Entering edit mode
Hi Rory,
I am using DiffBind to identify sites differentially bound (affinity analysis) by a transcription factor in control (WT), heterozygous (HET), and knockout (KO) cells. The data was generated using CUT&RUN. The TF was profiled for each genotype, and each genotype also has a negative control Rb IgG bam file.
I used both default normalization method and spike in normalization. The results are similar for both. DiffBind identifies a good number of sites that are significantly differentially bound between WT and KO. Comparing HET v. KO shows a reduction in sites that are significantly differentially bound, as expected.
What I am most interested in is which sites have significantly reduced peak height between WT and HET.
However, there is only 1 site that is significant when comparing WT v. HET. But on visual inspection, many sites have peaks that are about 50% reduced in peak height in the HET compared to WT. I am wondering why these sites are not being identified as significantly (FDR<0.05) differentially bound and if you have advice on how to capture these sites.
Is it possible to get a comparison that is (WT v. KO) v. (HET v. KO)? (I'm assuming WT v. HET is different from this since it does not account for KO).
Thank you for your time!
# spike in normalization
dbObj = dba.normalize(dbObj, normalize=DBA_NORM_LIB, spikein=TRUE)
dbObj = dba.analyze(dbObj)
dba.contrast(dbObj, bGetCoefficients=T)
[1] "Intercept" "Condition_HET_vs_WT" "Condition_KO_vs_WT"
# results
Design: [~Condition] | 3 Contrasts:
Factor Group Samples Group2 Samples2 DB.edgeR DB.DESeq2
1 Condition WT 7 HET 6 1 1
2 Condition WT 7 KO 6 6453 3652
3 Condition HET 6 KO 6 4044 2711
# please also include the results of running the following in an R session
sessionInfo( )
R version 4.1.2 (2021-11-01)
Platform: x86_64-conda-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)
Matrix products: default
BLAS/LAPACK: /programs/biogrids/x86_64-linux/r/4.1/lib/libopenblasp-r0.3.17.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] parallel stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] BiocParallel_1.28.3 csaw_1.28.0
[3] profileplyr_1.10.2 RColorBrewer_1.1-3
[5] forcats_0.5.1 stringr_1.4.0
[7] dplyr_1.0.9 purrr_0.3.4
[9] readr_2.1.2 tidyr_1.2.0
[11] tibble_3.1.7 ggplot2_3.3.6
[13] tidyverse_1.3.1 DiffBind_3.2.7
[15] SummarizedExperiment_1.24.0 Biobase_2.54.0
[17] MatrixGenerics_1.6.0 matrixStats_0.62.0
[19] GenomicRanges_1.46.1 GenomeInfoDb_1.30.0
[21] IRanges_2.28.0 S4Vectors_0.30.1
[23] BiocGenerics_0.40.0
loaded via a namespace (and not attached):
[1] rappdirs_0.3.3
[2] rtracklayer_1.54.0
[3] R.methodsS3_1.8.1
[4] coda_0.19-4
[5] bit64_4.0.5
[6] irlba_2.3.5
[7] DelayedArray_0.20.0
[8] R.utils_2.11.0
[9] data.table_1.14.2
[10] hwriter_1.3.2.1
[11] KEGGREST_1.34.0
[12] RCurl_1.98-1.6
[13] doParallel_1.0.17
[14] generics_0.1.1
[15] GenomicFeatures_1.46.5
[16] org.Mm.eg.db_3.14.0
[17] preprocessCore_1.56.0
[18] EnrichedHeatmap_1.22.0
[19] RSQLite_2.2.14
[20] shadowtext_0.1.0
[21] bit_4.0.4
[22] tzdb_0.3.0
[23] enrichplot_1.14.2
[24] xml2_1.3.3
[25] lubridate_1.8.0
[26] assertthat_0.2.1
[27] viridis_0.6.2
[28] amap_0.8-18
[29] apeglm_1.16.0
[30] hms_1.1.1
[31] fansi_0.5.0
