compare_peaks
caitlinpage
2024-09-04
Last updated: 2024-10-01
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Knit directory: damsel_paper/analysis/
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Introduction
library(Damsel)
library(plyranges)Loading required package: BiocGenerics
Attaching package: 'BiocGenerics'The following objects are masked from 'package:stats':
IQR, mad, sd, var, xtabsThe following objects are masked from 'package:base':
anyDuplicated, aperm, append, as.data.frame, basename, cbind,
colnames, dirname, do.call, duplicated, eval, evalq, Filter, Find,
get, grep, grepl, intersect, is.unsorted, lapply, Map, mapply,
match, mget, order, paste, pmax, pmax.int, pmin, pmin.int,
Position, rank, rbind, Reduce, rownames, sapply, setdiff, table,
tapply, union, unique, unsplit, which.max, which.minLoading required package: IRangesWarning: package 'IRanges' was built under R version 4.4.1Loading required package: S4VectorsWarning: package 'S4Vectors' was built under R version 4.4.1Loading required package: stats4
Attaching package: 'S4Vectors'The following object is masked from 'package:utils':
findMatchesThe following objects are masked from 'package:base':
expand.grid, I, unnameLoading required package: GenomicRangesLoading required package: GenomeInfoDb
Attaching package: 'plyranges'The following object is masked from 'package:IRanges':
sliceThe following object is masked from 'package:stats':
filterlibrary(dplyr)
Attaching package: 'dplyr'The following objects are masked from 'package:plyranges':
between, n, n_distinctThe following objects are masked from 'package:GenomicRanges':
intersect, setdiff, unionThe following object is masked from 'package:GenomeInfoDb':
intersectThe following objects are masked from 'package:IRanges':
collapse, desc, intersect, setdiff, slice, unionThe following objects are masked from 'package:S4Vectors':
first, intersect, rename, setdiff, setequal, unionThe following objects are masked from 'package:BiocGenerics':
combine, intersect, setdiff, unionThe following objects are masked from 'package:stats':
filter, lagThe following objects are masked from 'package:base':
intersect, setdiff, setequal, unionlibrary(tidyr)
Attaching package: 'tidyr'The following object is masked from 'package:S4Vectors':
expandlibrary(ggplot2)
library(BSgenome.Dmelanogaster.UCSC.dm6)Loading required package: BSgenomeLoading required package: BiostringsLoading required package: XVector
Attaching package: 'Biostrings'The following object is masked from 'package:base':
strsplitLoading required package: BiocIOLoading required package: rtracklayer
Attaching package: 'rtracklayer'The following object is masked from 'package:BiocIO':
FileForFormatlibrary(TxDb.Dmelanogaster.UCSC.dm6.ensGene)Loading required package: GenomicFeaturesLoading required package: AnnotationDbiLoading required package: BiobaseWelcome to Bioconductor
Vignettes contain introductory material; view with
'browseVignettes()'. To cite Bioconductor, see
'citation("Biobase")', and for packages 'citation("pkgname")'.
Attaching package: 'AnnotationDbi'The following object is masked from 'package:dplyr':
selectThe following object is masked from 'package:plyranges':
selectlibrary(org.Dm.eg.db)library(ggVennDiagram)
Attaching package: 'ggVennDiagram'The following object is masked from 'package:tidyr':
unitelibrary(rtracklayer)Compare Damsel and Vissers
damsel_peaks <- readRDS("../output/damsel_peaks.rds")
vissers_peaks <- readRDS("../output/vissers_peaks.rds")
vissers_peaks_mod <- readRDS("../output/vissers_peaks_mod.rds")nrow(damsel_peaks)[1] 3120nrow(vissers_peaks)[1] 2286- Visser’s peaks had to be modified in order to be usable (missing chromosome name)
