Dear Aaron, and all, happy and healthy new year !
I was following the tutorial presented at :
may I ask, what function do you use in order to remove from a SCE object the barcodes with low total UMI counts
(i.e the empty droplets) ? thanks a lot !
-- bogdan
Looks like you solved your own problem. Note that 1% is just a threshold I've used; if you don't like that level of error, you can (and should) set it lower. For example, the BioC-devel versions of the workflows use 0.1%.
Thank you Aaron. If I may add a question about scRNA-seq analysis (of 10X_Genomics datasets) :
we have 3 experimental conditions to compare (i.e. 3 scRNA-seq datasets : control, treatment1, treatment2).
when I analyze each dataset, i could apply the pipelines that you comprehensively described at :
however, shall i compare the specific cell subpopulations in CONTROL vs TREATMENT1 (or TREATMENT2),
which workflow shall I follow (in order to be able to find differentially expressed genes in specific cells) ?
thanks a lot, happy weekend !
Follow the instructions in the batch workflow to merge all data sets together; cluster cells into subpopulations; and then perform differential expression between conditions within each cluster. This will allow you to identify, e.g., cell-type-specific changes in expression for each cluster. You can also test for differential abundance within each cluster to identify changes in population composition between conditions, a la mass cytometry. I will write up a more detailed workflow on how to do this next month.
Dear Aaron, thank you very much.
Having a workflow that will give a more detailed example on how to perform differential expression :
1) between conditions within each cluster, and 2) between conditions of different clusters,
would be very very helpful. For our scRNA data analysis, we would like to use at least 2 workflows :
-- an workflow based on SingleCellExperiment packages that you've written, including "scatter", and "scran"
-- an workflow based on Seurat, at outlined here : https://satijalab.org/seurat/immune_alignment.html
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version 2.3.6
... just found the function in the tutorial together with the explanations of the Monte Carlo procedure and the choice of FDR < 0.01 (sorry, 've overlooked it ..)