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create mode 100644 htswanalysis/MACS/lib/gsl/gsl-1.11/wavelet/bspline.c create mode 100644 htswanalysis/MACS/lib/gsl/gsl-1.11/wavelet/daubechies.c create mode 100644 htswanalysis/MACS/lib/gsl/gsl-1.11/wavelet/dwt.c create mode 100644 htswanalysis/MACS/lib/gsl/gsl-1.11/wavelet/gsl_wavelet.h create mode 100644 htswanalysis/MACS/lib/gsl/gsl-1.11/wavelet/gsl_wavelet2d.h create mode 100644 htswanalysis/MACS/lib/gsl/gsl-1.11/wavelet/haar.c create mode 100644 htswanalysis/MACS/lib/gsl/gsl-1.11/wavelet/test.c create mode 100644 htswanalysis/MACS/lib/gsl/gsl-1.11/wavelet/wavelet.c create mode 100644 htswanalysis/MACS/setup.py diff --git a/htswanalysis/MACS/00README b/htswanalysis/MACS/00README new file mode 100644 index 0000000..f6e9e07 --- /dev/null +++ b/htswanalysis/MACS/00README @@ -0,0 +1,183 @@ +README for MACS +Time-stamp: <2008-04-28 16:55:52 Tao Liu> + +* Introduction + +With the improvement of sequencing techniques, chromatin +immunoprecipitation followed by high throughput sequencing (ChIP-Seq) +is getting popular to study genome-wide protein-DNA interactions. To +address the lack of powerful ChIP-Seq analysis method, we present a +novel algorithm, named Model-based Analysis of ChIP-Seq (MACS), for +identifying transcript factor binding sites. MACS captures the +influence of genome complexity to evaluate the significance of +enriched ChIP regions, and MACS improves the spatial resolution of +binding sites through combining the information of both sequencing tag +position and orientation. MACS can be easily used for ChIP-Seq data +alone, or with control sample with the increase of specificity. + +* Install + +Please check the file 'INSTALL' in the distribution. + +* Usage + +Usage: macs <-t tfile> [options] + +MACS -- Model-based Analysis for ChIP-Sequencing + +Options: + --version show program's version number and exit + -h, --help show this help message and exit. + -t TFILE, --treatment=TFILE + ChIP-seq treatment files. REQUIRED + -c CFILE, --control=CFILE + control files. + --name=NAME experiment name, which will be used to generate output + file names. DEFAULT: "NA" + --format=FORMAT Format of tag file, "ELAND" or "BED". DEFAULT: "BED" + --gsize=GSIZE genome size, default:2700000000 + --tsize=TSIZE tag size. DEFAULT: 25 + --bw=BW band width. DEFAULT: 300 + --pvalue=PVALUE pvalue cutoff for peak detection. DEFAULT: 1e-5 + --mfold=MFOLD select the regions with MFOLD high-confidence + enrichment ratio against background to build model. + DEFAULT:32 + --verbose=VERBOSE set verbose level. 0: only show critical message, 1: + show additional warning message, 2: show process + information, 3: show debug messages. DEFAULT:2 + +** Parameters: + +*** -t/--treatment FILENAME + +This is the only REQUIRED parameter for MACS. + +ChIP-seq treatment data file can be in either BED format (refer to: +http://genome.ucsc.edu/FAQ/FAQformat#format1) or ELAND output format. + +In an ELAND output file, each line represents one sequence, with fields of: + + 1. Sequence name (derived from file name and line number if format is not Fasta) + 2. Sequence + 3. Type of match: + NM - no match found. + QC - no matching done: QC failure (too many Ns basically). + RM - no matching done: repeat masked (may be seen if repeatFile.txt was specified). + U0 - Best match found was a unique exact match. + U1 - Best match found was a unique 1-error match. + U2 - Best match found was a unique 2-error match. + R0 - Multiple exact matches found. + R1 - Multiple 1-error matches found, no exact matches. + R2 - Multiple 2-error matches found, no exact or 1-error matches. + 4. Number of exact matches found. + 5. Number of 1-error matches found. + 6. Number of 2-error matches found. + Rest of fields are only seen if a unique best match was found (i.e. the match code in field 3 begins with "U"). + 7. Genome file in which match was found. + 8. Position of match (bases in file are numbered starting at 1). + 9. Direction of match (F=forward strand, R=reverse). + 10. How N characters in read were interpreted: ("."=not applicable, "D"=deletion, "I"=insertion). + Rest of fields are only seen in the case of a unique inexact match (i.e. the match code was U1 or U2). + 11. Position and type of first substitution error (e.g. 12A: base 12 was A, not whatever is was in read). + 12. Position and type of first substitution error, as above. + +Notes: + +1) For BED format, the 6th column of strand information is required by +MACS. And please pay attention that the coordinates in BED format is +zero-based and half-open +(http://genome.ucsc.edu/FAQ/FAQtracks#tracks1). + +2) For ELAND format, only matches with match type U0, U1 or U2 is +accepted by MACS, i.e. only the unique match for a sequence with less +than 3 errors is involed in calculation. + +3) For the experiment with several replicates, it is recommended to +concatenate several ChIP-seq treatment files into a single file. To do +this, under Unix/Mac or Cygwin (for windows OS), type: + +$ cat replicate1.bed replicate2.bed replicate3.bed > all_replicates.bed + +*** -c/--control + +The control or mock data file in either BED format or ELAND output +format. Please follow the same direction as for -t/--treatment. + +*** --name + +The name string of the experiment. MACS will use this string NAME to +create output files like 'NAME_peaks.xls', 'NAME_negative_peaks.xls', +'NAME_peaks.bed' and 'NAME_model.r'. So please avoid any confliction +between these filenames and your existing files. + +*** --gsize + +PLEASE assign this parameter to fit your needs! + +It's the mappable genome size or effective genome size which is +defined as the genome size which can be sequenced. Because of the +repetitive features on the chromsomes, the actual mappable genome size +will be smaller than the original size. The default 2.7Gb is +recommended for UCSC human hg18 assembly. + +*** --tsize + +The size of sequencing tags. + +*** --bw + +The band width which is used to scan the genome for model +building. You can set this parameter as the ChIP DNA fragment size +expected from wet experiment. + +*** --pvalue + +The pvalue cutoff. + +*** --mfold + +This parameter is used to select the regions with MFOLD fold tag +enrichment against background to build model. Default is 32. Higher +the MFOLD, less the number of candidate regions. If you see an ERROR or +CRITICAL message from MACS, it is recommended to lower this parameter. + +*** --verbose + +If you don't want to see any message during the running of MACS, set +it to 0. But the CRITICAL messages will never be hidden. If you want +to see rich information like how many peaks are called for every +chromosome, you can set it to 3 or larger than 3. + +* Output files + + 1. NAME_peaks.xls is a tabular file which contains