Hopefully I have posted this in the correct place.
I have recently started in a lab and have been tasked with helping look at some scRNA-seq data. This dataset includes ~30 manually isolated single cells, and these cells are assumed to be all of the same cell type.
After reading some papers and example workflows, it seems like many single cell experiments involve several hundreds to thousands of cells, which are then clustered into different cell types, with differential expression being used to identify 'marker genes' to represent the gene signatures of the different types, whereas we have manually isolated cells we are assuming to be of a certain type.
With ~30 cells which are supposed to be the same cell type, this approach would not seem to make sense. Prior to me starting, they had produced some gene lists with key genes they expect to see expressed in these cells which I have used to produce some expression heatmaps. There has also been the idea to get a list of genes associated with later stage tissue (these are stem cells) to use in a similar fashion.
I also found this workflow: https://www.bioconductor.org/help/wo...tic-stem-cells which uses 96 single cell HSCs (i.e. not a large population of different types of cells) which I thought might be more relevant to my dataset.
Is what we have already done + the linked workflow about as much as we can do with such a dataset? What else could we potentially pull from the data, regarding the transcriptome/expression profile of these cells?
Others I have talked to in person seem quite pessimistic about the type of information we could get (mainly due to low cell numbers), and think not much more could be done and this should be purely exploratory.
One other idea is if there is a chance we could get our hands on a dataset that has profiled on a similar cell type we could compare expression profiles?
Sorry for the long winded question.