Question: basic statistics books to understand DESeq2
0
gravatar for Bioinfonext
17 months ago by
Bioinfonext250
Korea
Bioinfonext250 wrote:

Hi,

I am from biological background, Could you please advise me to basic biostatistics books that can help me to understand the DESeq2 tutorial?

Kind Regards Bioinfonext

R • 792 views
ADD COMMENTlink modified 17 months ago by mmfansler350 • written 17 months ago by Bioinfonext250

Have you read the paper and the vignette?

ADD REPLYlink written 17 months ago by WouterDeCoster44k

Yes, I need to understand basic statistics terms used in DESeq2 like:

1) I am always confused with how to design the formula: in case of one factor, two factors and three factors

2) When exactly need to use interaction terms or group all factor into one?

3) what is beta prior?

4) what is shrink log fold changes? I only know log fold change.

5) Dispersion is generally estimation of variance but what is shrinkage?

Kind Regards Bioinfonext

ADD REPLYlink modified 17 months ago • written 17 months ago by Bioinfonext250

Try this one.

Beginner’s guide to using the DESeq2 package

Michael Love1∗, Simon Anders2, Wolfgang Huber2

https://bioc.ism.ac.jp/packages/2.14/bioc/vignettes/DESeq2/inst/doc/beginner.pdf

I hope, it is not a double answer

ADD REPLYlink modified 17 months ago • written 17 months ago by natasha.sernova3.7k

Thank you very much.

ADD REPLYlink written 17 months ago by Bioinfonext250

If it's not enough, ask your questions one after another one in google.

For example, I’ve asked your question 5 in Google.

Below is the top answer.

The larger a dispersion value, the larger the difference in expression has to be in order for a gene to be called DE. As the number of replicates for each condition increases, the amount of dispersion shrinkage per gene decreases as we are then able to estimate the dispersion parameter from the data without shrinkage.

DESeq2 Dispersion Shrinkage - more samples is better?

https://support.bioconductor.org/p/100764/

ADD REPLYlink modified 17 months ago • written 17 months ago by natasha.sernova3.7k
3
gravatar for mmfansler
17 months ago by
mmfansler350
MSKCC | New York, NY
mmfansler350 wrote:

Modern Statistics for Modern Biology by Holmes and Huber covers DESeq2 in Chapter 8 and tries to provide most of the statistical background to get there with the earlier chapters. It does assume some basic facility with R.

There is currently an online reading group, which meets weekly to present and discuss the chapters and exercises. Chapter 8 is scheduled for discussion on May 29th, 2019.

ADD COMMENTlink modified 17 months ago • written 17 months ago by mmfansler350

Thank you very much to you all. I am not able to understand few things:

1) Which normalized method DESeq used at the step by default: vst or rlog

dds <- DESeq(dds)

2) what are the further step after getting res table:

> res.root.soil= results(diagdds, contrast = c("Tissue", "Root", "Soil"), alpha = 0.1)

> summary(res.root.soil)



out of 30438 with nonzero total read count

adjusted p-value < 0.1

LFC > 0 (up)     : 10, 0.033% 

LFC < 0 (down)   : 29584, 97% 

outliers [1]     : 0, 0% 

low counts [2]   : 5664, 19% 

(mean count < 0)

[1] see 'cooksCutoff' argument of ?results

[2] see 'independentFiltering' argument of ?results

3) what is the use of this step and when should I perform this and this coef is the same which I used at the res step with contrast

res <- lfcShrink(dds, coef="condition_trt_vs_untrt", type="apeglm")

Kind Regards

ADD REPLYlink modified 16 months ago • written 16 months ago by Bioinfonext250
Please log in to add an answer.

Help
Access

Use of this site constitutes acceptance of our User Agreement and Privacy Policy.
Powered by Biostar version 2.3.0
Traffic: 1446 users visited in the last hour