This is a series of example plots and tables from a combined RNA-seq/ChIP-seq study on differences between naive and memory T-cell activation. You can view the (old and messy) code for these plots here.

• p-value distributions.pdf is a series of p-value histograms for each of the contrasts tested. A contrast with no significant differential expression would exhibit a uniform distribution, while differential expression would be reflected by an excess of small p-values.
• FPKM by Peak Status H3K4.pdf shows the variation in gene expression based on the presence or absence of two histone marks in the gene promoters.
• promoter-edger-topgenes3-ql.xlsx is a spreadsheet of all promoters with differential histone modification in their promoters based on the ChIP-seq read counts.
• Promoter Peak Distance Profile.pdf shows the distribution of distances from transcription start sites to the nearest peak for the three histone modifications studied. This was used to determine the “promoter radius” for read counting. Notably, the three histone marks do not all have the same promoter radius.
• rnaseq-MDSPlots.pdf shows a series of MDS plots (similar to PCA plots) before and after correction of a known batch effect. Note the implausible zigzag-shaped progression over time before correction, compared to the more plausible cyclic time progression after.
• rnaseq-edgeR-vs-limma.pdf and rnaseq-limma-weighted-vs-uw.pdf show comparisons of p-values for all genes in each contrast of the RNA-seq data, comparing edgeR and limma-voom with/without sample quality weights. The final choice of method was limma-voom with sample quality weights.
• rnaseq-maplots-limma-sampleweights.pdf shows the MA plot for each contrast of the RNA-seq data

There are also some plots from an in-progress analysis of the same data based on sliding windows, rather than just analyzing promoter regions. You can view the code for generating these plots here, and you can view some presentation slides based on this analysis here.

• CCF-plots.pdf shows the cross-correlation functions of the ChIP-Seq data for 3 different histone marks, at several different levels of smoothing. This plot is used to determine the fragment size. You can also observe from the periodic wave-like pattern, indicating that multiple adjacent histones tend to share the same histone modification.
• CCF-plots-noBL.pdf shows the same plots as above, but without removing reads in so-called “blacklist” regions that typically contain high-coverage artifact signals. The result is a much messier plot, with many samples having an artifactual peak at the read length (dotted line) rather than the actual width of a histone (solid line).
• site-profile-plots.pdf shows plots of the relative coverage depth profiles around local coverage maxima in the ChIP-Seq data. This plot is used to determine the footprint size of the protein being imunoprecipitated. Since this is histone mark data, the footprint size should match the size of a nucleosome, about 147 bp.
• D4659vsD5053_idrplots.pdf shows an example plot from the Irreproducible Discovery Rate analysis used to identify biologically reproducible peaks in the ChIP-Seq data. The plot shows the degree of consistency in the scores for overlapping peaks in two biological replicates. Peaks with consistently high-ranking scores in both replicates are considered reproducible.
• The following reports show QC and exploratory analysis for 3 histone marks and RNA-seq: H3K4me3, H3K4me2, H3K27me3, RNA-seq. The purpose of these reports is to ensure that the modelling assumptions and strategies are appropriate for the data. Sometimes several strategies are tested against each other, and the best performer is chosen for the subsequent differential expression/modification analysis.
• The following reports show the differential expression/modification analyses and p-value histograms for the 3 histone marks and RNA-seq: H3K4me3, H3K4me2, H3K27me3, RNA-seq
• The RNA-seq data were processed using 10 different combinations of quantification pipeline and transcriptome reference. rnaseq-compare.html shows a series of comparisons designed to investigate the differences between these pipelines and references.