I know the Biology behind `deferentially expressed transcripts`.

But I do not understand **what does it mean / difference between** `deferentially expressed genes` and `deferentially expressed transcripts` / isoform levels?

In my RNA-Seq analysis, there are sig. difference data at DEG levels, but those genes are not present at the transcript level. 
Is this possible? or my analysis went wrong?


Hypothetical  e.g.:

@ At1g16610 ;  I have sig results

But @ At1g16610.1 /At1g16610.2 / At1g16610.3 etc nothing is significant.


** I am a wet-lab person, learning bioinformatics thanks to this great community at BIOSTAR
There are 3 ways changes in expression can occure in a gene with multiple transcripts:

 1. All expressed transcripts can be regulated in the same direction (up or down) giving rise to a differential gene expression. In this case the individual transcripts do not necessarily have to be differentially expressed by themself for the gene to be differentially expressed (since the many smaller individual transcript changes when added together becomes a larger change) - but naturally they can be.
 2. A subset of expressed transcripts can be regulated. An easy example is that a single transcript is upregulated while the rest are unchanged aka a differentially expressed transcript. This could also lead to a differentially expressed gene if the transcript is one of the most expressed in that gene or the change is very large. But if the transcript is lowly expressed (compared to the other transcripts) this might not cause the gene to be differentially expressed..
 3. The third possibility is an isoform switch where one transcript is upregulated while another transcript is downregulated. In this case the transcripts might be differentially expressed - but since they cancel each other out on a gene level the gene can remain unchanged.

It is however important to remember that these events are not mutually exclusive. You can easily have an isoform switch in a gene which is also differentially expressed.

Hope this helped.

Cheers
Kristoffer

P.s. if you are interested in isoform switches my R package [IsoformSwitchAnalyzeR][1] is made to identify isoform switches with predicted functional consequences both in individual genes and on a genome wide level - and it works directly from Salmon/Kallisto data as well. Via these links you can see examples of the [gene level analysis][2] and [genome wide level analysis][3].


  [1]: https://bioconductor.org/packages/devel/bioc/html/IsoformSwitchAnalyzeR.html
  [2]: https://bioconductor.org/packages/devel/bioc/vignettes/IsoformSwitchAnalyzeR/inst/doc/IsoformSwitchAnalyzeR.html#the-switchplot
  [3]: https://bioconductor.org/packages/devel/bioc/vignettes/IsoformSwitchAnalyzeR/inst/doc/IsoformSwitchAnalyzeR.html#genome-wide-summaries