information about +called peaks. You can open it in excel and sort/filter using excel +functions. Information include: chromosome name, start position of +peak, end position of peak, length of peak region, peak summit +position related to the start position of peak region, number of tags +in peak region, -10*log10(pvalue) for the peak region (e.g. pvalue +=1e-10, then this value should be 100), fold change for this region +against random Poisson distribution with local lambda, FDR in +percentage. Coordinates in XLS is 1-based which is different with BED +format. + + 2. NAME_peaks.bed is BED format file which contains the peak +locations. You can load it to UCSC genome browser or Affymetrix IGB +software. + + 3. NAME_negative_peaks.xls is a tabular file which contains +information about negative peaks. Negative peaks are called by +swapping the ChIP-seq and control channel. + + 4. NAME_model.r is an R script which you can use to produce a PDF +image about the model based on your data. Load it to R by: + +$ R --vanilla < NAME_model.r + +Then a pdf file NAME_model.pdf will be generated in your current +directory. Note, R is required to draw this figure. + +* FAQs + +NA now. + diff --git a/htswanalysis/MACS/COPYING b/htswanalysis/MACS/COPYING new file mode 100644 index 0000000..5f22124 --- /dev/null +++ b/htswanalysis/MACS/COPYING @@ -0,0 +1,131 @@ + + + + + The "Artistic License" + + Preamble + +The intent of this document is to state the conditions under which a +Package may be copied, such that the Copyright Holder maintains some +semblance of artistic control over the development of the package, +while giving the users of the package the right to use and distribute +the Package in a more-or-less customary fashion, plus the right to make +reasonable modifications. + +Definitions: + + "Package" refers to the collection of files distributed by the + Copyright Holder, and derivatives of that collection of files + created through textual modification. + + "Standard Version" refers to such a Package if it has not been + modified, or has been modified in accordance with the wishes + of the Copyright Holder as specified below. + + "Copyright Holder" is whoever is named in the copyright or + copyrights for the package. + + "You" is you, if you're thinking about copying or distributing + this Package. + + "Reasonable copying fee" is whatever you can justify on the + basis of media cost, duplication charges, time of people involved, + and so on. (You will not be required to justify it to the + Copyright Holder, but only to the computing community at large + as a market that must bear the fee.) + + "Freely Available" means that no fee is charged for the item + itself, though there may be fees involved in handling the item. + It also means that recipients of the item may redistribute it + under the same conditions they received it. + +1. You may make and give away verbatim copies of the source form of the +Standard Version of this Package without restriction, provided that you +duplicate all of the original copyright notices and associated disclaimers. + +2. You may apply bug fixes, portability fixes and other modifications +derived from the Public Domain or from the Copyright Holder. A Package +modified in such a way shall still be considered the Standard Version. + +3. You may otherwise modify your copy of this Package in any way, provided +that you insert a prominent notice in each changed file stating how and +when you changed that file, and provided that you do at least ONE of the +following: + + a) place your modifications in the Public Domain or otherwise make them + Freely Available, such as by posting said modifications to Usenet or + an equivalent medium, or placing the modifications on a major archive + site such as uunet.uu.net, or by allowing the Copyright Holder to include + your modifications in the Standard Version of the Package. + + b) use the modified Package only within your corporation or organization. + + c) rename any non-standard executables so the names do not conflict + with standard executables, which must also be provided, and provide + a separate manual page for each non-standard executable that clearly + documents how it differs from the Standard Version. + + d) make other distribution arrangements with the Copyright Holder. + +4. You may distribute the programs of this Package in object code or +executable form, provided that you do at least ONE of the following: + + a) distribute a Standard Version of the executables and library files, + together with instructions (in the manual page or equivalent) on where + to get the Standard Version. + + b) accompany the distribution with the machine-readable source of + the Package with your modifications. + + c) give non-standard executables non-standard names, and clearly + document the differences in manual pages (or equivalent), together + with instructions on where to get the Standard Version. + + d) make other distribution arrangements with the Copyright Holder. + +5. You may charge a reasonable copying fee for any distribution of this +Package. You may charge any fee you choose for support of this +Package. You may not charge a fee for this Package itself. However, +you may distribute this Package in aggregate with other (possibly +commercial) programs as part of a larger (possibly commercial) software +distribution provided that you do not advertise this Package as a +product of your own. You may embed this Package's interpreter within +an executable of yours (by linking); this shall be construed as a mere +form of aggregation, provided that the complete Standard Version of the +interpreter is so embedded. + +6. The scripts and library files supplied as input to or produced as +output from the programs of this Package do not automatically fall +under the copyright of this Package, but belong to whoever generated +them, and may be sold commercially, and may be aggregated with this +Package. If such scripts or library files are aggregated with this +Package via the so-called "undump" or "unexec" methods of producing a +binary executable image, then distribution of such an image shall +neither be construed as a distribution of this Package nor shall it +fall under the restrictions of Paragraphs 3 and 4, provided that you do +not represent such an executable image as a Standard Version of this +Package. + +7. C subroutines (or comparably compiled subroutines in other +languages) supplied by you and linked into this Package in order to +emulate subroutines and variables of the language defined by this +Package shall not be considered part of this Package, but are the +equivalent of input as in Paragraph 6, provided these subroutines do +not change the language in any way that would cause it to fail the +regression tests