# diffloop: Identifying differential DNA loops from chromatin topology data

#### Caleb Lareau & Martin Aryee

#### 2017-01-25

    library(diffloop)
    library(diffloopdata)
    library(ggplot2)
    library(GenomicRanges)
    library(ggrepel)
    library(DESeq2)

## About

Due to the maintainer’s inability to figure out data package resubmission to Bioc, a compiled vignette is hosted here.

## Notes on Data

All data used in these analyses were processed from GEO and are contained in the `diffloopdata` package. Please feel free to contact Caleb (the maintainer) with any questions concerning the data or this workflow. The diffloopdata package is available here

## Session info

    sessionInfo()

    ## R version 3.3.2 (2016-10-31)
    ## Platform: x86_64-w64-mingw32/x64 (64-bit)
    ## Running under: Windows Server 2012 R2 x64 (build 9600)
    ##
    ## locale:
    ## [1] LC_COLLATE=C
    ## [2] LC_CTYPE=English_United States.1252
    ## [3] LC_MONETARY=English_United States.1252
    ## [4] LC_NUMERIC=C
    ## [5] LC_TIME=English_United States.1252
    ##
    ## attached base packages:
    ## [1] parallel  stats4    stats     graphics  grDevices utils     datasets
    ## [8] methods   base
    ##
    ## other attached packages:
    ##  [1] DESeq2_1.14.1              SummarizedExperiment_1.4.0
    ##  [3] Biobase_2.34.0             ggrepel_0.6.5
    ##  [5] GenomicRanges_1.26.2       GenomeInfoDb_1.10.2
    ##  [7] IRanges_2.8.1              S4Vectors_0.12.1
    ##  [9] BiocGenerics_0.20.0        ggplot2_2.2.1
    ## [11] diffloopdata_1.2.0         diffloop_1.2.2
    ##
    ## loaded via a namespace (and not attached):
    ##  [1] edgeR_3.16.5             splines_3.3.2
    ##  [3] foreach_1.4.3            Formula_1.2-1
    ##  [5] assertthat_0.1           statmod_1.4.27
    ##  [7] latticeExtra_0.6-28      Rsamtools_1.26.1
    ##  [9] yaml_2.1.14              RSQLite_1.1-2
    ## [11] backports_1.0.5          lattice_0.20-34
    ## [13] limma_3.30.9             digest_0.6.11
    ## [15] RColorBrewer_1.1-2       XVector_0.14.0
    ## [17] checkmate_1.8.2          colorspace_1.3-2
    ## [19] htmltools_0.3.5          Matrix_1.2-8
    ## [21] plyr_1.8.4               XML_3.98-1.5
    ## [23] biomaRt_2.30.0           genefilter_1.56.0
    ## [25] zlibbioc_1.20.0          xtable_1.8-2
    ## [27] scales_0.4.1             BiocParallel_1.8.1
    ## [29] annotate_1.52.1          tibble_1.2
    ## [31] htmlTable_1.8            pbapply_1.3-1
    ## [33] nnet_7.3-12              lazyeval_0.2.0
    ## [35] survival_2.40-1          magrittr_1.5
    ## [37] memoise_1.0.0            evaluate_0.10
    ## [39] foreign_0.8-67           tools_3.3.2
    ## [41] data.table_1.10.0        matrixStats_0.51.0
    ## [43] stringr_1.1.0            munsell_0.4.3
    ## [45] locfit_1.5-9.1           cluster_2.0.5
    ## [47] AnnotationDbi_1.36.1     Biostrings_2.42.1
    ## [49] grid_3.3.2               RCurl_1.95-4.8
    ## [51] iterators_1.0.8          bitops_1.0-6
    ## [53] base64enc_0.1-3          rmarkdown_1.3
    ## [55] gtable_0.2.0             codetools_0.2-15
    ## [57] DBI_0.5-1                reshape2_1.4.2
    ## [59] R6_2.2.0                 GenomicAlignments_1.10.0
    ## [61] gridExtra_2.2.1          zoo_1.7-14
    ## [63] knitr_1.15.1             dplyr_0.5.0
    ## [65] rtracklayer_1.34.1       Sushi_1.12.0
    ## [67] Hmisc_4.0-2              rprojroot_1.2
    ## [69] readr_1.0.0              stringi_1.1.2
    ## [71] Rcpp_0.12.9              geneplotter_1.52.0
    ## [73] rpart_4.1-10             acepack_1.4.1

## Citations

\(^1\)Hnisz, Denes, et al. “Activation of proto-oncogenes by disruption of chromosome neighborhoods.” Science (2016).
\(^2\)Ji, Xiong, et al. “3D Chromosome Regulatory Landscape of Human Pluripotent Cells.” Cell stem cell (2015).
\(^3\)Phanstiel, D, et al. “Mango: A bias correcting ChIA-PET analysis pipeline.” Bioinformatics (2015).
\(^4\)Robinson, M, et al. “edgeR: a Bioconductor package for differential expression analysis of digital gene expression data.” Bioinformatics (2010).
\(^5\)Law, C, et al. “Voom: precision weights unlock linear model analysis tools for RNA-seq read counts.” Genome Biology (2014).