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stuart-lab / signac

R toolkit for the analysis of single-cell chromatin data
https://stuartlab.org/signac/
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Small Peaks equals large counts #978

Closed iloveless1 closed 2 years ago

iloveless1 commented 2 years ago

Hi,

I am analyzing a scATAC-seq dataset. After following preprocessing steps, I used the GeneActivity function to quantify the RNA expression based on the accessibility in the region. There are a lot of small fragments in the region in cluster 2 on the coverage plot for Spock1, but nothing discernably different from the other clusters. Because there are so many small fragments, the expression of Spock1 in this cluster is one of the top 10 differentially expressed genes. There are a handful of other genes that fall into this category as well. The biologist that I am working with stained for Spock1 and few other genes that follow the same pattern in her samples and wasn't able to find a discernible amount. Is there perhaps a preprocessing step or a parameter for GeneActivity that I missed that could help solve this problem?

Spock1

Thanks for your help.

timoast commented 2 years ago

There are a lot of small fragments in the region in cluster 2 on the coverage plot for Spock1, but nothing discernably different from the other clusters. Because there are so many small fragments, the expression of Spock1 in this cluster is one of the top 10 differentially expressed genes.

According to the expression plot along the side here (assuming that's gene activity), cluster2 has quite low Spock1 activity which is consistent with the coverage track. I think the DE results will depend on what you're comparing the cells to.