for the language. + +8. Aggregation of this Package with a commercial distribution is always +permitted provided that the use of this Package is embedded; that is, +when no overt attempt is made to make this Package's interfaces visible +to the end user of the commercial distribution. Such use shall not be +construed as a distribution of this Package. + +9. The name of the Copyright Holder may not be used to endorse or promote +products derived from this software without specific prior written permission. + +10. THIS PACKAGE IS PROVIDED "AS IS" AND WITHOUT ANY EXPRESS OR +IMPLIED WARRANTIES, INCLUDING, WITHOUT LIMITATION, THE IMPLIED +WARRANTIES OF MERCHANTIBILITY AND FITNESS FOR A PARTICULAR PURPOSE. + + The End diff --git a/htswanalysis/MACS/ChangeLog b/htswanalysis/MACS/ChangeLog new file mode 100644 index 0000000..894849a --- /dev/null +++ b/htswanalysis/MACS/ChangeLog @@ -0,0 +1,11 @@ +2008-05-28 Tao Liu + Version 1.1 + + * TabIO, PeakModel.py ... + Bug fixed to let MACS tolerate some cases while there is no tag on + either plus strand or minus strand. + + * setup.py + Check the version of python. If the version is lower than 2.4, + refuse to install with warning. + diff --git a/htswanalysis/MACS/INSTALL b/htswanalysis/MACS/INSTALL new file mode 100644 index 0000000..084ba8b --- /dev/null +++ b/htswanalysis/MACS/INSTALL @@ -0,0 +1,84 @@ +INSTALL Guide For MACS +Time-stamp: <2008-05-07 23:57:21 Tao Liu> + +Please check the following instructions to complete your installation. + +* Prerequisite + +Python version must be greater than or equal to 2.4 to run MACS. We +recommend using the version 2.5. + +* Install from source + +MACS uses Python's distutils tools for source installations. To +install a source distribution of MACS, unpack the distribution tarball +and open up a command terminal. Go to the directory where you unpacked +MACS, and simply run the install script : + +$ python setup.py install + +By default, the script will install python library and executable +codes globally, which means you need to be root or administrator of +the machine so as to complete the installation. Please contact the +administrator of that machine if you want their help. If you need to +provide a nonstandard install prefix, or any other nonstandard +options, you can provide many command line options to the install +script. Use the –help option to see a brief list of available options: + +$ python setup.py --help + +For example, if I want to install everything under my own HOME +directory, use this command: + +$ python setup.py install --prefix /home/taoliu/ + +* Configure enviroment variables + +After running the setup script, you might need to add the install +location to your PYTHONPATH and PATH environment variables. The +process for doing this varies on each platform, but the general +concept is the same across platforms. + +** PYTHONPATH + +To set up your PYTHONPATH environment variable, you'll need to add the +value PREFIX/lib/pythonX.Y/site-packages to your existing +PYTHONPATH. In this value, X.Y stands for the major–minor version of +Python you are using (such as 2.4 or 2.5 ; you can find this with +sys.version[:3] from a Python command line). PREFIX is the install +prefix where you installed MACS. If you did not specify a prefix on +the command line, MACS will be installed using Python's sys.prefix +value. + +On Linux, using bash, I include the new value in my PYTHONPATH by +adding this line to my ~/.bashrc : + +$ export PYTHONPATH=/home/taoliu/lib/python2.5/site-packages:$PYTHONPATH + +Using Windows, you need to open up the system properties dialog, and +locate the tab labeled Environment. Add your value to the PYTHONPATH +variable, or create a new PYTHONPATH variable if there isn't one +already. + +** PATH + +Just like your PYTHONPATH, you'll also need to add a new value to your +PATH environment variable so that you can use the MACS command line +directly. Unlike the PYTHONPATH value, however, this time you'll need +to add PREFIX/bin to your PATH environment variable. The process for +updating this is the same as described above for the PYTHONPATH +variable. + +$ export PATH=/home/taoliu/bin:$PATH + +* About GSL package + +We include GSL -- GNU Scientific Library (GPLv3 licensed), version +1.11, in our distribution. MACS uses its CDF functions in some +calculations. The first time you build/install MACS, it will take you +a long time to compile the GSL modules, so please be patient. + + +-- +Tao Liu + diff --git a/htswanalysis/MACS/lib/PeakDetect.py b/htswanalysis/MACS/lib/PeakDetect.py new file mode 100644 index 0000000..6bf5a20 --- /dev/null +++ b/htswanalysis/MACS/lib/PeakDetect.py @@ -0,0 +1,438 @@ +# Time-stamp: <2008-05-28 13:38:06 Tao Liu> + +"""Module Description + +Copyright (c) 2008 Yong Zhang, Tao Liu + +This code is free software; you can redistribute it and/or modify it +under the terms of the Artistic License (see the file COPYING included +with the distribution). + +@status: experimental +@version: $Revision$ +@author: Yong Zhang, Tao Liu +@contact: taoliu@jimmy.harvard.edu +""" + +import logging +import math +from MACS.gsl.cdf import poisson_cdf_Q + +SAVE_WIGGLE = False + +class PeakDetect: + """Class to do the peak calling. + + e.g: + >>> from MACS.PeakDetect import PeakDetect + >>> pd = PeakDetect(treat=treatdata, control=controldata, pvalue=pvalue_cutoff, frag_length=100, scan_window=200, gsize=3000000000) + >>> pd.call_peaks() + >>> print pd.toxls() + """ + def __init__ (self,treat=None, control=None, pvalue=None, frag_length=None, scan_window=None, gsize = None): + """Initialize the PeakDetect object. + + Parameters: + 1. treat : treatment data which is a FWTrackI instance + 2. control : control data which is a FWTrackI instance + 3. pvalue : pvalue cutoff, the pvalue is actually -10*log(10) real_pvalue + 4. frag_length : fragment length predicted by PeakModel process + 5. scan_window : scan window width predicted by PeakModel process + 6. gsize : effective genome size + """ + self.treat = treat + self.control = control + self.ratio_treat2control = None + self.pvalue = pvalue + self.frag_length = frag_length + self.shift_size = frag_length/2 + self.scan_window = scan_window + self.gsize = gsize + self.gap = self.shift_size + self.lambda_bg = float(self.scan_window)*self.treat.total/self.gsize + logging.debug("#3 background lambda: %.2f " % (self.lambda_bg)) + self.min_tags = self.inverse_poisson_cdf(self.lambda_bg,self.pvalue)+1 + logging.debug("#3 min tags: %d" % (self.min_tags)) + self.peaks = None + self.final_peaks = None + self.final_negative_peaks = None + + def inverse_poisson_cdf (self, lam, q ): + """Very Naive inverse CDF (PMF) for poisson distribution. + + The maximum return value is 1000, if it is over 1000, return + None. + """ + p = 10**(q/10*-1) + for i in range(1,1000): + if poisson_cdf_Q(lam,i) < p: + return i + return None + + def call_peaks (self): + """Call peaks function. + + Scan the whole genome for peaks. RESULTS WILL BE SAVED IN + self.final_peaks and self.final_negative_peaks. + """ + if self.control: + self.peaks = self.