Chen Li

+1

Thanks for implementing this! I used it to regress out total counts and cell cycle scores before highly variable gene selection, and it worked well. The clusters are better separated...

The input of DTW script is a file with three columns: latent time, motif accessibility, and TF gene expression. The accessibilities and expressions of genes of interest can be extracted...

For each defined target and motif pair you can use a script similar to this to save the accessibility and expression. Depending on your specific goal (e.g. chromatin priming), the...

I think the issue here is due to adata.X being a sparse matrix. You can try `r = np.array(adata_result[:,gene].X.A)` instead and see if it works out.

Please follow Velocyto's documentation to run their CLI tool. See http://velocyto.org/velocyto.py/tutorial/cli.html#run10x-run-on-10x-chromium-samples. If that doesn't work on Multiome, you can also try directly calling Velocyto run http://velocyto.org/velocyto.py/tutorial/cli.html#run-run-on-any-technique-advanced-use using the 10X barcode...

Hi David, We recommend first using scVelo's preprocessing steps to prepare the input data. The "filter_and_normalize" function is able to select genes with minimum counts in the unspliced and spliced...

Hi, seeing that the three time-points separate really well in the UMAP, it may be worth integrating the datasets first to remove the potential differences caused by batch effects. You...

Hi Dan. By GRN, are you referring to the motif analysis we did for the human brain? It follows the steps of [chromVAR](https://greenleaflab.github.io/chromVAR/articles/Introduction.html), or similarly, RunChromVAR section in the Seurat...

Hi, sorry for late reply. Which version of multivelo did you use? It seems the issue is from the mv.recover_dynamics_chrom function. It is a bit difficult to tell the exact...