The identification of cell types in single cell genomics data (cell type annotation) can provide new insights into cellular identities, heterogeneity, and functional roles. Cell type annotation is challenging and time-consuming, but essential to meaningful interpretation of the data. Recently there has been progress in automating this process. These methods are intricately connected to efforts to construct cell atlases, which categorize cells into metastable states and developmentally fluid ones. Annotation often requires mitigation of technical confounders such as batch effects and cell multiplets.
In this seminar series, the presenters will demonstrate different approaches to cell type annotation in various complex tissues such as tumors, embryos, brain, and the immune system. The methods developed will be presented in the context of larger frameworks of bioinformatics tools that they have developed and applied to interesting problems in biology and medicine.
April 6, 2023 at 1 PM
Rahul Satija, D. Phil. Associate Professor of Biology, Center for Genomics Systems and Biology, New York University. Associate Faculty, Institute for Systems Genetics, NYU Langone Medical Center. Core Member, New York Genome Center
Azimuth is a web application that uses an annotated reference dataset to automate the processing, analysis, and interpretation of a new single-cell RNA-seq experiment. Azimuth leverages a ‘reference-based mapping’ pipeline that inputs a counts matrix of gene expression in single cells, and performs normalization, visualization, cell annotation, and differential expression (biomarker discovery). All results can be explored within the app, and easily downloaded for additional downstream analysis. – Satija Lab
The development of Azimuth is led by the New York Genome Center Mapping Component as part of the NIH Human Biomolecular Atlas Project (HuBMAP).
May 25, 2023 at 1 PM
Prof. Dr. Fabian Theis Director of the Computational Health Center, Director of the Institute for Computational Biology, Helmholtz Munich
Computational trajectory inference enables the reconstruction of cell state dynamics from single-cell RNA sequencing experiments. However, trajectory inference requires that the direction of a biological process is known, largely limiting its application to differentiating systems in normal development. Here, we present CellRank (https://cellrank.org) for single-cell fate mapping in diverse scenarios, including regeneration, reprogramming and disease, for which direction is unknown. http://doi.org/10.1038/s41592-021-01346-6
June 1, 2023 at 1 PM
Chuan Xu, Ph.D. (from Sarah Teichmann lab)
Cell Typist: Automated Cell Type Annotation. CellTypist is an automated cell type annotation tool for scRNA-seq datasets on the basis of logistic regression classifiers optimised by the stochastic gradient descent algorithm. Through CellTypist, cell type labels can be transferred from the built-in models (with a current focus on immune cell types) or any user-trained models to the query data. – Teichmann Lab
June 22, 2023 at 1 PM
Mallar Bhattacharya, M.D., M.S. Associate Professor of Medicine, School of Medicine, UCSF
At steady state, resident macrophages maintain disparate aspects of tissue homeostasis relevant to tissue-specific function. For example, without resident alveolar macrophages, surfactant turnover and lung immunity are both compromised with life-threatening consequences. In the setting of injury, both sterile and infectious, multiple myeloid populations are recruited to peripheral tissues, and this heterogeneity has recently come under sharper focus with the granular analysis afforded by single cell sequencing both in mouse and human.
The goal of our laboratory is to define the functional properties of these subgroups of macrophages, and the central hypothesis is that direct contact between macrophages and tissue-specific stakeholders–fibroblasts in the remodeling lung and lung vasculature in sepsis and vascular leak among myriad examples–drives disease outcomes. – Bhattacharya Lab