Hello,
I'm trying to distinguish whether the overall binding pattern of a protein (Protein A) differs between two conditions. When I use a sampleSheet.csv that only includes the data for Protein A and plot the Heatmap (after counting, analyzing etc...) the time points are nested among each other in the clustering dendrogram. I conclude that there are little differences in binding between the two timepoints.
However when I include the data for another protein (Protein B) being ChIPed for, the Heatmap produced separates the binding data for the two different proteins (as expected), however the dendrogram for Protein A has now changed and does in fact separate the time points quite clearly.
Why does the combination of Protein A + B in the counting/analysis process create a different clustering relationship for Protein A than if Protein A is included in the analysis on its own?
DiffBind code:
samp <- read.csv('samplesheet.csv', header=T)
dbsamp <- dba(sampleSheet = samp)
dbcount <- dba.count(dbsamp,
bUseSummarizeOverlaps = TRUE,
bParallel = T)
dbcontrast <- dba.contrast(dbcount, minMembers = 3,
categories = c(DBA_TREATMENT, DBA_FACTOR))
dbanalyze <- dba.analyze(dbcontrast, method = DBA_EDGER)
dba.plotHeatmap(dbanalyze, correlations = T,
ColAttributes = DBA_GROUP,
RowAttributes = DBA_GROUP,
method = DBA_EDGER)
Session Info:
> sessionInfo()
R version 3.3.3 (2017-03-06)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 16.04.2 LTS
locale:
[1] LC_CTYPE=en_GB.UTF-8 LC_NUMERIC=C LC_TIME=en_GB.UTF-8 LC_COLLATE=en_GB.UTF-8
[5] LC_MONETARY=en_GB.UTF-8 LC_MESSAGES=en_GB.UTF-8 LC_PAPER=en_GB.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] parallel stats4 stats graphics grDevices utils datasets methods base
other attached packages:
[1] RColorBrewer_1.1-2 pheatmap_1.0.8 DiffBind_2.2.9 SummarizedExperiment_1.4.0
[5] Biobase_2.34.0 GenomicRanges_1.26.4 GenomeInfoDb_1.10.3 IRanges_2.8.2
[9] S4Vectors_0.12.2 BiocGenerics_0.20.0
loaded via a namespace (and not attached):
[1] Category_2.40.0 bitops_1.0-6 tools_3.3.3 backports_1.0.5 R6_2.2.0
[6] rpart_4.1-10 KernSmooth_2.23-15 Hmisc_4.0-2 DBI_0.6 lazyeval_0.2.0
[11] colorspace_1.3-2 nnet_7.3-12 gridExtra_2.2.1 DESeq2_1.14.1 sendmailR_1.2-1
[16] graph_1.52.0 htmlTable_1.9 rtracklayer_1.34.2 caTools_1.17.1 scales_0.4.1
[21] checkmate_1.8.2 BatchJobs_1.6 genefilter_1.56.0 RBGL_1.50.0 stringr_1.2.0
[26] digest_0.6.12 Rsamtools_1.26.1 foreign_0.8-67 AnnotationForge_1.16.1 XVector_0.14.1
[31] base64enc_0.1-3 htmltools_0.3.5 limma_3.30.13 htmlwidgets_0.8 RSQLite_1.1-2
[36] BBmisc_1.11 GOstats_2.40.0 hwriter_1.3.2 BiocParallel_1.8.1 gtools_3.5.0
[41] acepack_1.4.1 dplyr_0.5.0 RCurl_1.95-4.8 magrittr_1.5 GO.db_3.4.0
[46] Formula_1.2-1 Matrix_1.2-8 Rcpp_0.12.10 munsell_0.4.3 stringi_1.1.3
[51] edgeR_3.16.5 zlibbioc_1.20.0 gplots_3.0.1 fail_1.3 plyr_1.8.4
[56] grid_3.3.3 gdata_2.17.0 lattice_0.20-34 Biostrings_2.42.1 splines_3.3.3
[61] GenomicFeatures_1.26.3 annotate_1.52.1 locfit_1.5-9.1 knitr_1.15.1 rjson_0.2.15
[66] systemPipeR_1.8.1 geneplotter_1.52.0 biomaRt_2.30.0 XML_3.98-1.5 ShortRead_1.32.1
[71] latticeExtra_0.6-28 data.table_1.10.4 gtable_0.2.0 amap_0.8-14 assertthat_0.1
[76] ggplot2_2.2.1 xtable_1.8-2 survival_2.41-2 tibble_1.2 GenomicAlignments_1.10.1
[81] AnnotationDbi_1.36.2 memoise_1.0.0 cluster_2.0.6 brew_1.0-6 GSEABase_1.36.0Many thanks,
Tim.