- They also do not contain a p value - making it difficult to rank peaks and identify the most significant
vissers_peaks_mod$peak_num <- 1:nrow(vissers_peaks_mod)
vissers_peaks_mod$Position <- paste0(vissers_peaks_mod$seqnames, "-", vissers_peaks_mod$start)peak_all <- union_ranges(as_granges(damsel_peaks), as_granges(vissers_peaks_mod)) %>%
data.frame() %>% mutate(compare_num = 1:n())
peak_compare_damsel <- find_overlaps(as_granges(peak_all), as_granges(damsel_peaks)) %>% data.frame()
peak_compare_vissers <- find_overlaps(as_granges(peak_all), as_granges(vissers_peaks_mod)) %>% data.frame()venn_data <- list(Damsel = peak_compare_damsel$compare_num, Vissers = peak_compare_vissers$compare_num)
ggVennDiagram(venn_data)
Compare Damsel and Marshall
marshall_peaks_1 <- rtracklayer::import("../data/sd_1_SRR794884-vs-Dam.kde-norm.gatc-FDR0.01.peaks.gff")
marshall_peaks_2 <- rtracklayer::import("../data/sd_2_SRR7948877-vs-Dam.kde-norm.gatc-FDR0.01.peaks.gff")
marshall_peaks <- readRDS("../output/marshall_peaks.rds")head(marshall_peaks_1)GRanges object with 6 ranges and 5 metadata columns:
seqnames ranges strand | source type score phase
<Rle> <IRanges> <Rle> | <factor> <factor> <numeric> <integer>
[1] 2L 71990-73420 * | NA NA 1.99 <NA>
[2] 2L 128081-131214 * | NA NA 2.10 <NA>
[3] 2L 153858-155437 * | NA NA 2.13 <NA>
[4] 2L 178291-180711 * | NA NA 2.64 <NA>
[5] 2L 454087-456365 * | NA NA 1.28 <NA>
[6] 2L 1054665-1056694 * | NA NA 2.11 <NA>
FDR
<character>
[1] 0.00722203739243461
[2] 9.74130844090044e-05
[3] 0.00722203739243461
[4] 4.98769409283497e-07
[5] 0.000523003066327455
[6] 1.42029958212876e-08
-------
seqinfo: 7 sequences from an unspecified genome; no seqlengthshead(marshall_peaks_2)GRanges object with 6 ranges and 5 metadata columns:
seqnames ranges strand | source type score phase
<Rle> <IRanges> <Rle> | <factor> <factor> <numeric> <integer>
[1] 2L 65673-67905 * | NA NA 2.16 <NA>
[2] 2L 71492-76602 * | NA NA 3.11 <NA>
[3] 2L 93377-95662 * | NA NA 2.42 <NA>
[4] 2L 107819-110289 * | NA NA 1.41 <NA>
[5] 2L 125703-131214 * | NA NA 3.25 <NA>
[6] 2L 153858-155609 * | NA NA 3.26 <NA>
FDR
<character>
[1] 0.000144173134011161
[2] 1.01370808975688e-06
[3] 1.32989250846386e-05
[4] 1.32989250846386e-05
[5] 6.29805566732406e-07
[6] 0.000828993724568052
-------
seqinfo: 6 sequences from an unspecified genome; no seqlengths- Marshall peaks had to be compiled in order to be analysed
peak_all <- union_ranges(as_granges(damsel_peaks), as_granges(marshall_peaks)) %>%
data.frame() %>% mutate(compare_num = 1:n())
peak_compare_damsel <- find_overlaps(as_granges(peak_all), as_granges(damsel_peaks)) %>% data.frame()
peak_compare_marshall <- find_overlaps(as_granges(peak_all), as_granges(marshall_peaks)) %>% data.frame()venn_data <- list(Damsel = peak_compare_damsel$compare_num, Marshall = peak_compare_marshall$compare_num)
ggVennDiagram(venn_data)
Compare all 3 approaches
peak_all <- union_ranges(union_ranges(as_granges(damsel_peaks), as_granges(vissers_peaks_mod)), as_granges(marshall_peaks)) %>% data.frame() %>%
mutate(compare_num = 1:n()) %>% as_granges()
peak_compare_damsel <- find_overlaps(as_granges(peak_all), as_granges(damsel_peaks), maxgap = 150) %>% data.frame()
peak_compare_vissers <- find_overlaps(as_granges(peak_all), as_granges(vissers_peaks_mod), maxgap = 150) %>% data.frame()
peak_compare_marshall <- find_overlaps(as_granges(peak_all), as_granges(marshall_peaks), maxgap = 150) %>% data.frame()
venn_data <- list(Damsel = peak_compare_damsel$compare_num, Vissers = peak_compare_vissers$compare_num,
Marshall = peak_compare_marshall$compare_num)
ggVennDiagram(venn_data)