__call_peaks_w_control () + else: + self.peaks = self.__call_peaks_wo_control () + + def toxls (self): + """ + """ + text = "" + if self.control and self.peaks: + text += "\t".join(("chr","start", "end", "length", "summit", "tags", "-10*log10(pvalue)", "fold_change", "FDR(%)"))+"\n" + elif self.peaks: + text += "\t".join(("chr","start", "end", "length", "summit", "tags", "-10*log10(pvalue)", "fold_change"))+"\n" + else: + return None + + chrs = self.peaks.keys() + chrs.sort() + for chrom in chrs: + for peak in self.peaks[chrom]: + text += "%s\t%d\t%d\t%d" % (chrom,peak[0]+1,peak[1],peak[2]) + peak_summit_relative_pos = peak[3]-peak[0]+1 + text += "\t%d" % (peak_summit_relative_pos) + text += "\t%d\t%.2f" % (peak[5],peak[6]) + text += "\t%.2f" % (peak[7]) + if self.control: + text += "\t%.2f" % (peak[8]) + text+= "\n" + return text + + def neg_toxls (self): + text = "" + text += "\t".join(("chr","start", "end", "length", "summit", "tags", "-10*log10(pvalue)","fold_change"))+"\n" + chrs = self.final_negative_peaks.keys() + chrs.sort() + for chrom in chrs: + for peak in self.final_negative_peaks[chrom]: + text += "%s\t%d\t%d\t%d" % (chrom,peak[0]+1,peak[1],peak[2]) + peak_summit_relative_pos = peak[3]-peak[0]+1 + text += "\t%d" % (peak_summit_relative_pos) + text += "\t%d\t%.2f" % (peak[5],peak[6]) + text += "\t%.2f" % (peak[7]) + text+= "\n" + return text + + def tobed (self): + text = "" + chrs = self.peaks.keys() + chrs.sort() + for chrom in chrs: + for peak in self.peaks[chrom]: + text+= "%s\t%d\t%d\n" % (chrom,peak[0],peak[1]) + return text + + def __add_fdr (self, final, negative): + """ + A peak info type is a: dictionary + + key value: chromosome + + items: (peak start,peak end, peak length, peak summit, peak + height, number of tags in peak region, peak pvalue, peak + foldchange, fdr) <-- tuple type + """ + pvalue2fdr = {} + pvalues_final = [] + pvalues_negative = [] + chrs = final.keys() + for chrom in chrs: + for i in final[chrom]: + pvalues_final.append(i[6]) # i[6] is pvalue in peak info + pvalue2fdr[i[6]]=None + chrs = negative.keys() + for chrom in chrs: + for i in negative[chrom]: + pvalues_negative.append(i[6]) + pvalues_final.sort(reverse=True) + pvalues_negative.sort(reverse=True) + for p in pvalue2fdr.keys(): + n_final = pvalues_final.index(p) + pvalues_final.count(p) + n_negative = 0 + for i in range(len(pvalues_negative)): + if pvalues_negative[i] >= p: + n_negative += 1 + else: + break + pvalue2fdr[p] = 100.0 * n_negative / n_final + #print pvalue2fdr + new_info = {} + chrs = final.keys() + for chrom in chrs: + new_info[chrom] = [] + for i in final[chrom]: + tmp = list(i) + tmp.append(pvalue2fdr[i[6]]) + new_info[chrom].append(tuple(tmp)) # i[6] is pvalue in peak info + return new_info + + def __call_peaks_w_control (self): + """To call peaks with control data. + + A peak info type is a: dictionary + + key value: chromosome + + items: (peak start,peak end, peak length, peak summit, peak + height, number of tags in peak region, peak pvalue, peak + foldchange) <-- tuple type + """ + self.ratio_treat2control = float(self.treat.total)/self.control.total + logging.info("#3.1 shift treatment data") + self.__shift_trackI(self.treat) + logging.info("#3.1 merge +/- strand of treatment data") + self.treat.merge_plus_minus_ranges_w_duplicates () + logging.debug("#3.1 after shift and merging, tags: %d" % (self.treat.total)) + logging.info("#3.1 call peak candidates") + peak_candidates = self.__call_peaks_from_trackI (self.treat) + + logging.info("#3.2 shift control data") + logging.info("#3.2 merge +/- strand of control data") + self.__shift_trackI(self.control) + self.control.merge_plus_minus_ranges_w_duplicates () + logging.debug("#3.2 after shift and merging, tags: %d" % (self.control.total)) + logging.info("#3.2 call negative peak candidates") + negative_peak_candidates = self.__call_peaks_from_trackI (self.control) + + + logging.info("#3.3 use control data to filter peak candidates...") + self.final_peaks = self.__filter_w_control(peak_candidates,self.control, self.ratio_treat2control) + logging.info("#3.3 find negative peaks by reversing treat and control") + self.final_negative_peaks = self.__filter_w_control(negative_peak_candidates,self.treat, 1.0/self.ratio_treat2control) + return self.__add_fdr (self.final_peaks, self.final_negative_peaks) + + def __cal_wig_for_peaks (self): + loose_peak_regions = self.__call_peaks_from_trackI (self.treat,pvalue=1e-3) + + + + def __call_peaks_wo_control (self): + """To call peaks w/o control data. + + """ + logging.info("#3 shift treatment data") + self.__shift_trackI(self.treat) + logging.info("#3 merge +/- strand of treatment data") + self.treat.merge_plus_minus_ranges_w_duplicates () + logging.debug("#3 after shift and merging, tags: %d" % (self.treat.total)) + logging.info("#3 call peak candidates") + peak_candidates = self.__call_peaks_from_trackI (self.treat) + logging.info("#3 use self to calculate local lambda and filter peak candidates...") + self.final_peaks = self.__filter_w_control(peak_candidates,self.treat,1,pass_1k=True) + return self.final_peaks + + def __print_peak_info (self, peak_info): + """Print out peak information. + + A peak info type is a: dictionary + + key value: chromosome + + items: (peak start,peak end, peak length, peak summit, peak + height, number of tags in peak region, peak pvalue, peak + foldchange) <-- tuple type + """ + chrs = peak_info.keys() + chrs.sort() + for chrom in chrs: + peak_list = peak_info[chrom] + for peak in peak_list: + print chrom+"\t"+"\t".join(map(str,peak)) + + def __filter_w_control (self, peak_info, control, treat2control_ratio, pass_1k=False, write2wig= False ): + """Use control data to calculate several lambda values around + 1k, 5k and 10k region around peak summit. Choose the highest + one as local lambda, then calculate p-value in poisson + distribution. + + Return value type in this format: + a dictionary + key value : chromosome + items : array of (peak_start,peak_end,peak_length,peak_summit,peak_height,peak_num_tags,peak_pvalue,peak_foldchange) + """ + final_peak_info = {} + chrs = peak_info.keys() + chrs.sort() + total = 0 + for chrom in chrs: + logging.debug("#3 Chromosome %s" % (chrom)) + n_chrom = 0 + final_peak_info[chrom] = [] + peak_list = peak_info[chrom] + (ctags,tmp) = control.get_ranges_by_chr(chrom) + index_ctag = 0 + flag_find_ctag_in_peak = False + prev_index_ctag = 0 + len_ctags =len(ctags) + for i in