Kipoi - Seminar

The monthly virtual seminar series is designed as a platform for interested Kipoi users and developers and will host talks on the applications of deep learning on biological data. The seminar is held on every first Wednesday of the month at 5:30 p.m. - 6:30 p.m. CET. We are also happy to share the recordings of the seminar on YouTube.

How to take part

The Virtual Seminar Series takes place via Zoom. To take part in the seminar, you can register for the online Zoom conference here. Your personal join link will be valid for all upcoming lectures of the series.

How to apply as a speaker

The seminar is a great opportunity to present your recent work to a large international audience. If you want to apply as a speaker, please use the contact in the registration confirmation email.

Next seminar

Title: Learning cell communication from spatial graphs of cells - Node centric expression models (NCEM)
6 July 2022 5:30 p.m. - 6:30 p.m. Central European Time

Speaker: Anna Schaar, Helmholtz Zentrum Munich

Abstract:

Spatial molecular profiling assays have enabled single-cell genomics to move from studying tissue heterogeneity to tissue organization, providing new avenues to study cellular communication within a tissue. Yet, with the new information comes additional complexity: deriving insights from this data requires a new set of analysis tools. We present a computational method based on graph neural networks which reconciles disentanglement of gene expression variation and cell communication modeling: node-centric expression modeling (NCEM). The NCEM approach is not limited to targeted spatial transcriptomics data, but can be extended to spot transcriptomics if within-cell-type variation can be recovered in deconvolution analyses. The statistical cell–cell dependencies discovered by NCEM are plausible signatures of known molecular processes underlying cell communication. We show that NCEM's cell type coupling analysis workflow and the identification of receiver and sender effects across multiple datasets finds plausible putative dependencies and niche-dependent cell state variation on the example of human lymph nodes, inflamed colon and colorectal cancer in both targeted and spot transcriptomics data.

Upcoming speakers

  • 1 June 2022 - Yoshua Bengio, Université de Montréal, Mila – Quebec Artificial Intelligence Institute

Previous speakers

The scientific committee