Back

A rule-based data-informed cellular consensus map of the human mononuclear phagocyte cell space

Guenther, P.; Cirovic, B.; Bassler, K.; Haendler, K.; Becker, M.; Dutertre, C. A.; Bigley, V.; Newell, E. W.; Collin, M.; Ginhoux, F.; Schlitzer, A.; Schultze, J. L.

2019-06-03 immunology
10.1101/658179 bioRxiv
Show abstract

Single-cell genomic techniques are opening new avenues to understand the basic units of life. Large international efforts, such as those to derive a Human Cell Atlas, are driving progress in this area; here, cellular map generation is key. To expedite the inevitable iterations of these underlying maps, we have developed a rule-based data-informed approach to build next generation cellular consensus maps. Using the human dendritic-cell and monocyte compartment in peripheral blood as an example, we performed computational integration of previous, partially overlapping maps using an approach we termed backmapping, combined with multi-color flow-cytometry and index sorting-based single-cell RNA-sequencing. Our general strategy can be applied to any atlas generation for humans and other species.\n\nGraphical Abstract\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=110 SRC=\"FIGDIR/small/658179v1_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (18K):\norg.highwire.dtl.DTLVardef@c1b7b2org.highwire.dtl.DTLVardef@32b68org.highwire.dtl.DTLVardef@16e5fcorg.highwire.dtl.DTLVardef@1552028_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIDefining a consensus of the human myeloid cell compartment in peripheral blood\nC_LIO_LI3 monocytes subsets, pDC, cDC1, DC2, DC3 and precursor DC make up the compartment\nC_LIO_LIDistinguish myeloid cell compartment from other cell spaces, e.g. the NK cell space\nC_LIO_LIProviding a generalizable method for building consensus maps for the life sciences\nC_LI

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.