range(len(peak_list)): + (peak_start,peak_end,peak_length,peak_summit,peak_height,peak_num_tags) = peak_list[i] + left_10k = peak_summit-5000 + left_5k = peak_summit-2500 + left_1k = peak_summit-500 + right_10k = peak_summit+5000 + right_5k = peak_summit+2500 + right_1k = peak_summit+500 + num_10k = 0 + num_5k = 0 + num_1k = 0 + while index_ctag < len_ctags: + if ctags[index_ctag] < left_10k: + # go to next control tag + index_ctag+=1 + elif right_10k < ctags[index_ctag]: + # finalize and go to next peak region + flag_find_ctag_in_peak = False + index_ctag = prev_index_ctag + break + else: + # in -10k ~ 10k region + if not flag_find_ctag_in_peak: + flag_find_ctag_in_peak = True + prev_index_ctag = index_ctag + num_10k +=1 + if left_1k self.pvalue: + n_chrom += 1 + total += 1 + peak_foldchange = float(peak_num_tags)/local_lambda + final_peak_info[chrom].append((peak_start,peak_end,peak_length,peak_summit,peak_height,peak_num_tags,peak_pvalue,peak_foldchange)) +# else: +# logging.debug("lambda rules") + + logging.debug("#3 peaks whose pvalue < cutoff: %d" % (n_chrom)) + logging.info("#3 Finally, %d peaks are called!" % (total)) + return final_peak_info + + def __call_peaks_from_trackI (self, trackI): + """Call peak candidates from trackI data. Using every tag as + step and scan the self.scan_window region around the tag. If + tag number is greater than self.min_tags, then the position is + recorded. + + Return: data in this format. (peak_start,peak_end,peak_length,peak_summit,peak_height,peak_num_tags) + """ + peak_candidates = {} + logging.debug("#3 search peak condidates...") + chrs = trackI.get_chr_names() + chrs.sort() + total = 0 + for chrom in chrs: + logging.debug("#3 Chromosome %s" % (chrom)) + n_chrom = 0 + peak_candidates[chrom] = [] + (tags,tmp) = trackI.get_ranges_by_chr(chrom) + len_t = len(tags) + cpr_tags = [] # Candidate Peak Region tags + cpr_tags.extend(tags[:self.min_tags-1]) + number_cpr_tags = self.min_tags-1 + p = self.min_tags-1 # Next Tag Index + while p < len_t: + if number_cpr_tags >= self.min_tags: + if tags[p] - cpr_tags[-1*self.min_tags+1] <= self.scan_window: + # add next tag, if the new tag is less than self.scan_window away from previous no. self.min_tags tag + cpr_tags.append(tags[p]) + number_cpr_tags += 1 + p+=1 + else: + # candidate peak region is ready, call peak... + (peak_start,peak_end,peak_length,peak_summit,peak_height) = self.__tags_call_peak (cpr_tags) + peak_candidates[chrom].append((peak_start,peak_end,peak_length,peak_summit,peak_height,number_cpr_tags)) + cpr_tags = [tags[p]] # reset + + number_cpr_tags = 1 + total += 1 + n_chrom += 1 + else: + # add next tag, but if the first one in cpr_tags + # is more than self.scan_window away from this new + # tag, remove the first one from the list + if tags[p] - cpr_tags[0] >= self.scan_window: + cpr_tags.pop(0) + number_cpr_tags -= 1 + cpr_tags.append(tags[p]) + number_cpr_tags += 1 + p+=1 + logging.debug("#3 peak candidates: %d" % (n_chrom)) + logging.debug("#3 Total number of candidates: %d" % (total)) + return peak_candidates + + def __tags_call_peak (self, tags ): + """Project tags to a line. Then find the highest point. + + """ + start = tags[0]-self.frag_length/2 + end = tags[-1]+self.frag_length/2+1 + region_length = end - start + line= [0]*region_length + for tag in tags: + tag_projected_start = tag-start-self.frag_length/2 + tag_projected_end = tag-start+self.frag_length/2 + for i in range(tag_projected_start,tag_projected_end): + line[i] += 1 + tops = [] + top_height = 0 + for i in range(len(line)): + if line[i] > top_height: + top_height = line[i] + tops = [i] + elif line[i] == top_height: + tops.append(i) + peak_summit = tops[len(tops)/2]+start + return (start,end,region_length,peak_summit,top_height) + + + def __shift_trackI (self, trackI): + """Shift trackI data to right (for plus strand) or left (for + minus strand). + + """ + chrs = trackI.get_chr_names() + chrs.sort() + for chrom in chrs: + tags = trackI.get_ranges_by_chr(chrom) + # plus + for i in range(len(tags[0])): + tags[0][i]=tags[0][i]+self.shift_size + # minus + for i in range(len(tags[1])): + tags[1][i]=tags[1][i]-self.shift_size + return + diff --git a/htswanalysis/MACS/lib/PeakModel.py b/htswanalysis/MACS/lib/PeakModel.py new file mode 100644 index 0000000..f00fa0e --- /dev/null +++ b/htswanalysis/MACS/lib/PeakModel.py @@ -0,0 +1,266 @@ +# Time-stamp: <2008-05-28 13:14:16 Tao Liu> + +"""Module Description + +Copyright (c) 2008 Yong Zhang, Tao Liu + +This code is free software; you can redistribute it and/or modify it +under the terms of the Artistic License (see the file COPYING included +with the distribution). + +@status: experimental +@version: $Revision$ +@author: Yong Zhang, Tao Liu +@contact: taoliu@jimmy.harvard.edu +""" +import sys, time, random +import logging + +class PeakModel: + """Peak Model class. + """ + def __init__ (self, treatment=None, gz = 0, fold=32, max_pairnum=500, bw=200, ts = 25, bg=0, max_dup_tags=1): + self.treatment = treatment + self.gz = gz + self.fold = fold + self.max_pairnum = max_pairnum + self.bw = bw + self.tsize = ts + self.bg_redundant_rate = bg + self.mr = max_dup_tags + self.summary = "" + self.plus_line = None + self.minus_line = None + self.shifted_line = None + self.frag_length = None + self.scan_window = None + self.min_tags = None + self.peaksize = None + self.build() + + def build (self): + """Build the model. + + prepare self.frag_length, self.scan_window, self.plus_line, + self.minus_line and self.shifted_line to use. + """ + self.peaksize = 2*self.bw + self.min_tags = float(self.treatment.total) * self.fold * self.peaksize / self.gz /2 # mininum hits on single strand + # use treatment data to build model + paired_peakpos = self.__paired_peaks () + # select up to 1000 pairs of peaks to build model + num_paired_peakpos = 0 + num_paired_peakpos_remained = self.max_pairnum + num_paired_peakpos_picked = 0 + for c in paired_peakpos.keys(): + num_paired_peakpos +=len(paired_peakpos[c]) + if num_paired_peakpos_remained == 0: + paired_peakpos.pop(c) + else: + paired_peakpos[c] = paired_peakpos[c][:num_paired_peakpos_remained] + num_paired_peakpos_remained -= len(paired_peakpos[c]) + num_paired_peakpos_picked += len(paired_peakpos[c]) + + logging.info("#2 number of paired peaks: %d" % (num_paired_peakpos)) + if num_paired_peakpos < 100: + logging.critical("Too few paired peaks (%d) so I can not build the model! Lower your MFOLD parameter may erase this error." % (num_paired_peakpos)) + logging.critical("Process is terminated!") + sys.exit(1) + elif num_paired_peakpos < self.max_pairnum: + logging.warn("Fewer paired peaks (%d) than %d! Model may not be build well! Lower your MFOLD parameter may erase this warning. Now I will use %d pairs to build model!" % (num_paired_peakpos,self.max_pairnum,num_paired_peakpos_picked)) + logging.debug("Use %d pairs to build the model." % (num_paired_peakpos_picked)) + self.