Hi Everyone, I am very new to scRNA-seq analysis, and I'm trying to analyze some scRNA-seq data. The goal of the analysis is to compare lncRNA and mRNA genes by calculating:- overall mean expression of lncRNA genes and mRNA genes - and expression levels f lncRNA genes and mRNA genes.
So far, I performed the following on my data: QC analysis, normalization, identification of differentially expressed genes, scaling and regressing out some housekeeping gens - all done using the Seurat pipeline in R.
Please I do not know what do do next from here and seem to be stuck on what to do. Also if anyone once could recommend a good easy to follow tutorial on how to go about this, it would be appreciated.
Any feedback would be most appreciated. Thanks.
Kedu
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The
AverageExpressionfunction in Seurat will provide you the mean expression of genes/features.Thank you Arup for the pointer. I clustered my cells using tSE and UMAP, and each time I run the "AverageExpression" Function it calculates for each cluster. Is there a way to undo the clustering as I want to analyze just the lncRNA and mRNA gene expression, and not theclusters.
Thank you
Load data > NormalizeData > AverageExpression.Thank you Arup. I was able to undo the clustering... I know this may sound trivial, but please any pointers on what to do next? I'm finding it difficult to get a workflow.
On paper what I had planned to do was:
Load data > Process data (normalize data, scale data, regress out wanted genes) > Perform dimensionality reduction > Identify lncRNA gene and mRNA gene > Separate them into two groups > Analyze each group (look at overall mean expression levels, non-zero mean expression levels, look at number of cells where genes are expressed, and look at the most abundantly expressed genes in each set) > Compare analytical results of each set.
I don't know if this is the way to go about it? Right now I'm stuck trying to identify and separate the lncRNA genes and mRNA genes.
Thank you