Hello, I'm currently trying to look for trajectory analysis from my dataset and pseudotime analysis, I'm trying to look for the genes that contribute more significantly to the pseudotime calculation since they might be of interesting biological significance.
I tried with Monocle3, copying the workflow from the tutorial: https://cole-trapnell-lab.github.io/monocle3/docs/differential/
However, not that I've got the output, I find difficulty in understanding what information it has given me and how could I make better use of the analytical result (or just checking whether it's confidence level is sufficient). I understand that what I've got from the graph_test() is a list of genes, and (probably) their pseudotime related information. However, the only column I understand is that the first column is the list of genes... Could someone please help me understand what the other columns indicate? Like the p_value seems understandable and is an indication on statistical significance but I'm not sure what statistic it's telling me about and what comparison is it making...
Thank you so much!
I have the same problem interpreting the data. However, from my understanding, significant q_value from
graph_testfunction of monocle3 indicates genes that varies along the trajectory (pseudotime), however, they did not indicate whether they increase (upregulate) or decrease (downregulate) since we do not have information about log_fold change? Therefore, some people created algorithm such as TradeSeq (https://statomics.github.io/tradeSeq/articles/Monocle.html), which enable us to find genes that are downregulated or upregulated along the Trajectory using monocle 3 object. Not sure how much it can be trusted and running Tradseq takes too long, which can be up to more than a week for my case and therefore not sure whether this is reliable... People who developed Monocle 3 and Tradseq seq never responded to my questions... etc. so it is difficult in some cases etc.