identifying trends in RNA-seq data
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Bogdan ▴ 620
@bogdan-2367
Last seen 25 days ago
Palo Alto, CA, USA

Dear all,

shall we have multiple samples that were collected at 4 different time points,

which algorithms would you recommend in order to identify the trends ?

thanks a lot,

-~ Bogdan

RNASeq trend • 132 views
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How about DESeq2 LRT followed by hclust of the DEGs and visualization in a heatmap, so basically the standard approach for any time-course data?

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Thank you for your suggestion. I was thinking more in terms of the figure 3 in the article :

http://cole-trapnell-lab.github.io/pdfs/papers/trapnell-cacchiarelli-monocle.pdf

where they show the following categories genes :

<> immediate up-regulation

<> transient up-regulation

<> gradual up-regulation

<> immediate down-regulation

<> transient down-regulation

<> gradual down-regulation

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Figure 3 is basically just a different way of visualizing figure 2C I think. Pseudotime is a concept from single-cell RNA-seq and would not apply in your case as you know what the timepoints are, so you can order the columns with that knowledge rather than using a machine learning approach. Clustering rows is then just hclust to derive clusters. It would be then on you how you call each cluster, e.g. with the labels you use above depending on how drastic the changes are between timepoints.

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