Hi, everyone:

I have used extractROWS method for expanding intersected regions from hitlist, and it works fine. However, for 3 GRanges objects (third GRanges can be manually produced ), I have applied my R function for each of them, then wants to apply chisq.test, but I have trouble to coerce list objects (that contain intersected genomic interval) into GRanges objects, and I ran into this problem: 

 as(res_b, "GRanges")
Error in as(res_b, "GRanges") : 
  no method or default for coercing “list” to “GRanges”

For the feasibility of my problem, I have this reproducible example : 

# set up data

​a <- GRanges(
  seqnames=Rle(c("chr1", "chr2", "chr3", "chr4"), c(3, 2, 1, 2)), ranges=IRanges(seq(1, by=9, len=8), seq(7, by=9, len=8)),  rangeName=letters[seq(1:8)], score=sample(1:20, 8, replace = FALSE))
a$pvalue <- 10^(score(a)/-1) ; a$score <- NULL
b <- GRanges(
  seqnames=Rle(c("chr1", "chr2", "chr3","chr4"), c(4, 3, 1, 1)), ranges=IRanges(seq(2, by=5, len=9), seq(4, by=5, len=9)), rangeName=letters[seq(1:9)], score=sample(1:20, 9, replace = FALSE))
b$pvalue <- 10^(score(b)/-1) ; b$score <- NULL

# overlap

ov_ab <- as(findOverlaps(a, b), "List")

# purify

res_b <- lapply(ov_ab, function(x) {
  idx0 <- which.min(extractROWS(b$pvalue, x))
  idx1 <- extractROWS(seq_along(b), x)[idx0]
  b[idx1]
})

Objective: how can I coerce res_b to GRanges objects ? Is there any way to get out this issue ? Any recommendation, possible solution is highly appreciated. Thanks a lot.

All the best:

Jurat

A slightly round-about way of doing it, but how about converting your list to a GRangesList, then to a data.frame, and then to single GRanges object e.g.

singleGRange <- GRanges(as.data.frame(GRangesList(res_b)))

> singleGRange
GRanges object with 4 ranges and 4 metadata columns:
      seqnames    ranges strand |     group  group_name   rangeName    pvalue
         <Rle> <IRanges>  <Rle> | <integer> <character> <character> <numeric>
  [1]     chr1  [ 7,  9]      * |         1        <NA>           b     1e-15
  [2]     chr1  [12, 14]      * |         2        <NA>           c     1e-12
  [3]     chr1  [17, 19]      * |         3        <NA>           d     1e-20
  [4]     chr2  [32, 34]      * |         4        <NA>           g     1e-14
  -------
  seqinfo: 4 sequences from an unspecified genome; no seqlengths

It would help to understand your high-level question. I think you want to find the subjects with the minimum p-value, grouped by query range. Since a subject might overlap multiple queries, we need to use extractList() to get p-values grouped by query, and then use which.min() and compute the global index:

min_b <- which.min(extractList(b$pvalue, ov_ab))
min_b_global <-  min_b + start(PartitioningByEnd(ov_ab)) - 1L
res_b <- b[min_b_global]

Perhaps we need a global() that converts the local index into unlisted space (the second line above).