__paired_peak_model(paired_peakpos) + + def __str__ (self): + """For debug... + + """ + return """ +Summary of Peak Model: + Baseline: %d + Fragment size: %d + Scan window size: %d +""" % (self.min_tags,self.frag_length,self.scan_window) + + def __paired_peak_model (self, paired_peakpos): + """Use paired peak positions and treatment tag positions to build the model. + + Modify self.(frag_length, model_shift size and scan_window size. and extra, plus_line, minus_line and shifted_line for plotting). + """ + window_size = 1+2*self.peaksize + self.plus_line = [0]*window_size + self.minus_line = [0]*window_size + for chrom in paired_peakpos.keys(): + paired_peakpos_chrom = paired_peakpos[chrom] + tags = self.treatment.get_ranges_by_chr(chrom) + tags_plus = tags[0] + tags_minus = tags[1] + # every paired peak has plus line and minus line + # add plus_line + self.plus_line = self.__model_add_line (paired_peakpos_chrom, tags_plus,self.plus_line) + # add minus_line + self.minus_line = self.__model_add_line (paired_peakpos_chrom, tags_minus,self.minus_line) + + # find top + plus_tops = [] + minus_tops = [] + plus_max = max(self.plus_line) + minus_max = max(self.minus_line) + for i in range(window_size): + if self.plus_line[i] == plus_max: + plus_tops.append(i) + if self.minus_line[i] == minus_max: + minus_tops.append(i) + self.frag_length = minus_tops[len(minus_tops)/2] - plus_tops[len(plus_tops)/2] + 1 + shift_size = self.frag_length/2 + self.scan_window = max(self.frag_length,self.tsize)*2 + # a shifted model + self.shifted_line = [0]*window_size + plus_shifted = [0]*shift_size + plus_shifted.extend(self.plus_line[:-1*shift_size]) + minus_shifted = self.minus_line[shift_size:] + minus_shifted.extend([0]*shift_size) + for i in range(window_size): + self.shifted_line[i]=minus_shifted[i]+plus_shifted[i] + return True + + def __model_add_line (self, pos1, pos2, line): + """Project each pos in pos2 which is included in + [pos1-self.peaksize,pos1+self.peaksize] to the line. + + """ + i1 = 0 # index for pos1 + i2 = 0 # index for pos2 + i2_prev = 0 # index for pos2 in previous pos1 + # [pos1-self.peaksize,pos1+self.peaksize] + # region + i1_max = len(pos1) + i2_max = len(pos2) + last_p2 = -1 + flag_find_overlap = False + + while i1 p2: # move pos2 + i2 += 1 + elif p1+self.peaksize < p2: # move pos1 + i1 += 1 + i2 = i2_prev # search minus peaks from previous index + flag_find_overlap = False + else: # overlap! + if not flag_find_overlap: + flag_find_overlap = True + i2_prev = i2 # only the first index is recorded + # project + for i in range(p2-p1+self.peaksize-self.tsize/2,p2-p1+self.peaksize+self.tsize/2): + if i>=0 and i mp: # move minus + im += 1 + elif pp+self.peaksize < mp: # move plus + ip += 1 + im = im_prev # search minus peaks from previous index + flag_find_overlap = False + else: # overlap! + if not flag_find_overlap: + flag_find_overlap = True + im_prev = im # only the first index is recorded + if float(pn)/mn < 2 and float(pn)/mn > 0.5: # number tags in plus and minus peak region are comparable... + pair_centers.append((pp+mp)/2) + im += 1 + return pair_centers + + def __naive_find_peaks (self, taglist, countlist ): + """Naively call peaks based on tags counting. The redundant rate in peak region must be less than 2-fold of background( global) redundant rate + + Return peak positions and the tag number in peak region by a tuple list [(pos,num)]. + """ + peak_info = [] # store peak pos in every peak region and + # unique tag number in every peak region + if len(taglist)<2: + return peak_info + pos = taglist[0] + count = countlist[0] + current_tag_list = [pos] # list to find peak pos + current_redundant_tags = max(count-self.mr,0) + for i in range(1,len(taglist)): + pos = taglist[i] + count = countlist[i] + if (pos-current_tag_list[0]+1) > self.peaksize: # call peak in current_tag_list + # a peak will be called if redundant tags are less + # than 2*redunant rate of background + current_redundant_rate = float(current_redundant_tags)/(current_redundant_tags+len(current_tag_list)) + if current_redundant_rate <= 2*self.bg_redundant_rate: + # a peak will be called if tag number is ge min tags. + if len(current_tag_list) >= self.min_tags: + peak_info.append((self.__naive_peak_pos(current_tag_list),len(current_tag_list))) + current_tag_list = [] # reset current_tag_list + current_redundant_tags = 0 # reset current_redundant_tags number + current_tag_list.append(pos) # add pos while 1. no + # need to call peak; + # 2. current_tag_list is [] + current_redundant_tags += max(count-self.mr,0) + return peak_info + + def __naive_peak_pos (self, pos_list ): + """Naively calculate the position of peak. + + return the highest peak summit position. + """ + peak_length = pos_list[-1]+1-pos_list[0]+self.tsize + start = pos_list[0] -self.tsize/2 + horizon_line = [0]*peak_length # the line for tags to be projected + for pos in pos_list: + for pp in range(pos-start-self.tsize/2,pos-start+self.tsize/2): # projected point + horizon_line[pp] += 1 + + top_pos = [] # to record the top positions. Maybe > 1 + top_p_num = 0 # the maximum number of projected points + for pp in range(peak_length): # find the peak posistion as the highest point + if horizon_line[pp] > top_p_num: + top_p_num = horizon_line[pp] + top_pos = [pp] + elif horizon_line[pp] == top_p_num: + top_pos.append(pp) + return (top_pos[len(top_pos)/2]+start) diff --git a/htswanalysis/MACS/lib/TabIO/__init__.py b/htswanalysis/MACS/lib/TabIO/__init__.py new file mode 100644 index 0000000..ebe8920 --- /dev/null +++ b/htswanalysis/MACS/lib/TabIO/__init__.py @@ -0,0 +1,413 @@ +# Time-stamp: <2008-05-28 13:25:06 Tao Liu> + +"""Module for FWTrackI, BEDParser and ELANDParser classes + +Copyright (c) 2007 Tao Liu + +This code is free software; you can redistribute it and/or modify it +under the terms of the Artistic License (see the file COPYING included +with the distribution). + +@status: experimental +@version: $Revision$ +@author: Tao Liu +@contact: taoliu@jimmy.harvard.edu +""" + +# ------------------------------------ +# python modules +# ------------------------------------ +import re +import sys +import logging +# ------------------------------------ +# constants +# ------------------------------------ +__version__ = "TabIO $Revision$" +__author__ = "Tao Liu " +__doc__ = "FWTrackI, BEDParser and ELANDParser classes" + +# ------------------------------------ +# Misc functions +# ------------------------------------ + +# ------------------------------------ +# Classes +# ------------------------------------ + +class FWTrackI: + """Fixed Width Ranges along the whole genome (commonly with the + same annotation type), which are stored in a dict. + + Ranges are stored and organized by sequence names (chr names) in a + dict. They can be sorted by calling self.sort() function. + + Example: + >>> tabfile = TabFile("tag.bed",format="bed",mode="r") + >>> track = FWTrackI() + >>> for (chromosome,rg) in tabfile: + ... track.add_range(chromosome,rg) + >>> track.get_ranges_by_chr["chr1"] # all ranges in chr1 + """ + def __init__ (self,fw=0,anno=""): + """fw is the fixed-width for all ranges + """ + self.fw = fw + self.