Consider a single cell RNA seq data with a control and a test sample. These are integrated to minimize the batch effect. Reads have been normalized to the sequencing depth. One can compare the samples for their cluster composition. Further one can perform cluster-wise differential expression followed by enrichment analysis to find out genes and pathways up or down regulated in the samples. One may also perform trajectory analysis to study transition between cell types.
Apart from this, are there any ways to compare and find differences between samples in a single cell RNA seq data?
Dear Tushar Deshpande
you have already mentioned the most popular analysis performed on scRNA seq data.
Additionally you can look at the composition of the clusters between the treatment and control samples. This publication might give more insights into this part- Article Current best practices in single‐cell RNA‐seq analysis: a tutorial
best wishes
Shambhabi
Shambhabi Chatterjee Thank you, I was already considering cluster composition. I am wondering what kind of statistics one would use while comparing cluster composition when there are no replicates (i.e. cluster composition information of control and test sample without any replicates).
Tushar Deshpande I had a similar question in my mind last year and had asked the RG community for inputs here- https://www.researchgate.net/post/Are_there_any_standard_methods_to_comapre_single_cell_RNA_seq_data_from_different_samples
So far I have not found any way to do statistics. As you rightly pointed one would need to have replicates which have been sequenced at the same round to really perform statistical analysis.
From what I have read , I think the changes in cluster composition can be described and discussed in qualitative fashion if there are no replicates.
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