One lane is (still) not enough...
After my quick post yesterday where I said one lane isn't enough, I was asked to elaborate a bit more, if I could. Well, I don't want do get into the details of the experiment itself, but I'm happy to jump into the "controls" a bit more in depth.
What I can tell is that with one lane of RNA-Seq (Illumina data50bp), all of the variations I find show up either in known polymorphism database or as somatic SNPs, with a few exceptions. The few exceptions just turn out to be exceptions for lack of coverage.
For a "control", I took two data sets (from two separate patients) - each with 6 individual lanes of sequencing data. (I realize this isn't the most robust experiment, but it shows a point.) In the perfect world, each of the 6 lanes per person would have sampled the original library equally well.
So, I matched up one lane from each patient into 6 sets and asked the question: How many transcripts are void (less than 5 tags) in one sample and at least 5x greater in the other sample. (I did this in both directions.)
The results aren't great. In one direction, I see an average of 1245 Transcripts (about 680 genes, so there's some overlap amongst the transcript set) with a std dev. of 38 Transcripts. That sounds pretty consistent, till you look for the overlap in actual transcripts: avg 27.3 with a std dev of 17.4. (range 0-60). And, when with do the calculations, the most closely matched data sets only have a 5% overlap.
The results for the opposite direction were similar: Average of 277 transcripts found that met the criteria (std.dev of 33.61), with an average overlap between data sets being 4.8, std. dev 4.48. (range of 0-11 transcripts in common.) The best overlap in "upregulated" genes for this dataset was just over 4% concordance with a second pair of lanes.
So, what this tells me (for a VERY dirty experiment) is that expression of genes in one lane is highly variable depending on the lane for genes expressed at the low end. (Sampling at the high end usually pretty good, so I'm not too concerned about that.)
What I haven't answered yet is how many lanes is enough. Alas, I have to go do some volunteering, so that experiment will have to wait for another day. And, of course, the images I created along the way will have to follow later as well.
What I can tell is that with one lane of RNA-Seq (Illumina data50bp), all of the variations I find show up either in known polymorphism database or as somatic SNPs, with a few exceptions. The few exceptions just turn out to be exceptions for lack of coverage.
For a "control", I took two data sets (from two separate patients) - each with 6 individual lanes of sequencing data. (I realize this isn't the most robust experiment, but it shows a point.) In the perfect world, each of the 6 lanes per person would have sampled the original library equally well.
So, I matched up one lane from each patient into 6 sets and asked the question: How many transcripts are void (less than 5 tags) in one sample and at least 5x greater in the other sample. (I did this in both directions.)
The results aren't great. In one direction, I see an average of 1245 Transcripts (about 680 genes, so there's some overlap amongst the transcript set) with a std dev. of 38 Transcripts. That sounds pretty consistent, till you look for the overlap in actual transcripts: avg 27.3 with a std dev of 17.4. (range 0-60). And, when with do the calculations, the most closely matched data sets only have a 5% overlap.
The results for the opposite direction were similar: Average of 277 transcripts found that met the criteria (std.dev of 33.61), with an average overlap between data sets being 4.8, std. dev 4.48. (range of 0-11 transcripts in common.) The best overlap in "upregulated" genes for this dataset was just over 4% concordance with a second pair of lanes.
So, what this tells me (for a VERY dirty experiment) is that expression of genes in one lane is highly variable depending on the lane for genes expressed at the low end. (Sampling at the high end usually pretty good, so I'm not too concerned about that.)
What I haven't answered yet is how many lanes is enough. Alas, I have to go do some volunteering, so that experiment will have to wait for another day. And, of course, the images I created along the way will have to follow later as well.
Labels: Bioinformatics, false discovery rate, Genomics, Sequencing, Vancouver Short Read Analysis Package
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