Question: Kallisto-Sleuth or Kallisto-Deseq2?
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gravatar for Mozart
21 months ago by
Mozart140
Mozart140 wrote:

Hello everyone, I am using Kallisto-Sleuth at the very end of my pipeline in the RNA seq analysis. I'm trying to find on the web (as a newbie) all the reasons in favour of my choice; I would like to ask why choosing Kallisto and Slueth at the end of my pipeline would be a better choice than Deseq2..

rna-seq • 2.1k views
ADD COMMENTlink modified 4 months ago • written 21 months ago by Mozart140

Sounds like you made that choice without recognizing difference between alignment and mapping.

ADD REPLYlink written 21 months ago by genomax70k
6
gravatar for Istvan Albert
21 months ago by
Istvan Albert ♦♦ 81k
University Park, USA
Istvan Albert ♦♦ 81k wrote:

Kallisto is not an alternative to deseq2.

Kallisto does the quantification (assigns reads to transcripts). You can run deseq2 on the effective counts output of kallisto (after rounding these counts to integers).

Sleuth is the "alternative" to deseq2.

ADD COMMENTlink modified 21 months ago • written 21 months ago by Istvan Albert ♦♦ 81k

thanks and sorry if I am a bit confused about this; which pipeline is the best among the following ones: fastq file-->STAR-->bam file-->HTSEQCOUNT-->.txt-->DESEQ2/PCA/DE or fastq file-->KALLISTO-->SLEUTH-->differential expression analysis

or do you prefer any other different approach? finally I am not sure I have understood how to define the best "formula"...how can you say "this is a best solution"? please help me and sorry for my English

ADD REPLYlink written 21 months ago by Mozart140
3

There is no gold standard that outperforms the others. Both of the pipes that you mention are valid and perform well. For the end user (and this is just my personal opinion) it all comes down to which pipeline you feel most comfortable with, in terms of documentation and handling. Both DESeq2, Sleuth, as well as the other accepted approaches like edgeR do work, are accepted and well tested. In my experience, end users do use what they first got in contact with, either by searching around, reading blogs or what the instructor used in your first RNA-seq workshop. If you read the blogs of the biostatisticians who develop these tools, even they acknowledge that each approach has its own qualification, without that it outperforms others (see here for an example). So choose whatever you feel most comfortable with, follow the instructions in the manuals and vignettes, and get on with your analysis.

ADD REPLYlink written 21 months ago by ATpoint21k
1

Well said. Keeping in mind that the hypotheses generated need to be independently experimentally verified in any case.

ADD REPLYlink written 21 months ago by genomax70k

thank you for your replies; I come from a different background and I am just looking at these tools for the first time. Genomax how can I verify the hypotheses? If I don't misread what you said, you are basically state that after 'running' my pipeline I have to verify experimentally the result (so this further assessment should be done in silico, as well?); is there any way to validate my results prior to go on 'the bench'?

Secondly, is it a good idea to use kallisto as input for DESeq2 or maybe it is better to use deseq2 with "the STAR" pipeline? Or probably it could be worth doing fastq file-->KALLISTO-->DESeq2 & fastq file-->KALLISTO-->Sleuth?

ADD REPLYlink written 21 months ago by Mozart140
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Either possibility is OK. The main advantage of pseudo-alignment with kallisto is the increase in speed. If you want to use DESeq2 after quantification with kallisto, you can import the count estimates from kallisto into R/DESeq2 with the tximport package, as recommended for DESeq2 rather then using rounded estimates as suggested above (I do not know if this makes a big difference, but I always follow the advices of the tool developers, unless I have expert knowledge to decide against it). Here is an example workflow you could follow. I would do the following: Spend one day of quality time on reading the full documentation of DESeq2 and the full doc. of sleuth. Then choose the approach from which you think you understood the documentation better and you would feel more comfortable with.

ADD REPLYlink modified 21 months ago • written 21 months ago by ATpoint21k
1

is there any way to validate my results prior to go on 'the bench'?

As long as the biological differences are prominent in your dataset results, you get from DESeq2 or Sleuth should be reasonably similar. Someone with domain knowledge of the experiment (if that is not you) should be able to look at the results and get an idea if the results make a story. They will also be able to decide which genes can be selected for further experimental validation.

ADD REPLYlink written 21 months ago by genomax70k

Thanks for both replies, would it be a good idea carrying out two parallel workflows in order to double check the results?

ADD REPLYlink written 21 months ago by Mozart140
1

If it's your own curiosity, then you can run two workflows - it could be seen as a training exercise.

As an example, though, I once ran the following analyses on the same data:

  1. Tophat2 --> raw count abundance with BEDTools (a 'hack') --> DESeq2
  2. Kallisto --> DESeq2

...and got the same results where it mattered.

As genomax says, "as long as the biological differences are prominent in your dataset results", you should get the same results from each of the standard/accepted methods.

ADD REPLYlink written 21 months ago by Kevin Blighe46k

wow you clarified a lot of things! thank you very very much can you please help me with another question I put down below?

ADD REPLYlink written 21 months ago by Mozart140
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