resume/examples/Salomon/CD4

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.