Overview
These chipseq tracks have been generated by each peak caller. All peak-callers
create a track of called peaks. Some of them also create normalized signal
tracks, and those are shown here as well. These normaizlied tracks make it
easier to diagnose the quality of called peaks, since these signals are what
the peak-caller "sees" when it's running.
These normalized tracks are scaled differently for each peak-caller, see below
for details.
Selecting data
Checkbox matrix
Use checkboxes to show/hide data. Typically, the most meaningful variables have
been incorporated into the checkbox matrix. For ChIP-seq experiments, this is
usually antibody and celltype, but of course this will vary by experiment.
Filter menus
Underneath the checkbox matrix are drop-down menus. These show secondary ways
of selecting data. The filters update when you make changes, so changing one
filter will cause options to be grayed out in other filters if the data don't
exist for an option.
algorithm will subset all tracks by algorithm. run label subsets track
by the run label, which is highly dependent on the experiment.
The npeaks filter can be ignored. It's just a by-product of putting the
peak counts in the list of tracks.
Other filters may be included on an experiment-by-experiment basis.
Showing/hiding peaks and signal
Look just above the checkbox matrix for the "Select views" section. These
views are subsets of signal for each of the peak-callers that were used, plus
one track for all peak-callers.
If you want to see signal, the signal view for the corresponding algorithm
should be set to "full". If you want to see peaks, the peaks view should be
set to "dense". If you want to disable something, change it to "hide".
See below for details on each algorithm's signal tracks.
Adjusting y-axes for signal
Each of the signal views (just above the checkbox matrix) can have its name
clicked to change the settings for just that view. The most useful setting is
"vertical viewing range", which sets the ymin/ymax values. By default this is
set to values that work well across a wide range of experiments, but you may
have to tweak this for your particular experiment.
Config
The full config file is below. It was used to define peak-calling runs for the
pipeline, and serves as a reference to connect together the IP and input for
each run. You can ignore it for the most part, but it can be used to dig into
the details of exactly what parameters and what samples went into each run:
differential: []
final_bam_ext: .trim.fastq.unique.nodups.bam
peak_calling:
- algorithm: macs2
control:
- LM49_input-brains
label: shep
treatment:
- LM50_shep-brains
- algorithm: macs2
control:
- LM49_input-brains
label: mod
treatment:
- LM51_mod-brains
- algorithm: macs2
control:
- LM49_input-brains
label: suhw
treatment:
- LM52_suhw-brains
- algorithm: macs2
control:
- LM49_input-brains
label: cp190
treatment:
- LM53_cp190-brains
- algorithm: spp
control:
- LM49_input-brains
extra: --fdr=0.01
label: shep
treatment:
- LM50_shep-brains
- algorithm: spp
control:
- LM49_input-brains
extra: --fdr=0.01
label: mod
treatment:
- LM51_mod-brains
- algorithm: spp
control:
- LM49_input-brains
extra: --fdr=0.01
label: suhw
treatment:
- LM52_suhw-brains
- algorithm: spp
control:
- LM49_input-brains
extra: --fdr=0.01
label: cp190
treatment:
- LM53_cp190-brains
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