[Bioc-devel] Block bootstrap for GenomicRanges
Bernat Gel
bgel @ending from igtp@c@t
Tue Aug 14 15:15:51 CEST 2018
Hi all,
Since you are talking about genomic ranges permutation let me just say
that the regioneR package already does that. It does not have block
bootstrapping as defined here, but it implements two different
randomization models (one that randomizes each region independently and
another one based on "chromosome 'spinning' " that conserves the
internal structure of the set of ranges) and both can take into account
a mask defining regions where ranges cannot be randomized. It's quite
customizable and so it would be possible to add new permutation
strategies if needed.
It's probably not the most efficient implementation, but the
randomization process scales decently and it has proven useful over the
years.
Bernat
*Bernat Gel Moreno*
Bioinformatician
Hereditary Cancer Program
Program of Predictive and Personalized Medicine of Cancer (PMPPC)
Germans Trias i Pujol Research Institute (IGTP)
Campus Can Ruti
Carretera de Can Ruti, Camí de les Escoles s/n
08916 Badalona, Barcelona, Spain
Tel: (+34) 93 554 3068
Fax: (+34) 93 497 8654
08916 Badalona, Barcelona, Spain
bgel using igtp.cat <mailto:bgel using igtp.cat>
www.germanstrias.org <http://www.germanstrias.org/>
<http://www.germanstrias.org/>
El 08/14/2018 a las 03:06 PM, Kasper Daniel Hansen escribió:
> I agree this is super important. I think there may be multiple ways of
> thinking about a decent bootstrapping or permutation of ranges, in
> genomics. I am quite interested in the topic. I think it might belong in a
> new package. I would be interesting in extending the conversation and have
> a couple of different approaches (theoretical) that we could work on being
> efficient.
>
> Best,
> Kasper
>
> On Tue, Aug 14, 2018 at 8:27 AM Michael Love <michaelisaiahlove using gmail.com>
> wrote:
>
>> dear Hervé,
>>
>> Thanks again for the quick and useful reply!
>>
>> I think that the theory behind the block bootstrap [Kunsch (1989), Liu
>> and Singh (1992), Politis and Romano (1994)], needs that the blocks be
>> drawn with replacement (you can get some features twice) and that the
>> blocks can be overlapping. In a hand-waving way, I think, it's "good"
>> for the variance estimation on any statistic of interest that y' may
>> have more or less features than y.
>>
>> I will explore a bit using the solutions you've laid out.
>>
>> Now that I think about it, the start-position based solution that I
>> was thinking about will break if two features in y share the same
>> start position, so that's not good.
>>
>> On Mon, Aug 13, 2018 at 11:58 PM, Hervé Pagès <hpages using fredhutch.org>
>> wrote:
>>> That helps. I think I start to understand what you are after.
>>>
>>> See below...
>>>
>>>
>>> On 08/13/2018 06:07 PM, Michael Love wrote:
>>>> dear Hervé,
>>>>
>>>> Thanks for the quick reply about directions to take this.
>>>>
>>>> I'm sorry for not providing sufficient detail about the goal of block
>>>> bootstrapping in my initial post. Let me try again. For a moment, let
>>>> me ignore multiple chromosomes/seqs and just focus on a single set of
>>>> IRanges.
>>>>
>>>> The point of the block bootstrap is: Let's say we want to find the
>>>> number of overlaps of x and y, and then assess how surprised we are at
>>>> how large that overlap is. Both of them may have features that tend to
>>>> cluster together along the genome (independently). One method would
>>>> just be to move the features in y around to random start sites, making
>>>> y', say B times, and then calculate each of the B times the number of
>>>> overlaps between x and y'. Or we might make this better by having
>>>> blacklisted sites where the randomly shuffled features in y cannot go.
>>>>
>>>> The block bootstrap is an alternative to randomly moving the start
>>>> sites, where instead we create random data, by taking big "blocks" of
>>>> features in y. Each block is a lot like a View. And the ultimate goal
>>>> is to make B versions of the data y where the features have been
>>>> shuffled around, but by taking blocks, we preserve the clumpiness of
>>>> the features in y.
>>>>
>>>> Let me give some numbers to make this more concrete, so say we're
>>>> making a single block bootstrap sample of a chromosome that is 1000 bp
>>>> long. Here is the original y:
>>>>
>>>> y <- IRanges(c(51,61,71,111,121,131,501,511,521,921,931,941),width=5)
>>>>
>>>> If I go with my coverage approach, I should extend it all the way to
>>>> the end of the chromosome. Here I lose information if there are
>>>> overlaps of features in y, and I'm thinking of a fix I'll describe
>>>> below.
