Jointly segment several ChIP-seq samples to find the peaks
which are the same and different across samples. The fast approximate
maximum Poisson likelihood algorithm is described in
"PeakSegJoint: fast supervised peak detection via joint segmentation
of multiple count data samples"
move some functions to PeakSegPipeline: mclapply funs, bigwig funs.
halve test time for CRAN submission -- 30 minutes is way too long. to do that we remove test-functions-real-data.R for the CRAN submission:
1: test-functions-trivial.R 0.077 2: test-functions-converted.R 0.423 3: test-functions-features.R 0.526 4: test-functions-likelihood.R 2.928 5: test-functions-binSum.R 29.041 6: test-functions-no-zeros.R 38.127 7: test-functions-PeakSegJoint.R 56.224 8: test-functions-real-data.R 674.881
Remove free/malloc for seg_mean_vec in C code, instead use pointers from one allocMatrix which is returned in the model_list, and can be used to quantify the peak height relative to nearby background.
if(interactive()) to avoid slow CRAN check on example(PeakSegJointHeuristic)
Import penaltyLearning, remove interval regression code.
Suggest penaltyLearning, used in pipeline.R with colSums features.
Use geom_rect rather than geom_step in problem.joint.plot, to avoid an error when the data set has only 1 row.
better caching: readCoverage etc.
problem.joint.predict.job with list column.
multiClusterPeaks is a new R/C function that clusters peaks into overlapping groups, with at most 1 peak per sample (in contrast, clusterPeaks allows more than one per per sample).
problem.joint.predict.many now prints progress 1 / N joint prediction problems, and skips a joint prediction problem if its peaks.bed file already exists (allows restarting).
problem.joint.targets.train now prints progress 1 / N labeled joint problems.
problem.joint now sets first and last chromStart to corresponding problem start/end, before giving the data to ProfileList and PeakSegJoint. This ensures that the peak start/end are never outside of the problem start/end.
New functions problem.joint.predict.many, problem.joint.targets.train, problem.joint.train, problem.joint.plot, problem.joint.target, problem.joint.predict
Depend ggplot2 >= 2.0
New problem.joint function in pipeline.R which is used in https://github.com/tdhock/PeakSegFPOP
Docs for PeakSegJointError.
Step6 outputs PeakSegJoint-predictions-viz/index.html which shows peak predictions with links to the UCSC genome browser. If an Input sample group is present, then we make a facetted scatterplot of Input versus other groups. If no Input sample group is present then we make a bar plot which shows only the other groups. If there are labels for Input samples, then we only report "specific" peaks in the output bed files. Specific peaks are defined as being present in not too many Input samples (threshold picked by minimizing the number of incorrect regions where Input samples are labeled as either up or down).
Run test error estimation in parallel on chunk.order.seed and test.fold.
Plot test fold distribution across chromosomes on test error summary page.
peak.or.null returns model with 1 peak (not S peaks as was before).
bugfix with train error animint for data sets with many (>100) samples.
exec/Step*.R scripts re-organized so that the genome prediction problem may be split into any number of jobs, rather than just one job per chromosome. The number of jobs can be specified as the environment variable JOBS or the R variable n.jobs when running 00_AllSteps_qsub.R.
readBigWig is faster since we now use stdout + fread, rather than writing bedGraph to an intermediate file.
Re-factor lots of the code so that we can handle an individual that has samples for more than one cell type. The inst/examples/ data set now includes bcell/McGill0322.bigwig and tcell/McGill0322.bigwig to simulate this case (although these two samples do not in fact come from the same individual). Now we call "bcell" and "tcell" the sample.group rather than cell.type, since sometimes e.g. for analyzing a bunch of H3K27ac samples, we want to include an "Input" negative control sample group (and this is not a cell type but a different experiment type).
facet_grid(sample.group + sample.id ~ .) to see the sample group in the data viz output.
Bugfix for test error computation in the output generated by exec/Step3e-estimate-test-error.R.
IntervalRegressionProblems(factor.regularization=NULL) means compute just one model with initial.regularization (rather than a sequence of increasingly more regularized models). This is useful after CV has been used to choose the best regularization parameter, and we just want to fit the model with that parameter to the entire train+validation set.
In pipeline, after training, for every test fold, plot test error as a function of number of training chunks, in order to see if the model could be improved by adding more labels.
Use maxjobs.mclapply, a thin wrapper around mclapply which limits its memory usage, and avoids getting jobs killed on the cluster.
BUGFIX for feature computation when one or more samples has no/zero coverage data.
Support for data sets with two or more label files.
Support for making predictions on samples with no labels at all -- these samples should be used in both the fitting and prediction steps.
test-qsub-pipeline.R makes sure the exec/*.R pipeline scripts run with no errors.
PeakSegJoint.c changed so that the likelihood function is defined from the first base with data to the last base with data, on any sample (before, it was ALL samples, and this is problematic now that we assume the input is a sparse profile).
Remove "read one sample coverage file and save the coverage profile for each labeled chunk" step from exec/ pipeline, since it is so fast with bigwig files now.
To support sparse bigwig data (created with -bg rather than -bga option to coverageBed, resulting in no rows with 0 coverage), binSum no longer returns an error code when chromEnd[i] != chromStart[i+1]. Instead, gaps in the coverage profile are properly treated as bases with 0 coverage.
ConvertModelList returns modelSelection.
peak1.infeasible data set.
Step3 looks for a peak in the same place as previous model, but does not enforce the seg1 < seg2 > seg3 constraint.
ConvertModelList returns segments for model with 0 peaks.
PeakSegJointSeveral function for running the C solver using several suboptimality parameters, and taking the model with the min Poisson loss for each model size.
rename seg_start_end to bin_start_end.
real data sets where buggy heuristic does not recover visually optimal segmentations.
bugfix for heuristic optimization for cases where there is a solution with non-zero index (writes new cumsum vec) before a better solution with zero index (does not write a new cumsum vec, and was stuck with the old cumsum vec).
Squared hinge loss FISTA implementation.
Bugfixes for C memory issues.
Fast C implementation of PeakSegJointHeuristic.
binSum, multi* copied from tdhock/PeakSegDP.