Closed DomenicoSkyWalker89 closed 1 year ago
Hi Domenico,
This dataset looks pretty hard, I think you can try it with the following two parameters:
I hope that helps.
Best, Tao Ren
Hi Tao Ren,
Thanks for the suggestions. I resolved at least in part the problem reaching now the following result that was not to bad:
Number of examples rejected= 461314 / 959396 num_of_rejcted NR 238494 R 222820 Name: count, dtype: int64 --- without rejection --- precision recall f1-score support
NR 0.70 0.79 0.74 552881
R 0.66 0.55 0.60 406515
accuracy 0.69 959396
macro avg 0.68 0.67 0.67 959396 weighted avg 0.68 0.69 0.68 959396
--- with rejection --- precision recall f1-score support
NR 0.81 0.89 0.85 314387
R 0.77 0.75 0.71 183695
accuracy 0.80 498082
macro avg 0.79 0.77 0.78 498082 weighted avg 0.80 0.80 0.80 498082
---test time: 5.945091724395752 seconds ---
ps sorry but I missed to say that these data did not arise from scRNA-seq but from flow and mass cytometry.
Best,
Domenico
Hello guys,
I found some insistences after PENCIL analysis. Briafly, I divided my cells into two groups R and NR, but after ran of PENCIL I observed a reductin of precision with rejection for the R group but not for the NR (attached image).
Furthermore plotting 'predicted_labels' were colored only NR and rejected cells but not the R ones.
Have you any suggestions to improve R precision and have some cells into the image? Than you very much for your help.
Best,
Domenico
PENCIL parameters (I modified the shuffle rate to reduce the rejection):
shuffle_rate=1/4, lambda_L1=1e-5, lambda_L2=1e-3, lr=0.01,