STSM: Advancing in silico myeloid cell deconvolution from bulk transcriptomics

Host Institution: Technical University of Munich, Germany

Supervisor/Coordinators: Markus List

Instructors: Markus List, Francesca Finotello, Javier De Las Rivas

Timeframe: 13th February to 16th February 2023

Attendees: On-site: 10x WG 1 members, Virtually: 5x WG 1 and/or WG 2 members

Purpose: Building on existing methods and software tools, we will investigate in how much detail myeloid cells can be differentiated (e.g. M1 and M2 macrophages) reliably using simulated pseudo-bulk data sets created from single-cell RNA-seq data, where we can systematically assess detection limits and spillover effects. Where available, we will also use experimental gold standard data sets (e.g. matched bulk RNA-seq and FACS data). For this STSM, we will focus on bulk data with varying difficulty, e.g. one example of PBMCs, cancer and auto-immune disease for which well-annotated single-cell data sets are available. As an outcome, we envision establishing best practices for the deconvolution of myeloid cells.

Tasks: It is our aim to identify the best computational strategy to quantify myeloid subsets in bulk transcriptomics data using in silico deconvolution.

Selection Criteria:

WG 1 members: Expertise in bioinformatics and/or bulk as well as single-cell RNA-seq data sets. Preferably prior experience with in silico cell type deconvolution

WG 2 members: Expertise in myeloid cell immunology in particular with the ability to assess the quality of cell-type specific gene signatures and the interpretation of cell subtypes corresponding to single-cell RNA clusters.


  • Availability from February 13 at 14:00 h until February 16th 13:00 h.
  • Notebook / laptop and skills in R / python to engage in data processing and application of in silico cell type deconvolution tools, plotting of results.
  • WG 1 members should have identified candidate data sets ready for analysis BEFORE the STSM.

Expected Outcome: Based on our work during the STSM we will write a white paper and possibly a manuscript on best practices for myeloid cell type deconvolution.

Application Deadline: 31st December 2022

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