>>>>
>>>> cov <- c(coverage(y), Rle(rep(0,55)))
>>>>
>>>> I could make one block bootstrap sample of y (this is 1 out of B in
>>>> the ultimate procedure) by taking 10 blocks of width 100. The blocks
>>>> have random start positions from 1 to 901.
>>>>
>>>> y.boot.1 <- unlist(Views(cov, start=round(runif(10,1,901)), width=100))
>>>
>>> Choosing blocks that can overlap with each others could make y' appear
>>> to have more features than y (by repeating some of the original
>>> features). Also choosing blocks that can leave big gaps in the
>>> chromosome could make y' appear to have less features than y
>>> (by dropping some of the original ranges). Isn't that a problem?
>>>
>>> Have you considered choosing a set of blocks that represent a
>>> partitioning of the chromosome? This would make y' look more like y
>>> by preserving the number of original ranges.
>>>
>>> In other words, if you can assign each range in y to one block and
>>> one block only, then you could generate y' by just shifting the
>>> ranges in y. The exact amount of shifting (positive or negative)
>>> would only depend on the block that the range belongs to. This would
>>> give you an y' that is parallel to y (i.e. same number of ranges
>>> and y'[i] corresponds to y[i]).
>>>
>>> Something like this:
>>>
>>> 1) Define the blocks (must be a partitioning of the chromosome):
>>>
>>> blocks <- successiveIRanges(rep(100, 10))
>>>
>>> 2) Assign each range in y to the block it belongs to:
>>>
>>> mcols(y)$block <- findOverlaps(y, blocks, select="first")
>>>
>>> 1) and 2) are preliminary steps that you only need to do once,
>>> before you generate B versions of the shuffled data (what you
>>> call y'). The next steps are for generating one version of the
>>> shuffled data so would need to be repeated B times.
>>>
>>> 3) Shuffle the blocks:
>>>
>>> perm <- sample(length(blocks))
>>> perm
>>> # [1] 6 5 8 3 2 7 1 9 4 10
>>>
>>> permuted_blocks <- successiveIRanges(width(blocks)[perm])
>>> permuted_blocks[perm] <- permuted_blocks
>>>
>>> 4) Compute the shift along the chromosome that each block went
>>> thru:
>>>
>>> block_shift <- start(permuted_blocks) - start(blocks)
>>>
>>> 5) Apply this shift to the ranges in y:
>>>
>>> shift(y, block_shift[mcols(y)$block])
>>> # IRanges object with 12 ranges and 1 metadata column:
>>> # start end width | block
>>> # <integer> <integer> <integer> | <integer>
>>> # [1] 651 655 5 | 1
>>> # [2] 661 665 5 | 1
>>> # [3] 671 675 5 | 1
>>> # [4] 411 415 5 | 2
>>> # [5] 421 425 5 | 2
>>> # ... ... ... ... . ...
>>> # [8] 11 15 5 | 6
>>> # [9] 21 25 5 | 6
>>> # [10] 921 925 5 | 10
>>> # [11] 931 935 5 | 10
>>> # [12] 941 945 5 | 10
>>>
>>> This code would work with overlapping ranges in y and/or
>>> blocks of variable size.
>>>
>>> Hope this helps,
>>>
>>> H.
>>>
>>>> This last line below is a hack to get back to the ranges for a single
>>>> block bootstrap sample of y. It works here, but only because none of
>>>> the features in y were overlapping each other.
>>>>
>>>> Instead of coverage(), if I'd made an Rle where the non-zero elements
>>>> are located at the start of ranges, and the value is the width of the
>>>> range, I think this Views approach would actually work.
>>>>
>>>> y.boot.1.rng <- IRanges(start(y.boot.1)[runValue(y.boot.1) == 1],
>>>> width=runLength(y.boot.1)[runValue(y.boot.1) == 1])
>>>>
>>>> I'm interested in building a function that takes in IRanges and
>>>> outputs these shuffled set of IRanges.
>>>>
>>> --
>>> Hervé Pagès
>>>
>>> Program in Computational Biology
>>> Division of Public Health Sciences
>>> Fred Hutchinson Cancer Research Center
>>> 1100 Fairview Ave. N, M1-B514
>>> P.O. Box 19024
>>> Seattle, WA 98109-1024
>>>
>>> E-mail: hpages using fredhutch.org
>>> Phone: (206) 667-5791
>>> Fax: (206) 667-1319
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