re: PCR dupes:

picard's MarkDuplicates is fairly well accepted for this purpose.

*He that would live in peace and at ease, *
*Must not speak all he knows, nor judge all he sees.*

Benjamin Franklin, Poor Richard's
Almanack<http://archive.org/details/poorrichardsalma00franrich>


On Sat, Nov 16, 2013 at 9:03 PM, Michael Love
<michaelisaiahlove@gmail.com>wrote:

> hi Yoong,
>
>
> On Sat, Nov 16, 2013 at 7:42 PM, FeiYian Yoong [guest] <
> guest@bioconductor.org> wrote:
>
> >
> > I am writing to inquire about independent filtering for my large RNA-seq
> > dataset. I have around 55,000 genes (raw gene counts) RNA sequencing data
> > from 91 libraries/samples, consisting of 3 biological replicates for 4
> > different genotypes on germinating seeds. I am currently working on
> > differential expression and subsequently transcriptomic network analysis
> > for these samples. Before performing any of these analyses, I'd like to
> > perform an independent filtering for my data to increase detection power
> > for differentially expressed genes.
>
>
> Independent filtering does not have to occur before differential
> expression analysis. As implemented in DESeq2 version >= 1.2.0, independent
> filtering is automatically performed by the results() function, after the
> DESeq() function has  been called. Please see the vignette for the details
> on the default workflow, which includes independent filtering.
>
>
>
> > I will be using your DESeq2 package (version 1.2.5) for my filtering and
> > differential expression analysis.
> >
> > Based on recommendation by a statistician, I have decided to perform the
> > following steps:
> >
> > 1) Fit a negative binomial GLM with genotype & time effects across all
> > samples for all genes that have nonzero counts in at least one sample
> > \
> >
> > 2) Filter weakly expressed genes (for example using a filter like the one
> > implemented in HTSFilter)
> >
>
> Filtering genes by mean normalized count is automatically done by the
> results() function.
>
>
>
> > 3) Adjust p-values for genes passing the filter to correct for multiple
> > testing
> >
> >
> This is also automatically done by the results() function.
>
>
>
> >
> > While the DESeq2 package was nicely written, since I am not a
> > statistician, I am still a little bit unclear on a few things. Hence, I
> > would like to clarify a few things with you, mainly the workflow for my
> > analysis. Based on my understanding from what's written in DESeq2
> package,
> > I should be doing the following (in chronological order):
> >
> >
> > 1. First, perform a differential expression (dds function) on my raw gene
> > counts for library size normalization. This step will fit my data to a
> > negative binomial generalized linear model with genotypes & time effects
> > across all samples for all genes that have nonzero counts in at least one
> > sample.
> >
> >
> The DESeq() function estimates size factors for library size
> normalization, estimates the dispersion, then fits a negative binomial
> generalized linear model.
>
>
>
> > 2. Second, use the result I obtain from step 1 to go through independent
> > filtering step using filter_p function from genefilter package.
> >
> >
> The results() function calls the filter_p function from the genefilter
> packages. This maximize the number of adjusted p-values which will be less
> than a given value alpha (defaults to 0.1), by excluding genes with low
> mean normalized count over all samples.
>
>
>
> > 3. Third, use the result from step 2 to filter weakly expressed genes
> > further more using HTSFilter package.
> >
>
> This is the same as step 2.
>
>
>
> > 4. Finally, adjust p-values for genes passing the filter to correct for
> > multiple testing. I am not entirely sure how to do this. Can I perform
> this
> > step using DESeq2 package?
> >
> >
> This is also covered by step 2.
>
>
>
> >
> > Furthermore, does DESeq2 take care of PCR duplicate artifacts?
> >
> >
> No, DESeq2 does not take care of PCR duplicate artifacts, because it
> begins with a summarized count table. If you feel that some of the samples
> might have a problem with many duplicate reads stacking up due to PCR, you
> might consider filtering these upstream of DESeq2. I don't know off the top
> of my head which are the best tools for this task.
>
> Mike
>
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>
>
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