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scATAC-seq analysis, data preprocessing
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15 months ago
chipolino • 40

Hi,

During scATAC-seq data preprocessing, does it make sense to filter data matrix, so it contains only most variable peaks (in the same way how we do it for scRNA-seq), before any further dimensionality reduction or clustering analysis?

Thanks

scATAC-seq • 166 views
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To better define cell types, it makes sense.

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That depends on the type of analysis you're referring to. PCA, for example, will always focus on the most variable regions. I haven't looked at scATAC-seq data myself but given that it's basically binary, I'm not sure how well the typical variance measures even hold up.

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can I do sparse PCA on scATAC-seq matrix and see, what peaks correspond to, let's say the first component? And choose those as the most informative (variable)?

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Well, I'm not sure how "peaks" would be defined in scATAC-seq as there's a maximum of 2 reads per open region per cell. Maybe you want to collapse the information from multiple cells at the same region? What exactly is the question you're trying to address?

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Usually, dimensionality reduction is done on top variable features (usually top 500). So you can take top variable peaks and build a PCA and see how the tSNE clusters looks like. If you want to overcome the sparsity of data, you could use KNN approach to merge data from n-similar cell. Before doing that I would check tSNE on top 500 variable peaks.

I did not know that the data is binary, so this paper seems to have a nice method to process the data.

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Thanks! But how do you find most variable peaks, if the data is binary?

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Sorry I am not aware that it's binary. I updated my answer and moved it to comment as it doesn't qualify as an answer anymore

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Asked on BioC in the first place and then cross-posted here as suggested there.

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