Changes in version 2024.10.1
- update src/*.c to use R_ versions of Calloc and Free, so that compilation works with _R_USE_STRICT_R_HEADERS_=true, thanks CRAN.
Changes in version 2024.6.27
- update PeakSegJoint.c to avoid integer overflow, thanks CRAN.
Changes in version 2024.1.24
- update NEWS and Rd files for CRAN.
Changes in version 2023.8.25
- re-inlinedocs to get default arg docs for un-exported functions, which are now required to avoid CRAN NOTE.
Changes in version 2023.4.24
- change fun() to fun(void) in C code to silence CRAN warnings from
gcc-12 default or gcc 10 with -Wstrict-prototypes.
- reformat NEWS file.
Changes in version 2022.4.6
- Update C code to use Calloc/Free.
Changes in version 2020.2.13
- fix int overflow problem in C code by using double instead.
Changes in version 2018.10.3
- memory problem fix -- now we make sure that n_bins >= bin_factor*2.
- avoid memory leak by free'ing profile_list before stopping with an error.
Changes in version 2018.01.09
- In PeakSegJointFaster, only consider peaks which are inside range of observed non-zero data.
Changes in version 2017.12.23
- Added PeakSegJointFaster C code with corresponding R functions: - Step1 (GridSearch) and Step2 (SearchNearPeak) sub-routines are run, with the assumption that peak is present in all samples For samples with infeasible means during optimization, we use flat loss rather than peak loss. - PeakSegJointFasterOne returns likelihood vectors for flat and peak models, along with matrix of segment mean values, for one bin.factor value. - PeakSegJointFaster returns the same, minimizing over several bin.factor values, then computing modelSelection data.frames, (the likelihood vectors are sorted to determine the path of models). Infeasible models are excluded in R code before modelSelection. - example(PeakSegJointFasterOne) shows that it is O(N) whereas PeakSegJointHeuristic is O(N^2).
Changes in version 2017.08.11
- 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:
- f seconds
Changes in version 2017.08.08
- Remove free/malloc for seg[123]_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)
Changes in version 2017.06.20
- Import penaltyLearning, remove interval regression code.
Changes in version 2017.05.05
- 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.
Changes in version 2017.04.20
- better caching: readCoverage etc.
- problem.joint.predict.job with list column.
Changes in version 2017.02.27
- 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.
Changes in version 2016.11.01
- New functions problem.joint.predict.many, problem.joint.targets.train, problem.joint.train, problem.joint.plot, problem.joint.target, problem.joint.predict
Changes in version 2016.10.16
- Depend ggplot2 >= 2.0
- New problem.joint function in pipeline.R which is used in https://github.com/tdhock/PeakSegFPOP
Changes in version 2016.01.22
- Docs for PeakSegJointError.
Changes in version 2015.11.03
- 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).
Changes in version 2015.09.21
- Run test error estimation in parallel on chunk.order.seed and test.fold.
Changes in version 2015.09.15
- 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.
Changes in version 2015.09.14
- 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
Changes in version 2015.09.11
- 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.
Changes in version 2015.09.04
- 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.
Changes in version 2015.08.06
- BUGFIX for feature computation when one or more samples has no/zero coverage data.
Changes in version 2015.08.05
- 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.
Changes in version 2015.07.30
- 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).
Changes in version 2015.07.14
- 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.
Changes in version 2015.06.19
- 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.
Changes in version 2015.05.22
- ConvertModelList returns modelSelection.
Changes in version 2015.05.15
- 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.
Changes in version 2015.05.14
- 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).
Changes in version 2015.05.05
- Squared hinge loss FISTA implementation.
Changes in version 2015.04.17
- Bugfixes for C memory issues.
Changes in version 2015.04.14
- Fast C implementation of PeakSegJointHeuristic.
Changes in version 2015.04.02
- binSum, multi* copied from tdhock/PeakSegDP.