On the necessity of controls
I guess I've had this rant building up for a while, and it's finally time to write it up.
One of the fundamental pillars of science is the ability to isolate a specific action or event, and determine it's effects on a particular closed system. The scientific method actually demands that we do it - hypothesize, isolate, test and report in an unbiased manner.
Unfortunately, for some reason, the field of genomics has kind of dropped that idea entirely. At the GSC, we just didn't bother with controls for ChIP-Seq for a long time. I can't say I've even seen too many matched WTSS (RNA-SEQ) experiments for cancer/normals. And that scares me, to some extent.
With all the statistics work I've put in to the latest version of FindPeaks, I'm finally getting a good grasp of the importance of using controls well. With the other software I've seen, they do a scaled comparison to calculate a P-value. That is really only half of the story. It also comes down to normalization, to comparing peaks that are present in both sets... and to determining which peaks are truly valid. Without that, you may as well not be using a control.
Anyhow, that's what prompted me to write this. As I look over the results from the new FindPeaks (3.3.3.1), both for ChIP-Seq and WTSS, I'm amazed at how much clearer my answers are, and how much better they validate compared to the non-control based runs. Of course, the tests are still not all in - but what a huge difference it makes. Real control handling (not just normalization or whatever everyone else is doing) vs. Monte Carlo show results that aren't in the same league. The cutoffs are different, the false peak estimates are different, and the filtering is incredibly more accurate.
So, this week, as I look for insight in old transcription factor runs and old WTSS runs, I keep having to curse the lack of controls that exist for my own data sets. I've been pushing for a decent control for my WTSS lanes - and there is matched normal for one cell line - but it's still two months away from having the reads land on my desk... and I'm getting impatient.
Now that I'm able to find all of the interesting differences with statistical significance between two samples, I want to get on with it and find them, but it's so much more of a challenge without an appropriate control. Besides, who'd believe it when I write it up with all of the results relative to each other?
Anyhow, just to wrap this up, I'm going to make a suggestion: if you're still doing experiments without a control, and you want to get them published, it's going to get a LOT harder in the near future. After all, the scientific method has been pretty well accepted for a few hundred years, and genomics (despite some protests to the contrary) should never have felt exempt from it.
One of the fundamental pillars of science is the ability to isolate a specific action or event, and determine it's effects on a particular closed system. The scientific method actually demands that we do it - hypothesize, isolate, test and report in an unbiased manner.
Unfortunately, for some reason, the field of genomics has kind of dropped that idea entirely. At the GSC, we just didn't bother with controls for ChIP-Seq for a long time. I can't say I've even seen too many matched WTSS (RNA-SEQ) experiments for cancer/normals. And that scares me, to some extent.
With all the statistics work I've put in to the latest version of FindPeaks, I'm finally getting a good grasp of the importance of using controls well. With the other software I've seen, they do a scaled comparison to calculate a P-value. That is really only half of the story. It also comes down to normalization, to comparing peaks that are present in both sets... and to determining which peaks are truly valid. Without that, you may as well not be using a control.
Anyhow, that's what prompted me to write this. As I look over the results from the new FindPeaks (3.3.3.1), both for ChIP-Seq and WTSS, I'm amazed at how much clearer my answers are, and how much better they validate compared to the non-control based runs. Of course, the tests are still not all in - but what a huge difference it makes. Real control handling (not just normalization or whatever everyone else is doing) vs. Monte Carlo show results that aren't in the same league. The cutoffs are different, the false peak estimates are different, and the filtering is incredibly more accurate.
So, this week, as I look for insight in old transcription factor runs and old WTSS runs, I keep having to curse the lack of controls that exist for my own data sets. I've been pushing for a decent control for my WTSS lanes - and there is matched normal for one cell line - but it's still two months away from having the reads land on my desk... and I'm getting impatient.
Now that I'm able to find all of the interesting differences with statistical significance between two samples, I want to get on with it and find them, but it's so much more of a challenge without an appropriate control. Besides, who'd believe it when I write it up with all of the results relative to each other?
Anyhow, just to wrap this up, I'm going to make a suggestion: if you're still doing experiments without a control, and you want to get them published, it's going to get a LOT harder in the near future. After all, the scientific method has been pretty well accepted for a few hundred years, and genomics (despite some protests to the contrary) should never have felt exempt from it.
Labels: Bioinformatics, biology, Chip-Seq, false discovery rate, FindPeaks, future, programming, transcriptome, Vancouver Short Read Analysis Package
2 Comments:
Hi Anthony - quite enjoy your blog and am trying out findpeaks on some histone modification chip-seq data.
One question - what exactly are you referring to as 'control' data? For chip-seq, sequencing the "input" ie non-enriched chromatin of your IPs makes sense, but does doubling the amount of sequencing you perform (therefore cost & analysis time) give enough of a return? eg in my first analysis we are using a normal and cancer cell line, and looking for differences between the two.
Also, what controls can you use for WTSS? Or do you just mean comparing two groups eg cancer vs normal?
Cheers,
Aaron
Hi Aaron,
I tried to answer your questions in a new blog posting:
Link
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