Deseq2 Multifactor Design – Design Forum

Deseq2 multifactor design

In fact, deseq2 can analyze any possible experimental design that can be expressed with fixed effects terms (multiple factors, designs with interactions, designs with continuous variables, splines, and so on are all possible).
We have searched different posts in different forums and i can’t figure out how to approach it.
Then the random factor that is strain 1, 2, 3.
This consists of 4 different stages (ages) of fish embryo.
Mvdbeek opened this issue oct 14, 2015 · 25 comments comments.
By adding variables to the design, one can control for additional variation in the counts.
Most the content were contributed by reseacher named as ming (a chinese postdoc of nih i think) and michael love (author of deseq2).
About multiple factors analysis in deseq2.
Hi michael, i’m working with a similar design where i have a model with 3 factors, two fixed factors, sex (m, f) and diet (low and high).
I have a total of 80 samples.

This is using 2.1.8.2 from the testtoolshed.
I found a thread about multiple factors analysis in deseq2 in the mailing list of bioconductor is interesting.
The contrast argument of the function _results_ needs a character vector of three componenets:
I am using deseq2 on r (version 3.2.2) for analysis of small rna expression data.
In the multifactor section of the `deseq2` manual:


rna seq DESeq2 complicated design effect of replicated


DESeq2 experimental design and interpretation In Data We


Genelevel differential expression analysis with DESeq2


DESeq2详细用法 简书


RNAseq matrix design in DESeq2 or edgeR


DESeq2 experimental design and interpretation In Data We


Strange MAplot using DESeq2


Genelevel differential expression analysis with DESeq2


Meandispersion functional form for simulations. DESeq2


limma、DESeq2、edgeR差异分析及绘制韦恩图 简书

I am using deseq2 on r (version 3.2.2) for analysis of small rna expression data.
I have a total of 80 samples.
Here i paste my multifactor design:
In fact, deseq2 can analyze any possible experimental design that can be expressed with fixed effects terms (multiple factors, designs with interactions, designs with continuous variables, splines, and so on are all possible).
I found a thread about multiple factors analysis in deseq2 in the mailing list of bioconductor is interesting.
Copy link member mvdbeek commented oct 14, 2015.
We have searched different posts in different forums and i can’t figure out how to approach it.
Using a ~batch + condition design, we obtained the following error:
The contrast argument of the function _results_ needs a character vector of three componenets:
It is too long and i try to make them look neat.

Then the random factor that is strain 1, 2, 3.
This consists of 4 different stages (ages) of fish embryo.
By adding variables to the design, one can control for additional variation in the counts.
About multiple factors analysis in deseq2.
In the multifactor section of the `deseq2` manual:
Mvdbeek opened this issue oct 14, 2015 · 25 comments comments.
I’m using deseq2_1.12.4 > as.data.frame(coldata(dds)) sex diet.
The name of the variable (in this case temperature), and the name of the factor level for the numerator of the log2 ratio (elevated) and the denominator (decreased)
This is using 2.1.8.2 from the testtoolshed.
I got the following input parameters:

Most the content were contributed by reseacher named as ming (a chinese postdoc of nih i think) and michael love (author of deseq2).
Hi michael, i’m working with a similar design where i have a model with 3 factors, two fixed factors, sex (m, f) and diet (low and high).

Read more here: Source link