Novel technology that allows to detect precisely the cell types and understand how they are organised in such multi-combined cell systems as brain is really awesome. However, due to high complexity of the experiment procedure it is really not easy to find correct overcomes for multiple problems in research project with scRNA-seq. An interesting aspect is the selection of appropriate data analysis methods and tools. There was a nice recent publication in Genome Biology about this. Glad to note that Qualimap2 was mentioned there, stating that even though it is working on multiple datasets,however was not designed for scRNA-seq and additional precise quality control is required for such data. And I totally agree with this statement.
Actually, a good overview for scRNA-seq quality control can be found in a recent tutorial publication in f1000. Additionally, there is a nice detailed course available from University of Cambridge Bioinformatics training unit.
In general scRNA data analysis procedure has many issues that might lead to errors and completely different final results can be produced by various tools on the same data. A known example: comments about the publication describing the tool for cell-cycle heterogenity normalization. Two recent publications provide rather useful status of scRNA-seq analysis procedure in my opinion. First one is "Disentangling neural cell diversity using single-cell transcriptomics" by JF Poulin et al. This manuscript gives an overview about current status in scRNA-seq data analysis and advices about experiment strategy selection. Second one is "Comprehensive Classification of Retinal Bipolar Neurons by Single-Cell Transcriptiomics" by K Shekhar et al. There is a full description of the analysis procedure of ~25000(!) bipolar cells (retina, visibility) including source code to reproduce it. In my opinion, such description should become a standard.
Also, there are already various resources about scRNA-seq tools. For example, here's a list in special github repo from Linnarsson lab. Of course, there are many other places with such information. So, if there are any additional confident resources really useful for this topic - will be glad to see the comments ;)
Actually, a good overview for scRNA-seq quality control can be found in a recent tutorial publication in f1000. Additionally, there is a nice detailed course available from University of Cambridge Bioinformatics training unit.
In general scRNA data analysis procedure has many issues that might lead to errors and completely different final results can be produced by various tools on the same data. A known example: comments about the publication describing the tool for cell-cycle heterogenity normalization. Two recent publications provide rather useful status of scRNA-seq analysis procedure in my opinion. First one is "Disentangling neural cell diversity using single-cell transcriptomics" by JF Poulin et al. This manuscript gives an overview about current status in scRNA-seq data analysis and advices about experiment strategy selection. Second one is "Comprehensive Classification of Retinal Bipolar Neurons by Single-Cell Transcriptiomics" by K Shekhar et al. There is a full description of the analysis procedure of ~25000(!) bipolar cells (retina, visibility) including source code to reproduce it. In my opinion, such description should become a standard.
Also, there are already various resources about scRNA-seq tools. For example, here's a list in special github repo from Linnarsson lab. Of course, there are many other places with such information. So, if there are any additional confident resources really useful for this topic - will be glad to see the comments ;)
