scRNA-Seq datasets with labels

Question

[Eisuan]

Hello everyone,

I'd like to find some labelled scRNA-seq datasets (from chromium 10x possibly) where cell identity is inferred from a reliable sample manipulation methodology and not from scRNA-seq analysis only (e.g. classical Seurat analysis). This is because I need to compare the latter framework with a different one.

Thank you so much for your time and help in advance.

[benformatics]

https://www.nature.com/articles/s41590-021-01059-0

[Eisuan]

Many thanks for your helpful suggestion. I am sorry for the late answer. I checked their datasets: unfortunately, the only complete transcriptome dataset has a high mt content according to metadata: filtering for <10% mt genes over a dataset of 13k cells yields 34 cells, 15% 778 and 20% 5.7k. However, I think could use this data to test the effect of higher % mitochondrial genes (since the other datasets I am using are filtered using a 5% cutoff).

[benformatics]

I don't think you should filter so high for the MT content. It is dataset dependent where the threshold should be set.

5% is more typical in single-nucleus RNA-seq which this is not.

[ATpoint]

What is a ‚sample manipulation methodology‘ ?

[Eisuan]

I mean prior steps, like Fluorescence-activated cell sorting (FACS), but it doesn't have to be FACS. Machine learning-wise, I mean the procedure that was used for label generation (thus, prior cell population classification).