[32] restfulr_0.0.13
[33] progress_1.2.2
[34] caTools_1.18.2
[35] dbplyr_2.1.1
[36] readxl_1.4.0
[37] geneplotter_1.72.0
[38] igraph_1.3.1
[39] DBI_1.1.2
[40] htmlwidgets_1.5.4
[41] ellipsis_0.3.2
[42] rGREAT_1.24.0
[43] backports_1.4.1
[44] annotate_1.72.0
[45] biomaRt_2.50.3
[46] vctrs_0.4.1
[47] cachem_1.0.6
[48] withr_2.4.3
[49] ggforce_0.3.3
[50] BSgenome_1.62.0
[51] bdsmatrix_1.3-4
[52] GenomicAlignments_1.30.0
[53] treeio_1.18.1
[54] prettyunits_1.1.1
[55] TxDb.Mmusculus.UCSC.mm10.knownGene_3.10.0
[56] cluster_2.1.3
[57] DOSE_3.20.1
[58] ape_5.6
[59] lazyeval_0.2.2
[60] crayon_1.4.2
[61] genefilter_1.76.0
[62] edgeR_3.36.0
[63] pkgconfig_2.0.3
[64] tweenr_1.0.2
[65] nlme_3.1-157
[66] rlang_1.0.2
[67] lifecycle_1.0.1
[68] filelock_1.0.2
[69] BiocFileCache_2.2.1
[70] modelr_0.1.8
[71] invgamma_1.1
[72] cellranger_1.1.0
[73] polyclip_1.10-0
[74] tiff_0.1-11
[75] Matrix_1.4-0
[76] aplot_0.1.4
[77] ashr_2.2-54
[78] chipseq_1.42.0
[79] boot_1.3-28
[80] reprex_2.0.1
[81] GlobalOptions_0.1.2
[82] pheatmap_1.0.12
[83] png_0.1-7
[84] viridisLite_0.4.0
[85] rjson_0.2.21
[86] bitops_1.0-7
[87] R.oo_1.24.0
[88] KernSmooth_2.23-20
[89] Biostrings_2.62.0
[90] blob_1.2.3
[91] shape_1.4.6
[92] mixsqp_0.3-43
[93] SQUAREM_2021.1
[94] qvalue_2.26.0
[95] ShortRead_1.52.0
[96] jpeg_0.1-9
[97] gridGraphics_0.5-1
[98] TxDb.Mmusculus.UCSC.mm9.knownGene_3.2.2
[99] scales_1.2.0
[100] memoise_2.0.1
[101] magrittr_2.0.3
[102] plyr_1.8.7
[103] gplots_3.1.3
[104] zlibbioc_1.40.0
[105] compiler_4.1.2
[106] scatterpie_0.1.7
[107] TxDb.Hsapiens.UCSC.hg38.knownGene_3.13.0
[108] BiocIO_1.4.0
[109] bbmle_1.0.24
[110] plotrix_3.8-2
[111] clue_0.3-60
[112] DESeq2_1.34.0
[113] Rsamtools_2.10.0
[114] cli_3.3.0
[115] systemPipeR_2.0.8
[116] XVector_0.34.0
[117] patchwork_1.1.1
[118] MASS_7.3-57
[119] tidyselect_1.1.2
[120] stringi_1.7.6
[121] emdbook_1.3.12
[122] yaml_2.3.5
[123] GOSemSim_2.20.0
[124] locfit_1.5-9.5
[125] latticeExtra_0.6-29
[126] ggrepel_0.9.1
[127] grid_4.1.2
[128] fastmatch_1.1-3
[129] tools_4.1.2
[130] circlize_0.4.14
[131] rstudioapi_0.13
[132] foreach_1.5.2
[133] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
[134] metapod_1.2.0
[135] gridExtra_2.3
[136] farver_2.1.0
[137] ggraph_2.0.5
[138] digest_0.6.29
[139] Rcpp_1.0.8.3
[140] broom_0.8.0
[141] org.Hs.eg.db_3.14.0
[142] httr_1.4.3
[143] AnnotationDbi_1.56.2
[144] ComplexHeatmap_2.11.1
[145] colorspace_2.0-3
[146] rvest_1.0.2
[147] XML_3.99-0.9
[148] fs_1.5.2
[149] truncnorm_1.0-8
[150] splines_4.1.2
[151] yulab.utils_0.0.4
[152] tidytree_0.3.9
[153] graphlayouts_0.8.0
[154] ggplotify_0.1.0
[155] xtable_1.8-4
[156] jsonlite_1.8.0
[157] ggtree_3.2.1
[158] soGGi_1.24.1
[159] tidygraph_1.2.1
[160] ggfun_0.0.6
[161] R6_2.5.1
[162] pillar_1.7.0
[163] htmltools_0.5.2
[164] glue_1.6.0
[165] fastmap_1.1.0
[166] codetools_0.2-18
[167] ChIPseeker_1.28.3
[168] fgsea_1.20.0
[169] GreyListChIP_1.26.0
[170] mvtnorm_1.1-3
[171] utf8_1.2.2
[172] lattice_0.20-45
[173] numDeriv_2016.8-1.1
[174] curl_4.3.2
[175] gtools_3.9.2
[176] GO.db_3.14.0
[177] survival_3.3-1
[178] limma_3.50.0
[179] munsell_0.5.0
[180] DO.db_2.9
[181] GetoptLong_1.0.5
[182] GenomeInfoDbData_1.2.7
[183] iterators_1.0.14
[184] haven_2.5.0
[185] reshape2_1.4.4
[186] gtable_0.3.0
