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][1].

[1]: https://github.com/DarwinAwardWinner/cd4-histone-paper-code

- [`p-value distributions.pdf`](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`](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`](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`](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`](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`](rnaseq-edgeR-vs-limma.pdf) and
  [`rnaseq-limma-weighted-vs-uw.pdf`](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`](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][2],
and you can view some presentation slides based on this analysis
[here][3].

[2]: https://github.com/DarwinAwardWinner/CD4-csaw
[3]: ./ChIP-Seq presentation.pdf

- [`CCF-plots.pdf`](CCF-plots.pdf) shows the cross-correlation
  functions of several 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`](CCF-plots-noBL.pdf) show the same plots, 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 a peak at the read
  length (dotted line) rather than the actual width of a histone
  (solid line).
- [`site-profile-plots.pdf`](site-profile-plots.pdf) shows plots of
  the relative coverage depth profiles around local coverage maxima.
  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.
- [`H3K4me3-window-abundance-vs-peaks.pdf`](H3K4me3-window-abundance-vs-peaks.pdf)
  shows the association between peak overlap status and abundance for
  all windows in the genome. As expected, windows that overlap a
  called peak tend to have a higher abundance than other windows.
- [`H3K4me3 Selected Sample 10KB Bin MA Plots.pdf`](H3K4me3 Selected
  Sample 10KB Bin MA Plots.pdf) shows selected MA plots between
  samples demonstrating the effects of several different potential
  normalization methods.
- [`H3K4me3-normfactors.pdf`](H3K4me3-normfactors.pdf) shows the
  associations between normalization factor and experimental variables
  for several different normalization methods.