Hi all,
Interesting thread, thanks for linking tools. I'd also like to highlight Marker Enrichment Modeling (MEM), a tool my group developed for learning cell identity.
The approach in MEM goes beyond traditional heatmaps of the median intensity (or staring at t-SNE plots) in a couple ways: 1) the value assessed is an enrichment score that is platform independent (we show comparison of fluorescence and mass cytometry where the same enrichment scores are obtained) and 2) MEM also creates an ordered text label that is a "compressed digest" of the apparent identity / special features of that subset (and generally more quickly readable than a heatmap).
MEM code and examples on GIthub here:
https://github.com/cytolab/memThis is from a class we teach on cell identification approaches and it has a lot more than the minimum needed for MEM, including multiple R markdowns, an install script for MEM, UMAP, t-SNE, and FlowSOM, and a few small FCS files. I believe it's about 20MB in total with 3 sets of example FCS files from publications.
This version of MEM produces a few different ways of viewing the data, including:
- Heatmaps of the MEM enrichment scores, median expression, and interquartile ranges for all populations
- A human and machine readable text "MEM label" as in:
"1 : UP CD4+4 CD3+4 • DN CD16-9 CD8-7 CD11c-5 HLA-DR-5 CD69-3"
This is for CD4 T cells. The scores go from +10 (max enriched) to 0 (no difference) to -10 (max excluded). (For those who have followed MEM: there is now also a "reference-less" version of MEM that can be tested in the examples on Github.)
This code also shows examples of comparing MEM labels generated from FlowSOM analysis of UMAP vs. FlowSOM analysis of t-SNE vs. expert gates (i.e., do we get the same phenotype and MEM label if we analyze 3 different ways?).
The original publication for MEM is:
Diggins et al., Nature Methods 2017
Characterizing cell subsets using marker enrichment modeling
https://www.ncbi.nlm.nih.gov/pubmed/28135256There is also a protocol walking through a few examples:
Diggins et al. Current Protocols in Cytometry 2018
Generating Quantitative Cell Identity Labels with Marker Enrichment Modeling (MEM)
https://www.ncbi.nlm.nih.gov/pubmed/29345329You can see uses of MEM and comparison of MEM labels recent publications, including:
Greenplate et al., Cancer Immunology Research 2019
Computational Immune Monitoring Reveals Abnormal Double-Negative T Cells Present across Human Tumor Types.
https://www.ncbi.nlm.nih.gov/pubmed/30413431Feel free to ping me here or in email if anyone has questions or needs help getting it working. There's a new "try your data" script we're developing if you want to test it out on your files.
Cheers,
-Jonathan Irish @ Vanderbilt