Leveraging the Cell Ontology to classify unseen cell types

Abstract

Single cell technologies are rapidly generating large amounts of data that enables us to understand biological systems at single-cell resolution. However, joint analysis of datasets generated by independent labs remains challenging due to a lack of consistent terminology to describe cell types. Here, we present OnClass, an algorithm and accompanying software for automatically classifying cells into cell types that are part of the controlled vocabulary that forms the Cell Ontology. A key advantage of OnClass is its capability to classify cells into cell types not present in the training data because it uses the Cell Ontology graph to infer cell type relationships. Furthermore, OnClass can be used to identify marker genes for all the cell ontology categories, regardless of whether the cell types are present or absent in the training data, suggesting that OnClass goes beyond a simple annotation tool for single cell datasets, being the first algorithm capable to identify marker genes specific to all terms of the Cell Ontology and offering the possibility of refining the Cell Ontology using a data-centric approach.

The Tabula Sapiens: A multiple-organ, single-cell transcriptomic atlas of humans

“…S1). Tissue experts used a defined cell ontology terminology to annotate cell types consistently across the different tissues ( 13 ), leading to a total of 475 distinct cell types with reference transcriptome profiles (tables S2 and S3). The full dataset can be explored online with the cellxgene tool through the Tabula Sapiens data portal ( 14 ).…”

Reprogrammed Schwann Cells Organize into Dynamic Tracks that Promote Pancreatic Cancer Invasion

“…We first assessed the relationship of SCs with pancreatic cancer patient outcomes. We obtained several signatures of human SCs, including myelinating SC and non-myelinating SC signatures from the Tabula Sapiens portal using OnClass (30) (Fig. 1A).…”

“…Signatures for SCs were obtained from Tabula-Sapiens using OnClass(30) (http://tabula-sapiens-onclass.ds.czbiohub.org/), from PanglaoDB (45)(https://panglaodb.se/markers.html), and from a single cell analysis study of the pancreas(46). The activities of SC and other pathways were compared across tumor samples using a newly developed IPAS scores (31) (https://calina01.u.hpc.mssm.edu/pathway_assessor/).…”

The Tabula Sapiens: a multiple organ single cell transcriptomic atlas of humans

“…We collected multiple tissues from individual human donors (designated TSP [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15] and performed coordinated single cell transcriptome analysis on live cells (12). We collected 17 tissues from one donor, 14 tissues from a second donor, and 5 tissues from two other donors (Fig.…”

“…S1 ). Tissue experts used a defined cell ontology terminology to annotate cell types consistently across the different tissues ( 13 ), leading to a total of 475 distinct cell types with reference transcriptome profiles ( tables S2, S3 ). The full dataset can be explored online with the cellxgene tool via the Tabula Sapiens data portal ( 14 ).…”