The IDR (Irreproducible Discovery Rate) framework is a unified approach to measure the reproducibility of findings identified from replicate experiments and provide highly stable thresholds based on reproducibility. Unlike the usual scalar measures of reproducibility, the IDR approach creates a curve, which quantitatively assesses when the findings are no longer consistent across replicates. In layman's terms, the IDR method compares a pair of ranked lists of identifications (such as ChIP-seq peaks). These ranked lists should not be pre-thresholded i.e. they should provide identifications across the entire spectrum of high confidence/enrichment (signal) and low confidence/enrichment (noise). The IDR method then fits the bivariate rank distributions over the replicates in order to separate signal from noise based on a defined confidence of rank consistency and reproducibility of identifications i.e the IDR threshold.
The method was developed by Qunhua Li and Peter Bickel's group and is extensively used by the ENCODE and modENCODE projects and is part of their ChIP-seq guidelines and standards.
Installation ------------ * Get the current repo ``` wget https://github.com/nboley/idr/archive/2.0.0.zip ``` * Install the dependencies - python3 - python3 headers - numpy - setuptools - matplotlib (only required for plotting the results) In Ubuntu 14.04+ one can run: (sudo) apt-get install python3-dev python3-numpy python3-setuptools python3-matplotlib In a shared environment, the dependencies and idr package may need to be installed locally. [Anaconda](http://continuum.io/downloads#py34) largely automates this process. To install anaconda, which includes all the neessary dependencies: ``` Download [Anaconda3-2.2.0-Linux-x86_64.sh](http://continuum.io/downloads#py34) bash Anaconda3-2.2.0-Linux-x86_64.sh ``` * Download and unzip the idr code ``` wget https://github.com/nboley/idr/archive/2.0.0.zip unzip 2.0.0.zip cd 2.0.0/ ``` * Then install idr ``` python3 setup.py install ``` Usage ----- List all the options ``` idr -h ``` Sample idr run using test peak files in the repo ``` idr --samples ../idr/test/data/peak1 ../idr/test/data/peak2 ``` Run idr using an oracle peak list (e.g. peaks called from merged replicates): ``` idr --samples ../idr/test/data/peak1 ../idr/test/data/peak2 --peak-list ../idr/test/data/merged_peaks ``` ### Peak matching The method in which peaks are matched can significantly affect the output. We have chosen defaults that we believe are reaosnable in the vast majoroity of cases, but it may be worth exploring the various options for your data set. * --peak-list is *not* provided Peaks are grouped by overlap and then merged. The merged peak aggregate value is determined by --peak-merge-method. Peaks that don't overlap another peak in every other replicate are not included unless --use-nonoverlapping-peaks is set. * --peak-list *is* provided Peaks are grouped by overlap, and then for each oracle peak a single peak from each replicate is chosen that overlaps the oracle peak. If there are multiple peaks that overlap the oracle, then ties are broken by applying the following criteria in order: 1) choose the replicate peak with a summit closest to the oracle peak's summit 2) choose the replicate peak that has the largest overlap with the oracle peak 3) choose the replicate peak with the highest score Output ------ ### Output file format The output format mimics the input file type, with some additional fields. We provide an example for narrow peak files - note that the first 6 columns are a standard bed6, the first 10 columns are a standard narrowPeak. Also, for columns 7-10, only the score that the IDR code used for rankign will be set - the remaining two columns will be set to -1. Broad peak output files are the same *except* that they do not include the the summit columns (e.g. columns 10, 18, and 22 for samples with 2 replicates) 1. chrom string Name of the chromosome for common peaks 2. chromStart int The starting position of the feature in the chromosome or scaffold for common peaks, shifted based on offset. The first base in a chromosome is numbered 0. 3. chromEnd int The ending position of the feature in the chromosome or scaffold for common peaks. The chromEnd base is not included in the display of the feature. 4. name string Name given to a region (preferably unique) for common peaks. Use '.' if no name is assigned. 5. score int Contains the scaled IDR value, min(int(log2(-125*IDR), 1000). e.g. peaks with an IDR of 0 have a score of 1000, idr 0.05 have a score of int(-125*log2(0.05)) = 540, and idr 1.0 has a score of 0. 6. strand [+-.] Use '.' if no strand is assigned. 7. signalValue float Measurement of enrichment for the region for merged peaks 8. p-value float Merged peak p-value 9. q-value float Merged peak q-value 10. summit int Merged peak summit 11. localIDR float -log10(Local IDR value) 12. globalIDR float -log10(Global IDR value) 13. rep1_chromStart int The starting position of the feature in the chromosome or scaffold for common replicate 1 peaks, shifted based on offset. The first base in a chromosome is numbered 0. 14. rep1_chromEnd int The ending position of the feature in the chromosome or scaffold for common replicate 1 peaks. The chromEnd base is not included in the display of the feature. 15. rep1_signalValue float Signal measure from replicate 1. Note that this is determined by the --rank option. e.g. if --rank is set to signal.value, this corresponds to the 7th column of the narrowPeak, whereas if it is set to p.value it corresponds to the 8th column. 16. rep1_summit int The summit of this peak in replicate 1. [rep 2 data] ... [rep N data] ### Plot output Upper Left: Replicate 1 peak ranks versus replicate 2 peak ranks - peaks that do not pass the specified idr threshold are colered red. Upper Right: Replicate 1 log10 peak scores versus replicate 2 log10 peak scores - peaks that do not pass the specified idr threshold are colered red. Bottom Row: Peaks rank versus idr scores are plotted in black. The overlayed boxplots display the distribution of idr values in each 5% quantile. The idr values are thresholded at the optimization precision - 1e-6 bny default. Contributors ------------ The main contributors of IDR code: * Nathan Boleu - Kundaje Lab, Dept. of Genetics, Stanford University * Anshul Kundaje - Assistant Professor, Dept. of Genetics, Stanford University * Peter J. Bickel - Professor, Dept. of Statistics, University of California at Berkeley Issues ------ If you notice any problem with the code, please file an issue over [here](https://github.com/nboley/idr/issues)