__ranges = {} + self.__comments = {} # store the comments if available for each range + self.__well_sorted = False + self.__well_merged = False + self.total = 0 + self.total_w_duplicates = 0 + self.annotation = anno # need to be figured out + + def add_range (self, chromosome, range): + """Add a range to the list according to the sequence name. + + chromosome -- mostly the chromosome name + range -- RangeI objectleverage + """ + if not isinstance(range,RangeI): + raise Exception("Must add a RangeI object!") + if not self.__ranges.has_key(chromosome): + self.__ranges[chromosome] = [[],[]] # for (+strand, -strand) + self.__comments[chromosome] = [[],[]] # for (+,-) + #print "add: "+str(range) + if range.strand == 1: + self.__ranges[chromosome][0].append(range.start) + self.__comments[chromosome][0].append(1) + elif range.strand == -1: + self.__ranges[chromosome][1].append(range.end) + self.__comments[chromosome][1].append(1) + self.total+=1 + self.__well_sorted = False + self.__well_merged = False + + def add_loc (self, chromosome, fiveendpos, strand): + """Add a range to the list according to the sequence name. + + chromosome -- mostly the chromosome name + fiveendpos -- 5' end pos, left for plus strand, neg for neg strand + strand -- 0: plus, 1: minus + """ + if not self.__ranges.has_key(chromosome): + self.__ranges[chromosome] = [[],[]] # for (+strand, -strand) + self.__comments[chromosome] = [[],[]] # for (+,-) + #print "add: "+str(range) + self.__ranges[chromosome][strand].append(fiveendpos) + self.__comments[chromosome][strand].append(1) + self.total+=1 + self.__well_sorted = False + self.__well_merged = False + + def get_ranges_by_chr (self, chromosome): + """Return array of locations by chromosome. + + Not recommanded! Use generate_rangeI_by_chr() instead. + """ + if self.__ranges.has_key(chromosome): + return self.__ranges[chromosome] + else: + raise Exception("No such chromosome name (%s) in TrackI object!\n" % (chromosome)) + + def get_comments_by_chr (self, chromosome): + """Return array of comments by chromosome. + + """ + if self.__comments.has_key(chromosome): + return self.__comments[chromosome] + else: + raise Exception("No such chromosome name (%s) in TrackI object!\n" % (chromosome)) + + + def generate_rangeI_by_chr_strand (self, chromosome,strand): + """A generator to return RangeI objects on given chromosome and given strand. + + strand: 0 for plus, 1 for minus strand + + Recommended function. + """ + if self.__ranges.has_key(chromosome): + for i in self.__ranges[chromosome][strand]: + yield RangeI(start=i,end=i+self.fw,strand=strand) # buggy!! + else: + raise Exception("No such chromosome name (%s) in TrackI object!\n" % (chromosome)) + + def get_chr_names (self): + """Return all the chromosome names stored in this track object. + """ + l = self.__ranges.keys() + l.sort() + return l + + def length (self): + return self.total*self.fw + + def merge_overlap (self): + """merge the SAME ranges. Record the duplicate number in self.__comments{} + +*Note: different with the merge_overlap() in TrackI class, which merges the overlapped ranges. + + """ + if not self.__well_sorted: + self.sort() + self.total = 0 + self.total_w_duplicates = 0 + for k in self.__ranges.keys(): # for each chromosome + #logging.debug(" ... mergeing chr: %s" % (k)) + (plus,minus) = self.__ranges[k] + (plus_c,minus_c) = self.__comments[k] + (new_plus,new_minus,new_plus_c,new_minus_c) = ([],[],[],[]) + # + strand + if len(plus)>0: + new_plus.append(plus[0]) + new_plus_c.append(1) + n = 0 # the position in new list + for p in plus[1:]: + if p == new_plus[n]: + new_plus_c[n]+=1 + else: + new_plus.append(p) + new_plus_c.append(1) + n += 1 + self.total += len(new_plus) + self.total_w_duplicates += sum(new_plus_c) + + # - strand + if len(minus)>0: + new_minus.append(minus[0]) + new_minus_c.append(1) + n = 0 # the position in new list + for p in minus[1:]: + if p == new_minus[n]: + new_minus_c[n]+=1 + else: + new_minus.append(p) + new_minus_c.append(1) + n += 1 + self.total += len(new_minus) + self.total_w_duplicates += sum(new_minus_c) + + self.__ranges[k]=[new_plus,new_minus] + self.__comments[k]=[new_plus_c,new_minus_c] + self.__well_merged = True + + def merge_plus_minus_ranges_w_duplicates (self): + """Merge minus strand ranges to plus strand. Reset the comments. keep duplicates. + + """ + self.total = 0 + self.total_w_duplicates = None + for chrom in self.__ranges.keys(): + (plus_tags,minus_tags) = self.__ranges[chrom] + new_plus_tags = [] + #reset comments + self.__comments[chrom][0] = [] + self.__comments[chrom][1] = [] + ip = 0 + im = 0 + lenp = len(plus_tags) + lenm = len(minus_tags) + while ip < lenp and im < lenm: + if plus_tags[ip] < minus_tags[im]: + new_plus_tags.append(plus_tags[ip]) + ip += 1 + else: + new_plus_tags.append(minus_tags[im]) + im += 1 + if im < lenm: + # add rest of minus tags + new_plus_tags.extend(minus_tags[im:]) + if ip < lenp: + # add rest of plus tags + new_plus_tags.extend(plus_tags[ip:]) + + self.__ranges[chrom] = [new_plus_tags,None] + self.total += len(new_plus_tags) + + def status (self): + """Return the information whether or not the track is sorted and/or merged. + + Return value: tuple (boolean,boolean) means (sorted?,merged?) + """ + return (self.__well_sorted,self.__well_merged) + + def sort (self): + """Sort all ranges in list. Recommanded after you import all ranges. + """ + if self.__well_sorted: + pass + for key in self.__ranges.keys(): + self.__ranges[key][0].sort(lambda x,y: cmp(x,y)) + self.__ranges[key][1].sort(lambda x,y: cmp(x,y)) + self.__well_sorted = True + + + def __str__ (self): + return self.__to_wiggle() + + def __to_wiggle (self): + t = "track type=wiggle_0 name=\"variableStep\" description=\"%s\"\n" % (self.annotation) + for key in self.__ranges.keys(): + t += "variableStep chrom=%s span=%d\n" % (key,self.fw) + for i in self.__ranges[key][0]: + t += str(i)+"\n" + for i in self.