New “discrete state” model of hematopoiesis published in Nature Immunology. https://t.co/E1bSt1cGN7 We propose a hierarchical model of hematopoiesis where stable “discrete states” serve as key regulatory nodes. With @nsalomonis @morris_lab @BioLegend @CytekBio @NIH @NIDDKgov and

pyInfinityFlow is maintained on GitHub ( https://t.co/3fACUCpShv) and provides both command-line execution and a Python API, which are documented using readthedocs: https://t.co/mE0wRGn6bq (6/6)

pyInfinityFlow generated Infinity Flow objects can be saved as FCS files and loaded into #flowjo, or other Flow Cytometry analysis software, to carry out in-silico gating side-by-side with UMAP coordinates to visualize the enrichment of target cell populations: (5/6)

We illustrated the ability to distiguish rare transcriptionally defined mouse lung cell populations by mapping an Infinity Flow dataset to an analagous scRNA-seq dataset from the #LungMAP project: https://t.co/Stk1KUsQFw (4/6)

We show that this implementation improves in speed and memory efficiency, allowing for imputation of hundreds of features across larger numbers of cells, which is necessary to profile rare and transitional cell populations: (3/6)

This package extends the Infinity Flow protocol to allow for more complex configurations. Data is held in AnnData Python objects to enable seamless integration with popular single-cell omics tools, such as #scanpy and #pytometry. (2/6)

We are excited to announce the publication of our new #Bioinformatics application! #pyInfinityFlow, enables combination of overlapping Flow Cytometry panels, unlocking the potential to profile more features across millions of cells: https://t.co/nbCaOTJcaA (1/6)

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