-
Notifications
You must be signed in to change notification settings - Fork 56
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
Help with reducing number of cells called #256
Comments
To me, sample number 3 looks like the kind of output I would expect. Sample 8 is not doing a great job calling cells, I agree with you. Can you post the first part of the cellbender log file, before the training starts, where cellbender says how many cells and empties there are, and estimates UMI counts in each? That will give me a better idea about what cellbender thinks it's seeing. |
Here is the beginning of cellbender log file: cellbender:remove-background: Command: |
Well things seem to look pretty much how I'd expect. It's not clear to me why we're not getting something a bit more reasonable... I am guessing a bit here, but could you try |
I think those settings improved it a bit, but it is still estimating ~50k cells instead of 30k. |
I have a log file from cellbender v0.2.2 that was able to estimate ~30k cells:
It shows similar priors for cells and empty droplets and count threshold. For v0.3.0, could it be that excluding 6-7k features estimated to have <= 0.1 background counts in cells be reducing the model complexity too much? How do I adjust that with Update: using |
Hi @racng , this is an interesting example. It does seem like cell probability inference is not working as well on this sample in v0.3.0 as it was in v0.2.2. (It is definitely the case that v0.3.0 does better than v0.2.2 on a lot of samples. But this seems to be an exception.) You are right that Is there any chance I could get a copy of that h5 file to try to experiment a bit and see what is going on? In the meantime, two other settings I'd try to just hope we can force the outcome we want...
|
@sjfleming I have just sent you an email via your Broad Institute email |
I have a particular sample that struggles to work well with cellbender. I am currently the latest cellbender v0.3.0. Its UMI curve has a weak knee structure. By eye, I am guessing there are around 30k cells but cellbender is overestimating that. I have tried running it with the default settings and also increasing the
total-droplets-included=50000
and setting alow expected-cells=10000
, but couldn't get the program to call cells at the expected levels. I have attached the html reports below for a sample that worked well (No. 3, default setting) vs. the sample having trouble (No. 8). Suggestions would be greatly appreciated! Thank you!output_report_8.html.zip
output_report_3.html.zip
The text was updated successfully, but these errors were encountered: