The increase in interest for Hi-C methods in the chromatin community has led to a need for more user-friendly and powerful analysis methods. The few currently available software packages for Hi-C do not allow a researcher to quickly summarize and visualize their data. An easy to use software package, which can generate a comprehensive set of publication-quality plots, would allow researchers to swiftly go from raw Hi-C data to interpretable results.
Here, we present GENome Organisation Visual Analytics (GENOVA): a software suite to perform in-depth analyses on various levels of genome organisation, using Hi-C data. GENOVA facilitates the comparison between multiple datasets and supports the majority of mapping-pipelines.
Quick, reliable, easy and flexible.
We have provided a quite lengthy vignette, so please read that first. If there are still unanswered questions, please use the issue-tracker.
Github-hash: 9951098
- ATA: outputs also the used TADs.bed
- HiC_matrixplot: smoothing of the regions with no data (e.g. the white stripes) has been implemented. This is done by filling these bins with the result of a Nadaraya/Watson normalization of the kernel. Set
smoothNA
to true to use andsmoothBandwidth
to tweak - HiC_matrixplot: If
chip.yMax
is NULL, a warning will be given for the used yMaxes. - PE-SCAn: A maximal distance can be given with
maxDist
. - PE-SCAn: verbosity can be limited off with
verbose = F
- PE-SCAn: Catches errors when no matrices are found by cov2d.
- PE-SCAn: A treshold for the minimum amount of BED-entries per chromosome is added:
minComparables
. - PE-SCAn: Shifted bed-entries bigger than their chromosome are now fixed (they "wrap around" to the start)
- PE-SCAn: Returns not only the O/E matrix, but a list with a O/E score-matrix (if shift != 0), otherwise an observed score-matrix, the underlying signal and background-matrices and the shift used.
- visualise.PESCAn.ggplot: several checks to see if the input-data is comparable (including shift).
- visualise.PESCAn.ggplot: colorscales for O/E and O are better (i.e. no log2-divergent scale for shift==0 matrices).
- cis.compartment.plot: a second experiment-object can be used. This is plotted in the lower-left corner. Note: only the first matrix is invisibly returned.
- cis.compartment.plot: smoothing of the regions with no data (e.g. the white stripes) has been implemented. This is done by filling these bins with the result of a Nadaraya/Watson normalization of the kernel. Set
smoothNA
to true to use andsmoothBandwidth
to tweak - getTADstats: a new function to get some stats on ATA-results. Still quite buggy.
- saddle: Forces user for either CS or chip.
- README now contains actual text.
- HiC_matrixplot:
bed.col
andbw.col
are merged to chip.col. This argument can take a vector of four colours, parallel to thechip
-argument. - HiC_matrixplot:
yMax
is renamed tochip.ymax
. - visualise.PESCAn.ggplot: works woth new output of PE-SCAn
- PE-SCAn: cov2d loops over a vectorised list without duplicates, which speeds up the code.
- PE-SCAn: cleaned up cov2d's return-code
- quantifyAPA: the
enrichmentType
argument lets you choose between pixel/mean(backgroundRegions) and the fraction of loops with more than 50% higher signal than the background. - visualise.compartmentStrength: at least one bin is used (bugfix).
- saddle: extra effort to use the CS is taken by matching the CS-vector and the eigen-vector. This will fix problems due to big centromeres or shaky/different centromere-calls.
- vignette: fixed typo's