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  • scSeq Analysis - Seurat and OOBE usage

    Hi all,

    I have a specific stats question, that is a bit deep in the weeds (for me anyway, and also reveals some of my shortcomings, in terms of understanding the background statistical techniques regarding random forests…).

    I'm attempting to use the Out of Bag Error classification that is suggested in Seurat's pbmc33k code object on my own dataset. The general idea is to over cluster your single cell data and then merge the clusters that are very similar (as assessed by the out of bag error stat). I do have a couple of questions however:

    1) How does one assess how many nodes need to be merged? In the pbmc example, the authors choose 8, based on "High OOBE" scores. From reading a bit, however, it appears those scores are fairly dataset specific. Do you have
    any suggestions as to any methodologies for this choice? (I paste the OOBE results from my dataset below).

    2) Also, it seems possible that one could have overclustering within 2 (or more) transcriptionally distinct
    sets of cells (let's say within dendritic cells and within macrophages). Presumably, this means you'd have to merge twice, somehow? Once within DCs and once within Macs? Is this possible? My understanding is the list below is ranked by the OOBE and you could different nodes with high OOBE that arise from very different cell types that you’d not want to merge together?

    Thanks so much, in advance.

    All the best,

    Josh










    node oobe
    59 0.2836879433
    57 0.2592592593
    60 0.2268518519
    46 0.1829971182
    49 0.1818181818
    54 0.1736694678
    56 0.1533546326
    47 0.1404682274
    51 0.1338582677
    43 0.1255374033
    36 0.1246226822
    58 0.1213592233
    48 0.1197771588
    61 0.1168831169
    33 0.1158497772
    45 0.1148036254
    39 0.1109399076
    53 0.1069958848
    50 0.0796812749
    44 0.0705882353
    37 0.0672131148
    40 0.0599078341
    34 0.0588235294
    38 0.0510752688
    55 0.0474777448
    41 0.0471063257
    42 0.0340531561
    35 0.0196319018
    52 0.0122699387
    32 0.0006361323

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