diff --git a/NEWS.md b/NEWS.md
index 47425ac..60af7ca 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -9,6 +9,8 @@
 - Ability to do downstream analysis on H5 data
   - Pseudo-bulk should be easy because we are just aggregating cells.
   - Wilcoxon might be a bit harder because ranks are calculated per gene but the H5 sparse data is column majored. Might need to find a fast on-disk transposition method.
+- Fix runUINMF aborting criteria
+  - UINMF is capable of running with k > number of shared genes. Don't have to abort on it.
 
 ## rliger 2.0.0
 
diff --git a/vignettes/articles/STARmap_dropviz_vig.Rmd b/vignettes/articles/STARmap_dropviz_vig.Rmd
index 921123f..d19ca63 100644
--- a/vignettes/articles/STARmap_dropviz_vig.Rmd
+++ b/vignettes/articles/STARmap_dropviz_vig.Rmd
@@ -66,10 +66,8 @@ Unshared Integrative Non-negative Matrix Factorization (UINMF) can be applied wi
 
 In this tutorial, we set dataset specific lambda (regularization parameter) values to penalize the dataset specific effect differently. 
 
-Another noteworthy advantage of UINMF is that we are able to use a larger number of factors than there are shared features. We captilize on this by changing the default value of `k` to 40. 
-
 ```{r factorization}
-lig <- runUINMF(lig, k = 40, lambda = c(10, 1))
+lig <- runUINMF(lig, k = 25, lambda = c(10, 1))
 ```
 
 ## Step 4: Quantile Normalization and Joint Clustering