jefworks / mudan Goto Github PK

Hi,
I was wondering that why the PC calculation for the expression matrix in the MUDAN is
a <- irlba::irlba(crossprod(m)/(nrow(m)-1), nu=0, nv=nPcs, tol=tol, ...)
a$l <- m %*% a$v
m <- t(a$l)
If I understand right, in the Pagoda2 or the Seurat setting, the PC calculation is just a SVD decomposition on the expression matrix using irlba package to get the U matrix.
What is the difference between MUDAN's PCs and others and is there any idea in that ?
Thanks for let me know.

Yao

Hi,

I'm recently following this tutorial to get familiar with the functions in the MUDAN package.

However the embeddings from Rtsne:: Rtsne could not be reproduced very well: if it is run respectively at two time points, the number of the clusters is consistent but the shape and/or the locations of the clusters/cells might be different. It might make the tutorial a little bit confusing, especially for the learners.

To address this and to make the results more 'reproducible', adding 'set.seed()' might be an efficient way. (e.g., https://satijalab.org/seurat/v3.1/interaction_vignette.html) In this case, the coordinates of each cell will be constant between different sessions.

Thanks,
JY

I have a matrix of data (dat.mat) with features in rows and cells in columns. Per the documentation for the getComMembership function, clustering this matrix should give labels over the cells. However, in order to actually cluster the cells, the matrix must be transposed, as shown below:

res <- MUDAN::getComMembership(t(dat.mat), k = k, method = igraph::cluster_infomap, verbose = FALSE)

This seems to indicate that the documentation is incorrect. getComMembership takes a matrix with samples in rows rather than columns.