r3fang
commented
5 years ago Hi there,
SnapATAC does not perform topic modeling, instead, it uses other type of dimensionality reduction technique that does not report the feature importance. Instead, you can use the function called findDAR to identify the differential peaks between clusters.
On Sep 13, 2019, at 10:40 AM, StatsAreForLosers notifications@github.com wrote:
Is there a place that snapATAC stores the feature topic probabilities matrix? I understand that LDA outputs a matrix of the probabiltiy a selected document (cell) has a specific topic (set of features) and from your source code that is stored in obj@smat@dmat. However, I am also curious if there is an output of the probability each future (genomic region) is used in the topic (set of features)? This can be useful in trying trying to find what genomic regions are distinguishing each topic and seeing which topics are enriched in each cluster.
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Is there a place that snapATAC stores the feature topic probabilities matrix? I understand that LDA outputs a matrix of the probabiltiy a selected document (cell) has a specific topic (set of features) and from your source code that is stored in
obj@smat@dmat. However, I am also curious if there is an output of the probability each future (genomic region) is used in the topic (set of features)? This can be useful in trying trying to find what genomic regions are distinguishing each topic and seeing which topics are enriched in each cluster.