- so overlap and fp is both worse than it used to be…
- but actually numbers are pretty similar to what I had - they’ve just changed slightly -worst part is damsel has about 200 extra with no overlap
- is it the lowest ranked?? - because I could accept that
peak_all <- union_ranges(union_ranges(as_granges(damsel_peaks[1:1000,]), as_granges(vissers_peaks_mod)), as_granges(marshall_peaks)) %>% data.frame() %>%
mutate(compare_num = 1:n()) %>% as_granges()
peak_compare_damsel <- find_overlaps(as_granges(peak_all), as_granges(damsel_peaks), maxgap = 150) %>% data.frame()
peak_compare_vissers <- find_overlaps(as_granges(peak_all), as_granges(vissers_peaks_mod), maxgap = 150) %>% data.frame()
peak_compare_marshall <- find_overlaps(as_granges(peak_all), as_granges(marshall_peaks), maxgap = 150) %>% data.frame()
venn_data <- list(Damsel = peak_compare_damsel$compare_num, Vissers = peak_compare_vissers$compare_num,
Marshall = peak_compare_marshall$compare_num)
ggVennDiagram(venn_data)
- ok if i did that right - that says that limiting to top 1000 doesn’t
change overlap - and actually makes things quite nice - oh but wait -
didn’t limit to top 1000 when going back
peak_all <- union_ranges(union_ranges(as_granges(damsel_peaks[1:1000,]), as_granges(vissers_peaks_mod)), as_granges(marshall_peaks)) %>% data.frame() %>%
mutate(compare_num = 1:n()) %>% as_granges()
peak_compare_damsel <- find_overlaps(as_granges(peak_all), as_granges(damsel_peaks[1:1000,]), maxgap = 150) %>% data.frame()
peak_compare_vissers <- find_overlaps(as_granges(peak_all), as_granges(vissers_peaks_mod), maxgap = 150) %>% data.frame()
peak_compare_marshall <- find_overlaps(as_granges(peak_all), as_granges(marshall_peaks), maxgap = 150) %>% data.frame()
venn_data <- list(Damsel = peak_compare_damsel$compare_num, Vissers = peak_compare_vissers$compare_num,
Marshall = peak_compare_marshall$compare_num)
ggVennDiagram(venn_data)
- ok that makes the overlap look less good - makes vissers look better -
but also makes vissers look worse - because they have more by themselves
- but this one is more correct way of doing if want it this way -omg of
course vissers and marshall are more similar to each other - they’re not
doing any kind of sample pairing
peak_all <- union_ranges(union_ranges(as_granges(damsel_peaks[1:1000,]), as_granges(vissers_peaks_mod[1:1000,])), as_granges(marshall_peaks)) %>% data.frame() %>%
mutate(compare_num = 1:n()) %>% as_granges()
peak_compare_damsel <- find_overlaps(as_granges(peak_all), as_granges(damsel_peaks[1:1000,]), maxgap = 150) %>% data.frame()
peak_compare_vissers <- find_overlaps(as_granges(peak_all), as_granges(vissers_peaks_mod[1:1000,]), maxgap = 150) %>% data.frame()
peak_compare_marshall <- find_overlaps(as_granges(peak_all), as_granges(marshall_peaks), maxgap = 150) %>% data.frame()
venn_data <- list(Damsel = peak_compare_damsel$compare_num, Vissers = peak_compare_vissers$compare_num,
Marshall = peak_compare_marshall$compare_num)
ggVennDiagram(venn_data)
- ok can’t really do this because vissers is not ranked - does make it
look interesting though - seems as if marshall is in the lower ranked
vissers anyway
peak_all <- union_ranges(union_ranges(as_granges(damsel_peaks[1:1000,]), as_granges(vissers_peaks_mod[1:1000,])), as_granges(marshall_peaks[1:1000,])) %>% data.frame() %>%
mutate(compare_num = 1:n()) %>% as_granges()
peak_compare_damsel <- find_overlaps(as_granges(peak_all), as_granges(damsel_peaks[1:1000,]), maxgap = 150) %>% data.frame()
peak_compare_vissers <- find_overlaps(as_granges(peak_all), as_granges(vissers_peaks_mod[1:1000,]), maxgap = 150) %>% data.frame()
