Thanks a lot for the information. Yes, actually when I took all the genes long with novel lncRNA transcripts and performed DEG analysis. And what I see is there re no novel lncRNA transcripts among DEGs I got. What I'm interested in is differentially expressed novel lncRNA transcripts. And ofcourse yes the novel lncRNA trannscripts have very low counts compared to known lncRNAs.
Yes, I have a plan to validate them with experiments, but the problem is like I mentioned above I don't see any novel lncRNAs among DEGs if I use all for them for downstream analysis, because novel are very very low expressed and not expressed in all samples.
What else I should do now? Any help.
These are "true" differentially expressed genes. It is a group mean that is significant, not every single event. You can use
FilterByExprand try to tweak parameters there to try and eliminate samples with many low counts. Or you can manually require that at least x% of samples per group have at least y counts. Maybe 50% per group must have at least 10 raw counts or so.thank you...I actually used Filtering step from
edgeRalready likekeep <- rowSums(cpm(y) > 1) >= sample number. With these among 10,000 I kept around 7000 lncRNA transcripts which are used for DEA.In there is other transcript in below box plot Is this still said as upregulated in GroupA?
thank you.
So are you performing differential analysis of just lncRNAs ? May be that’s wrong. You need to use all genes quantified.
I'm interested in only lncRNAs. I don't think it is wrong.
Are you performing
cpmon total number of reads just mapped to lncRNAs ? What if you don’t have a million reads mapped in a sample ? Your library size is total reads or reads mapped only to lncRNAs ?These 10,000 lncRNA transcripts were novel transcripts in my analysis. So, yes I used
cpmonly.Be aware that you should perform analysis on all genes and then select the interesting ones from the results. I hope you did not run edgeR on only the novel transcripts. The power of edgeR comes from sharing information across genes so you benefit from a large number of genes and many of those being highly-expressed and not changing. I suggest you include the novel transcripts (or genes depending on what exactly they are) to the set of genes already present in the reference annotation and repeat analysis.
From the pipeline lncRNApipe I used, to detect novel lncRNAs....I found approx 10,000 novel lncRNA transcripts. And this is what I used with edgeR for GroupA vs GroupB comparison.
Don't know what else I should use.
In that case, at least you need to provide the library sizes (sequencing depth of each sample) as a vector to edgeR cpm function (
lib.size) for the appropriate normalisation. Or merge your novel lncRNAs with known annotations (protein-coding genes and known lncRNAs) to create a final GTF and perform differential analysis all together.Yes, I added
lib.sizeforcpm. I'm afraid that we if I merge these novel transcripts with mRNA and Known lncRNAs, I won't be able to detect any novel differentially expressed transcripts. Because mRNAs are high expressed and lncRNAs are low expressed.I see that library sizes of the samples is very low. There are two samples with very high raw counts. I feel like doing downsampling to these two samples (GroupA_12 and GroupB_2). May I know how to this downsampling before DEA? Please let me know.
@geek_y and @ATpoint Could you please check my below comment and tell me your opinion.thanq
Hi, your lib sizes are too small because you are calculating only the lncRNA reads. You can get total number of reads in a bam file and use that number as library size for each sample. Otherwise I am not sure if CPM makes sense (Thats why logCPM values in your boxplots looks very high. If you have less than a million reads and you are scaling it to a million reads, which artificially inflates your signals).