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
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