compare-Damsel-Vissers
caitlinpage
2024-09-02
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(ggVennDiagram)
Attaching package: 'ggVennDiagram'The following object is masked from 'package:tidyr':
unitelibrary(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)gatc_regions <- getGatcRegions(BSgenome.Dmelanogaster.UCSC.dm6::BSgenome.Dmelanogaster.UCSC.dm6)$regions %>% data.frame()Warning in .local(x, row.names, optional, ...): 'optional' argument was ignored
Warning in .local(x, row.names, optional, ...): 'optional' argument was ignored
Warning in .local(x, row.names, optional, ...): 'optional' argument was ignored
Warning in .local(x, row.names, optional, ...): 'optional' argument was ignored
Warning in .local(x, row.names, optional, ...): 'optional' argument was ignored
Warning in .local(x, row.names, optional, ...): 'optional' argument was ignored
Warning in .local(x, row.names, optional, ...): 'optional' argument was ignoredWarning in GenomeInfoDb::renameSeqlevels(x = df_, value = newStyle): invalid
seqlevels 'chrM' ignoredCompare DM results
damsel_dm <- readRDS("../output/damsel_dm.rds")
vissers_dm <- readRDS("../output/vissers_dm.rds")head(vissers_dm)Loading required package: limmaWarning: package 'limma' was built under R version 4.4.1
Attaching package: 'limma'The following object is masked from 'package:BiocGenerics':
plotMA logFC logCPM LR PValue groupSd
chr2L-231 0.8135627 0.15655127 0.4788446 0.48894612 0
chr2L-372 0.7964397 0.20584811 0.5453325 0.46023115 0
chr2L-5154 1.5970202 -0.66316083 4.2724166 0.03873580 0
chr2L-5303 1.4069922 2.10057246 5.5817359 0.01814872 0
chr2L-6025 1.0885501 2.70375018 3.3924308 0.06549632 0
chr2L-6881 0.7484152 0.05202975 0.7966800 0.37208787 0dm_compare <- damsel_dm %>%
mutate(vissers_logFC = vissers_dm[match(damsel_dm$Position, row.names(vissers_dm)), "logFC"]) %>%
mutate(vissers_PValue = vissers_dm[match(.$Position, row.names(vissers_dm)), "PValue"]) %>%
mutate(vissers_dm = vissers_dm[match(damsel_dm$Position, row.names(vissers_dm)), "significant"],
vissers_logFC = ifelse(is.na(vissers_logFC), 0, vissers_logFC),
vissers_PValue = ifelse(is.na(vissers_PValue), 1, vissers_PValue))compare P Values
dm_compare %>%
ggplot(aes(x = vissers_PValue, y = PValue)) +
geom_point(alpha = 0.1) +
geom_vline(xintercept = 0.05, colour = "red", linetype = "dashed") +
geom_hline(yintercept = 0.05, colour = "red", linetype = "dashed") +
geom_smooth()`geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'
cor.test(dm_compare$vissers_PValue, dm_compare$PValue)
Pearson's product-moment correlation
data: dm_compare$vissers_PValue and dm_compare$PValue
t = 2295, df = 383652, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
0.9652407 0.9656704
sample estimates:
cor
0.9654562 Compare peaks
damsel_peaks <- readRDS("../output/damsel_peaks.rds")
vissers_peaks <- readRDS("../output/vissers_peaks.rds")
vissers_peaks_mod <- readRDS("../output/vissers_peaks_mod.rds")- 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(peak_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$peak_num, Vissers = peak_compare_vissers$peak_num.x)
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] limma_3.60.4
[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] ggVennDiagram_1.5.2
[14] ggplot2_3.5.1
[15] tidyr_1.3.1
[16] dplyr_1.1.4
[17] plyranges_1.24.0
[18] GenomicRanges_1.56.1
[19] GenomeInfoDb_1.40.1
[20] IRanges_2.38.1
[21] S4Vectors_0.42.1
[22] BiocGenerics_0.50.0
[23] Damsel_1.0.0
[24] 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] mgcv_1.9-1 getPass_0.2-4
[11] png_0.1-8 callr_3.7.6
[13] vctrs_0.6.5 stringr_1.5.1.9000
[15] pkgconfig_2.0.3 crayon_1.5.3
[17] fastmap_1.2.0 labeling_0.4.3
[19] utf8_1.2.4 Rsamtools_2.20.0
[21] promises_1.3.0 rmarkdown_2.27
[23] UCSC.utils_1.0.0 ps_1.7.6
[25] purrr_1.0.2 bit_4.0.5
[27] xfun_0.44 zlibbioc_1.50.0
[29] cachem_1.1.0 jsonlite_1.8.8
[31] blob_1.2.4 highr_0.10
[33] later_1.3.2 DelayedArray_0.30.1
[35] BiocParallel_1.38.0 parallel_4.4.0
[37] R6_2.5.1 bslib_0.7.0
[39] stringi_1.8.4 jquerylib_0.1.4
[41] Rcpp_1.0.12 SummarizedExperiment_1.34.0
[43] knitr_1.46 splines_4.4.0
[45] httpuv_1.6.15 Matrix_1.7-0
[47] tidyselect_1.2.1 rstudioapi_0.16.0
[49] abind_1.4-5 yaml_2.3.8
[51] codetools_0.2-20 curl_5.2.1
[53] processx_3.8.4 lattice_0.22-6
[55] tibble_3.2.1 KEGGREST_1.44.1
[57] withr_3.0.1 evaluate_0.23
[59] pillar_1.9.0 BiocManager_1.30.23
[61] MatrixGenerics_1.16.0 whisker_0.4.1
[63] renv_1.0.7 generics_0.1.3
[65] rprojroot_2.0.4 RCurl_1.98-1.16
[67] munsell_0.5.1 scales_1.3.0
[69] glue_1.7.0 tools_4.4.0
[71] GenomicAlignments_1.40.0 fs_1.6.4
[73] XML_3.99-0.17 grid_4.4.0
[75] colorspace_2.1-1 nlme_3.1-164
[77] GenomeInfoDbData_1.2.12 restfulr_0.0.15
[79] cli_3.6.2 fansi_1.0.6
[81] S4Arrays_1.4.1 gtable_0.3.5
[83] sass_0.4.9 digest_0.6.35
[85] SparseArray_1.4.8 farver_2.1.2
[87] rjson_0.2.21 memoise_2.0.1
[89] htmltools_0.5.8.1 lifecycle_1.0.4
[91] httr_1.4.7 statmod_1.5.0
[93] bit64_4.0.5