Thanks for your previous comment/advice! I have been able to run URD with my data set. I followed both the tutorial and the long example provided as supplemental file from your original paper.
I, however, have a couple more questions that perhaps you can help out with:
If I have multiple time points (not done in duplicate), do you still recommend to try the batch correction step (I don't think so but would like to hear your opinion)?
For Random walks, can all of the clusters in the final pseudotime stage be used as tips? Or do you recommend narrowing down this list of clusters to "true tips" that can be biologically identifiable and meaningful?
In the #Define the tip cells example (Page 38 in the SupplementaryAnalysis.pdf file), what is tip.to.walk? I was not able to run this section because I am not sure what this is or how to generate it? Is there an example that can be used as reference?
Since I was not able to run #3 above, I did not run the simulateRandomWalk function but instead simulateRandomWalksFromTips as below:
Is this reasonable? **I am running this locally - takes a long time but it gets done!
The tree I have built looks a bit strange (see below);
Is this a result of using all clusters from the final stage as tips?
I was able to identify some marker genes along each branch using only the setdiff function (I believe is the URD DE testing - did not try NMF). Do you recommend trying both?
Visualization of marker genes is giving me some trouble. Does the geneCascadeProcess function usually take a long time to run? I let it run and ran for a couple of days but did not get any results and ended up stopping it.
Sorry for the long thread. I am sure URD will be very useful in my project. Hoping you can provide some advice!
Hi,
Thanks for your previous comment/advice! I have been able to run URD with my data set. I followed both the tutorial and the long example provided as supplemental file from your original paper.
I, however, have a couple more questions that perhaps you can help out with:
If I have multiple time points (not done in duplicate), do you still recommend to try the batch correction step (I don't think so but would like to hear your opinion)?
For Random walks, can all of the clusters in the final pseudotime stage be used as tips? Or do you recommend narrowing down this list of clusters to "true tips" that can be biologically identifiable and meaningful?
In the #Define the tip cells example (Page 38 in the SupplementaryAnalysis.pdf file), what is tip.to.walk? I was not able to run this section because I am not sure what this is or how to generate it? Is there an example that can be used as reference?
Since I was not able to run #3 above, I did not run the simulateRandomWalk function but instead simulateRandomWalksFromTips as below:
axial.walks <- simulateRandomWalksFromTips(axial, tip.group.id="tip.clusters", root.cells=root.cells, transition.matrix = biased.tm, n.per.tip = 25000, root.visits = 1, max.steps = 5000, verbose = F)
Instead of processing random walks with processRandomWalks function as in this example, I processed mine as:
axial <- processRandomWalksFromTips(axial, axial.walks, verbose = F)
Is this reasonable? **I am running this locally - takes a long time but it gets done!
The tree I have built looks a bit strange (see below);
Is this a result of using all clusters from the final stage as tips?
I was able to identify some marker genes along each branch using only the setdiff function (I believe is the URD DE testing - did not try NMF). Do you recommend trying both?
Visualization of marker genes is giving me some trouble. Does the geneCascadeProcess function usually take a long time to run? I let it run and ran for a couple of days but did not get any results and ended up stopping it.
Sorry for the long thread. I am sure URD will be very useful in my project. Hoping you can provide some advice!