Open wikiselev opened 6 years ago
Hi,
I was about to open an issue related to this problem. I am repeating the sc3
function several times, using the same rand_seed
, yet I get very different results. I suspected that it might be related to using several cores (n_cores = 4
in my case). But if I set this value to 1, I still get different results. My full code is the following:
# Estimate optimal number of clusters using SC3
sc <- sc3_estimate_k(object = sc)
# Check estimated optimal number of k
# Output is 10
(k.use <- metadata(sc)$sc3$k_estimation)
# Add feature_symbol column in rowData(sc)
rowData(sc)$feature_symbol <- rownames(counts(sc))
# Perform SC3 clustering
sc <- sc3(object = sc, ks = k.use, gene_filter = TRUE,
pct_dropout_min = 10, pct_dropout_max = 90, d_region_min = 0.04,
d_region_max = 0.07, svm_num_cells = NULL, svm_train_inds = NULL,
svm_max = 5000, n_cores = 4, kmeans_nstart = 1000,
kmeans_iter_max = 1e+09, k_estimator = FALSE, biology = TRUE,
rand_seed = 1)
Any idea when this issue can be fixed?
Best, Leon
Hi @leonfodoulian , thanks! This is a known bug, I started working on it but at the moment it is very slow since I moved jobs and doing it at free time. Sorry for the inconvenience.
Still working, but now there is another person who is dramatically improving SC3
, so hopefully will be fixed soon.
Hi,
I am wondering if k estimates vary between runs. Could k estimates be affected by this issue?
Thanks!
Hi, no, k estimation algorithm is deterministic and should not change between runs.
Hi,
I am also having this problem that multiple runs of SC3 produce different results even when rand_seed is set to a fixed value. Is this still a known problem? I have just re-installed SC3 from Bioconductor (https://bioconductor.org/packages/release/bioc/html/SC3.html).
Best wishes, Lucy
Hey, yes, it's still a known problem, but, unfortunately, no one is working on it at the moment.
Looks like the seed is changed somewhere in the code, so sometimes results cannot be reproduced...