__ranges[key][1]: + t += str(i)+"\n" + return t + +class BEDParser: + """File Parser Class for tabular File. + + """ + def __init__ (self): + """All parameters except for format are the same as python's + builtin file class. + + Format parameter can be "bed","gff" or other particular string + for other format of file. For example, if your file is '\\t' + delimited, and the first column of the file is chromosome + name, the second column is start position, third column is end + position, fourth column is the strand, the coordinates are + 0-indexed and the range is closed, then you should write the + format string as "123401\\t" (six numbers and the delimiter). + + Note: Use the fifth and sixth number in format string to + indicate whether the coordinates are 0-indexed (0) or + 1-indexed (1) and whether the range is opened (0) or closed + (1) (i.e. whether the end position is included in the range + or not) + """ + self.__format__ = "123600\t" + self.__delimiter__ = self.__format__[6:] + self.__chr_col__ = int(self.__format__[0])-1 + self.__start_col__ = int(self.__format__[1])-1 + self.__end_col__ = int(self.__format__[2])-1 + self.__strand_col__ = int(self.__format__[3])-1 + self.__start_shift__ = int(self.__format__[4]) + self.__end_shift__ = int(self.__format__[5]) + + def build_fwtrack (self, fhd): + """Build FWTrackI from all lines, return a FWTrackI object. + + Note: All ranges will be merged (exclude the same + range) then sorted after the track is built. + + If both_strand is True, it will store strand information in + FWTrackI object. + + if do_merge is False, it will not merge the same range after + the track is built. + """ + fwtrack = FWTrackI() + i = 0 + m = 0 + for thisline in fhd: + (chromosome,fpos,strand) = self.__fw_parse_line(thisline) + i+=1 + if i == 1000000: + m += 1 + logging.info(" %d" % (m*1000000)) + i=0 + if not fpos or not chromosome: + continue + fwtrack.add_loc(chromosome,fpos,strand) + return fwtrack + + def __fw_parse_line (self, thisline ): + if not thisline: return (None,None,None) + thisline = thisline.rstrip() + if not thisline: return ("blank",None,None) + + if thisline.startswith("#") or thisline.startswith("track"): + return ("comment line",None,None) # comment line is skipped + thisfields = thisline.split("\t") + if len(thisfields) > 5 : + strand = thisfields[5] + else: + strand = "+" # default pos strand if no strand info can be found + if strand == "+": + return (thisfields[0], + int(thisfields[1]), + 0) + elif strand == "-": + return (thisfields[0], + int(thisfields[2]), + 1) + else: + raise self.StrandFormatError(thisline,strand) + + class StrandFormatError(Exception): + def __init__ (self, string, strand): + self.message = "Strand information can not be recognized in this line: \"%s\",\"%s\"" % (string,strand) + + def __str__ (self): + return repr(self.message) + + +class ELANDParser: + """File Parser Class for tabular File. + + """ + def __init__ (self): + """ + """ + pass + + def build_fwtrack (self, fhd): + """Build FWTrackI from all lines, return a FWTrackI object. + + Note: All ranges will be merged (exclude the same + range) then sorted after the track is built. + + If both_strand is True, it will store strand information in + FWTrackI object. + + if do_merge is False, it will not merge the same range after + the track is built. + """ + fwtrack = FWTrackI() + i = 0 + m = 0 + for thisline in fhd: + (chromosome,fpos,strand) = self.__fw_parse_line(thisline) + i+=1 + if i == 1000000: + m += 1 + logging.info(" %d" % (m*1000000)) + i=0 + if not fpos or not chromosome: + continue + fwtrack.add_loc(chromosome,fpos,strand) + return fwtrack + + def __fw_parse_line (self, thisline ): + if not thisline: return (None,None,None) + thisline = thisline.rstrip() + if not thisline: return ("blank",None,None) + + if thisline[0] == "#": return ("comment line",None,None) # comment line is skipped + thisfields = thisline.split("\t") + thistaglength = len(thisfields[1]) + + if thisfields[2] == "U0" or thisfields[2]=="U1" or thisfields[2]=="U2": + strand = thisfields[8] + if strand == "F": + return (thisfields[6], + int(thisfields[7])-1, + 0) + elif strand == "R": + return (thisfields[6], + int(thisfields[7])+thistaglength-2, + 1) + else: + raise self.StrandFormatError(thisline,strand) + else: + return (None,None,None) + + class StrandFormatError(Exception): + def __init__ (self, string, strand): + self.message = "Strand information can not be recognized in this line: \"%s\",\"%s\"" % (string,strand) + + def __str__ (self): + return repr(self.message) diff --git a/htswanalysis/MACS/lib/__init__.py b/htswanalysis/MACS/lib/__init__.py new file mode 100644 index 0000000..f45264a --- /dev/null +++ b/htswanalysis/MACS/lib/__init__.py @@ -0,0 +1 @@ +__all__ = ["TabIO"] diff --git a/htswanalysis/MACS/lib/gsl/__init__.py b/htswanalysis/MACS/lib/gsl/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/htswanalysis/MACS/lib/gsl/cdf.c b/htswanalysis/MACS/lib/gsl/cdf.c new file mode 100644 index 0000000..cbcd8a2 --- /dev/null +++ b/htswanalysis/MACS/lib/gsl/cdf.c @@ -0,0 +1,56 @@ +/* + * Copyright (C) 2008 Tao Liu. + * + * This code is free software; you can redistribute it and/or modify + * it under the terms of the Artistic License (see the file COPYING + * included with the distribution). + * + * + */ + +#include + +#include +/* include GSL cdf function for poisson */ +#include "gsl_cdf.h" + +static PyObject * +poisson_cdf_Q ( PyObject * self, PyObject * args) +{ + double lambda; + int k; + double pvalue; + + if (! PyArg_ParseTuple(args, "di", &lambda,&k)) + return NULL; + pvalue = gsl_cdf_poisson_Q (k,lambda); + return Py_BuildValue("d",pvalue); +} + + +static PyObject * +binom_cdf_Q ( PyObject * self, PyObject * args) +{ + double p; + int n; + int k; + double pvalue; + + if (! PyArg_ParseTuple(args, "dii", &p,&n,&k)) + return NULL; + pvalue = gsl_cdf_binomial_Q(k,p,n); + return Py_BuildValue("d",pvalue); +} + +static PyMethodDef pcdfMethods[] = + { + {"poisson_cdf_Q",poisson_cdf_Q,METH_VARARGS,"Calculate CDF for a poisson distribution ( for upper tail). Usage: poisson_cdf_Q(lambda,k); Return: P-value"}, + {"binom_cdf_Q",binom_cdf_Q,METH_VARARGS,"Calculate CDF for a binomial distribution ( for upper tail). Usage: binom_cdf_Q(p,n,k); Return: P-value"}, + {NULL,NULL} + }; + +void initcdf() +{ + PyObject * m; + m = Py_InitModule("cdf",pcdfMethods); +} diff --git a/htswanalysis/MACS/lib/gsl/gsl-1.11/.libs/gsl-histogram b/htswanalysis/MACS/lib/gsl/gsl-1.11/.libs/gsl-histogram new file mode 100755 index 0000000000000000000000000000000000000000..f29d1ab5653a75ffe496a6743f90bb2fe74cb8ff GIT binary patch literal 13156 zcmeHOU1%It6u#SzcG0xEt+D<$^RNY>cDIoVQWTmt+cuacrGHkN^fH;9*`0QFW}Q3R zCJ$m@EV3+1eH5Sao$2l-QS`}&-b2pZ z@1DPV=9>-dz}e4#`~J^0jIq_wRnT?NNrkbqSQF4Ep`0HW*qeVoe{d9=Skf_AzEa*S zl=H*+V~0EZloRJ+rE~9q(zzs@`^Kc-!mWLG{>&qcy@}#8YZ*g}ZBf{Cyy09IzGpeb z$gs4B;XcORhHp64K9@q{9tgMl9~?M5K$b{UtnocW{l~mf1m^|K*9f(Q^X2)y|JLVjcxdFA z{NPZ02Rq@Q{!ya<;;DCvjfL{>rw#m@u)NmK*k=g18~6a(A%saOcLTHs`gc-7{z`uO zAE9SJzFbv!Ioq1ZMUU+v*rB+bCK(!pd-amq06RB5z zLaq89rKV(>3W{GHzgXXsq}|W!84DJbtK*kbFP}%y%~U#n`DSV?fAMDOu~~{}A|kS` zAI$U+gbz@ueN6tZ;IF@b4cA6~zLuYB1Xu9Iw3eU!PBl|~gN&v6rcpSoF<;xBY2`L& 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