveraging atlases effectively. We developed northstar, a computational approach to classify cells in tumor datasets guided by but not restricted by previously annotated cell atlases. To benchmark northstar, we transferred annotations from a human brain atlas to a published dataset on glioblastoma and could recapitulate the tumor composition accurately and within seconds. We then collected 1,622 cells from 11 pancreatic tumors and could robustly identify healthy pancreatic and immune cells and neoplastic cell states. Three cell populations were shared across patients while five were private to a single sample. northstar's cell type classification offered rapid insight into the origins of neuroendocrine and exocrine tumors and fibromatosis. northstar is a useful tool to classify single-cell transcriptomes into known and novel cell types in the age of cell atlases.

Diverse homeostatic and immunomodulatory roles of immune cells in the developing mouse lung at single cell resolution

“…B cell receptors were assembled using BraCeR ( Lindeman et al, 2018 ) with the parameter –IGH_networks, which agreed with our in-house pipeline consisting of Basic ( Canzar et al, 2017 ) and Change-O ( Gupta et al, 2015 ). Northstar was used to compare our data with Tabula Muris ( Tabula Muris Consortium et al, 2018 ; Zanini et al, 2020 , https://www.biorxiv.org/content/10.1101/820928v2 ), while manual merging and embedding was performed to compare our data with Schyns et al, 2019 . Raw fastq files, count tables, and metadata are available on NCBI’s Gene Expression Omnibus (GEO) website (GSE147668), and the gene count and metadata tables are also available on FigShare at https://figshare.com/articles/Diverse_homeostatic_and_immunomodulatory_roles_of_immune_cells_in_the_developing_mouse_lung_revealed_at_single_cell_resolution/12043365 .…”

A comparison of data integration methods for single-cell RNA sequencing of cancer samples

“…Previous single cell batch correction benchmarking studies evaluated algorithm performance on simulated data or datasets derived from healthy tissues and peripheral blood mononuclear cells [4][5][6] . Despite an abundance of data, no single cell batch-effect correction methods are designed for or benchmarked on single cell datasets containing malignant cells 7 . Due to the inherent biological complexity both within and between tumours, these samples present unique technical challenges for batch correction that are not represented in previous benchmarking efforts.…”

Disruption of a GATA2, TAL1, ERG regulatory circuit promotes erythroid transition in healthy and leukemic stem cells

“…A similar circuit, comprised of GATA2, TAL1, and FLI1 (an ETS TF closely related to ERG) has been previously reported during embryonic HSC specification 69 , while GATA1, TAL1 and KLF1 form a sub-circuit in erythroid cells 70 . Indeed, recycling of regulatory modules is a key feature of developmental networks 38 , underlining the utility of cell classification strategies such as northstar 54 .…”

“…We reasoned that a more sophisticated feature selection together with soft guidance from healthy marrow data could reveal additional hidden heterogeneity. We therefore switched from unsupervised clustering to northstar, a semi-supervised clustering algorithm that leverages information from training data to channel the axes of heterogeneity during feature selection, graph construction, and cell community detection54 . Using healthy marrow transcriptomes6…”