dgrun / fateid Goto Github PK

Hi, thank you for developing FateID!
I ran the code as the reference manual provided with default parameters. But when I plot the average pseudo-temporal expression profile of genes within individual modules of the SOM, I noticed some variances in my plot. How can I optimize my parameters?
Thank you so much!!!

Hello Dominic @dgrun!

I just updated princurve to version 2.0.2 on CRAN. I noticed the FateID package uses princurve and wanted to notify you of the changes made so you can update your package accordingly.

The principal.curve function has been renamed to principal_curve, in order to get princurve up-to-date with current R naming conventions. In order to make the names of the parameters more self explanatory, the argument plot.true has been renamed to plot_iterations, and the output variable tag has been renamed to ord.

The function principal.curve still exists in the package, for now, as not to break your package. On July 1st, however, this function will become deprecated, and will later on be removed.

If you have any remarks or questions, feel free to let me know!
Robrecht

Dear Developers,
I noticed in the paper (Herman, Sagar, and Grün 2018) that Seurat was used also. Have you ever tried to import a seurat object into fateID? If you have, i could work in this way and adjust the script accordingly by giving consensus genes of interest. I would like to know how I can do this (if its not alot of work) and if there was ever a script around to do this.
Thanks a lot.

Hi,

Thank you for developing FateID. I have used the vignette on the original data with no problem. Now, I would like to use FateID in my data set. I have processed my data with Seurat and have a final object on which I want to identify fate biases. I have the following questions;

1. Is there now a function available to import directly from Seurat?
2. If I import my data from Seurat manually, am I right to say that "x" is the un-normalized counts matrix; and "y" will be cell identities? What about "v"?
3. If I reclassify all remaining cells using the cells within the target clusters as input with FateID, then I do not have to reclassify cells using the full data frame containing all genes can be used as input? (i.e. steps below)

Error in randomForest.default(xr, as.factor(pr), xt, nbtree = nbtree, :
Need at least two classes to do classification.

After this, I am stuck and cannot proceed. Any help will be much appreciated.

Thanks!