peak_compare_marshall <- find_overlaps(as_granges(peak_all), as_granges(marshall_peaks[1:1000,]), maxgap = 150) %>% data.frame()
venn_data <- list(Damsel = peak_compare_damsel$compare_num, Vissers = peak_compare_vissers$compare_num,
Marshall = peak_compare_marshall$compare_num)
ggVennDiagram(venn_data)
peak_all <- union_ranges(union_ranges(as_granges(damsel_peaks[1:1000,]), as_granges(vissers_peaks_mod)), as_granges(marshall_peaks[1:1000,])) %>% data.frame() %>%
mutate(compare_num = 1:n()) %>% as_granges()
peak_compare_damsel <- find_overlaps(as_granges(peak_all), as_granges(damsel_peaks[1:1000,]), maxgap = 150) %>% data.frame()
peak_compare_vissers <- find_overlaps(as_granges(peak_all), as_granges(vissers_peaks_mod), maxgap = 150) %>% data.frame()
peak_compare_marshall <- find_overlaps(as_granges(peak_all), as_granges(marshall_peaks[1:1000,]), maxgap = 150) %>% data.frame()
venn_data <- list(Damsel = peak_compare_damsel$compare_num, Vissers = peak_compare_vissers$compare_num,
Marshall = peak_compare_marshall$compare_num)
ggVennDiagram(venn_data)
sessionInfo()R Under development (unstable) (2024-01-17 r85813)
Platform: x86_64-apple-darwin20
Running under: macOS Sonoma 14.1.1
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.4-x86_64/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.4-x86_64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.0
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
time zone: Australia/Melbourne
tzcode source: internal
attached base packages:
[1] stats4 stats graphics grDevices datasets utils methods
[8] base
other attached packages:
[1] ggVennDiagram_1.5.2
[2] org.Dm.eg.db_3.19.1
[3] TxDb.Dmelanogaster.UCSC.dm6.ensGene_3.12.0
[4] GenomicFeatures_1.56.0
[5] AnnotationDbi_1.66.0
[6] Biobase_2.64.0
[7] BSgenome.Dmelanogaster.UCSC.dm6_1.4.1
[8] BSgenome_1.72.0
[9] rtracklayer_1.64.0
[10] BiocIO_1.14.0
[11] Biostrings_2.72.1
[12] XVector_0.44.0
[13] ggplot2_3.5.1
[14] tidyr_1.3.1
[15] dplyr_1.1.4
[16] plyranges_1.24.0
[17] GenomicRanges_1.56.1
[18] GenomeInfoDb_1.40.1
[19] IRanges_2.38.1
[20] S4Vectors_0.42.1
[21] BiocGenerics_0.50.0
[22] Damsel_1.0.0
[23] workflowr_1.7.1
loaded via a namespace (and not attached):
[1] DBI_1.2.3 bitops_1.0-8
[3] rlang_1.1.3 magrittr_2.0.3.9000
[5] git2r_0.33.0 matrixStats_1.3.0
[7] compiler_4.4.0 RSQLite_2.3.7
[9] getPass_0.2-4 png_0.1-8
[11] callr_3.7.6 vctrs_0.6.5
[13] stringr_1.5.1.9000 pkgconfig_2.0.3
[15] crayon_1.5.3 fastmap_1.2.0
[17] labeling_0.4.3 utf8_1.2.4
[19] Rsamtools_2.20.0 promises_1.3.0
[21] rmarkdown_2.27 UCSC.utils_1.0.0
[23] ps_1.7.6 purrr_1.0.2
[25] bit_4.0.5 xfun_0.44
[27] zlibbioc_1.50.0 cachem_1.1.0
[29] jsonlite_1.8.8 blob_1.2.4
[31] highr_0.10 later_1.3.2
[33] DelayedArray_0.30.1 BiocParallel_1.38.0
[35] parallel_4.4.0 R6_2.5.1
[37] bslib_0.7.0 stringi_1.8.4
[39] jquerylib_0.1.4 Rcpp_1.0.12
[41] SummarizedExperiment_1.34.0 knitr_1.46
[43] httpuv_1.6.15 Matrix_1.7-0
[45] tidyselect_1.2.1 rstudioapi_0.16.0
[47] abind_1.4-5 yaml_2.3.8
[49] codetools_0.2-20 curl_5.2.1
[51] processx_3.8.4 lattice_0.22-6
[53] tibble_3.2.1 withr_3.0.1
[55] KEGGREST_1.44.1 evaluate_0.23
[57] pillar_1.9.0 BiocManager_1.30.23
[59] MatrixGenerics_1.16.0 whisker_0.4.1
[61] renv_1.0.7 generics_0.1.3
[63] rprojroot_2.0.4 RCurl_1.98-1.16
[65] munsell_0.5.1 scales_1.3.0
[67] glue_1.7.0 tools_4.4.0
[69] GenomicAlignments_1.40.0 fs_1.6.4
[71] XML_3.99-0.17 grid_4.4.0
[73] colorspace_2.1-1 GenomeInfoDbData_1.2.12
[75] restfulr_0.0.15 cli_3.6.2
[77] fansi_1.0.6 S4Arrays_1.4.1
[79] gtable_0.3.5 sass_0.4.9
[81] digest_0.6.35 SparseArray_1.4.8
[83] farver_2.1.2 rjson_0.2.21
[85] memoise_2.0.1 htmltools_0.5.8.1
[87] lifecycle_1.0.4 httr_1.4.7
[89] bit64_4.0.5