These workshops are free and open to all researchers at Harvard University and affiliated institutions.

• Workshops on bioinformatics methods & related skills.
• Once a month for 3 hours
• Free and open to everyone at Harvard University and its affiliates
• Will meet online via Zoom
• Sign up at the links below to receive the workshop Zoom link

For the Fall 2021 Current Topics in Bioinformatics series, we are partnering with The Cancer Data Science Center at DF/HCC to offer a series of exciting talks on single-cell genomics. Leaders in the fields of scRNA-seq, scATAC-seq, and single-cell multimodal analyses at Harvard will present their newest tools, including a demo or tutorial.

Workshop description:

Applications for single cell multimodal analyses

Dr. Cliff Meyer with demo by Allen Lynch: MIRA: Joint regulatory modeling of multimodal expression and chromatin accessibility in single cells 

Rigorously comparing gene expression and chromatin accessibility in the same single cells could illuminate the logic of how coupling or decoupling of these mechanisms regulates fate commitment. In this talk, we present MIRA: Probabilistic Multimodal Models for Integrated Regulatory Analysis, a comprehensive methodology that systematically contrasts transcription and accessibility to infer the regulatory circuitry driving cells along developmental trajectories. MIRA leverages joint topic modeling of cell states and regulatory potential modeling of individual gene loci. MIRA thereby represents cell states in an efficient and interpretable latent space, infers high fidelity lineage trees, determines key regulators of fate decisions at branch points, and exposes the variable influence of local accessibility on transcription at distinct loci.

Dr. Jull Lundell, Dr. Kelly Street: Where did my tumor cells go? A better way to clean CyTOF data using ‘cytofQC’.

Abstract: Cytometry by time of flight, or CyTOF, is a powerful alternative to flow cytometry for quantifying targets on the surface and interior of cells. CyTOF data requires considerable cleaning because many observations are debris, doublets, or calibration beads. As with any technology, the data analysis is only as good as the data itself so careful data cleaning is essential. One of the biggest data cleaning challenges is dealing with doublets because it is difficult to distinguish between large cells and doublets.

We will provide a brief introduction to CyTOF and some of the problems with common data cleaning methods. We will then demonstrate ‘cytofQC’, an R package that uses machine learning to label observation types. This method mitigates many of the problems with common data cleaning practices because quality labels can annotate problematic observations without removing valuable data. In our case, this can be used to determine which cells may be true doublets and which are large cells.