Question: Time Course RNA-Seq
gravatar for buthercup_ch
2.5 years ago by
buthercup_ch20 wrote:


We just finished the QC for the total reads obtain from a Time Course RNA-Seq experiment (Illumina HiSeq2000), and are satisfied with the quality. Now we have started the Differential Expression Gene analysis, and the strategy proposed by our bioinformatican was the following: Calculating DEG for every condition compared with the initial one, so we will have at the end 5 comparisons. DEG for each gene will show a certain p-value in each case, so we would count as statistically significant those genes with p-value<0.05 in all 5 comparisons.

Do you think this is a right approach?

Thanks in advance for your comments and advices.

rna-seq bacteria time course • 2.4k views
ADD COMMENTlink modified 2.5 years ago by Ron910 • written 2.5 years ago by buthercup_ch20

What is the main goal here ? Do you also have the control for each time point or do you just want to see the genes that are altered in each time point w.r.t 0.h ?

If you are looking for genes that follow a pattern, you could use something like WGCNA to get the tightly co-expressed genes across all time points but you need to have enough samples.

You can also use JTK_CYCLE to get the cycling genes. I heard about this program from this science paper.

ADD REPLYlink modified 2.5 years ago • written 2.5 years ago by geek_y9.3k

If you do pair-wise comparisons is that going to be done with just one sample each or one sample to some replicates of control or are there replicates for all samples at all time points?

ADD REPLYlink written 2.5 years ago by genomax64k

Sounds about right....but there will be more considerations that the bioinformaticioan should know about...replicates etc.

Take a look at the original paper for cufflinks. They test they're new software on cells going through different stages of differentiation so gather data at 4 time points (instead of your 5). How they analyse and present the data should give you more ideas. The bioinformatician will (should!) already be familiar with cufflinks.

ADD REPLYlink modified 2.5 years ago • written 2.5 years ago by YaGalbi1.4k

Agreed to all advices above... If you have enough replicates for each time point, you may also want to do clustering for differentially expressed genes in each time point.

ADD REPLYlink written 2.5 years ago by jiwpark00200
gravatar for Persistent LABS
2.5 years ago by
Persistent LABS740 wrote:

Approach looks correct. It appears that you have six time-points, and therefore you will have 5 comparisons with respect to the first time point. I would recommend you to use adjusted p-value (also called q-value) < 0.05. Additionally, you can apply some filter on fold-change value (like 2) also. I am not sure about your pipeline. But if you have count data, you can use DESeq2 on your time-series experiment. There are different approaches to analyze time-series data based on your experiment [Reference:].

ADD COMMENTlink written 2.5 years ago by Persistent LABS740
gravatar for EVR
2.5 years ago by
EVR510 wrote:


Kindly go through the R package called maSigpro "".

ADD COMMENTlink written 2.5 years ago by EVR510
gravatar for andrew.j.skelton73
2.5 years ago by
andrew.j.skelton735.5k wrote:

If you voom transform your counts, then you could implement the HotellingT2 test using the TimeCourse package.

ADD COMMENTlink written 2.5 years ago by andrew.j.skelton735.5k
gravatar for Ron
2.5 years ago by
United States
Ron910 wrote:

You can also look at Enriched pathways using GSEA (Gene set Enrichment Analysis) after doing Differential Expression using DEseq package. There are lot of Categories in GSEA (Oncogenic,Immunologic etc) or you can curate your own signatures and do your enrichment analysis. Another thing you could do is do heatmaps between the categories using differentially expressed genes(from visualization perspective)

Some iDeas A: Analysis past the differentially expressed genes: RNAseq

ADD COMMENTlink modified 2.5 years ago • written 2.5 years ago by Ron910
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