Package polyphemus. February 15, 2013

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1 Package polyphemus February 15, 2013 Type Package Title Genome wide analysis of RNA Polymerase II-based ChIP Seq data. Version Date Author Martial Sankar, supervised by Marco Mendoza and Hinrich Gronenmeyer Maintainer Martial Sankar Polyphemus is able to combine signal intensity (count) profiles, with significant peak annotations in order to identify coding regions of interest for the comparative analysis or two or more samples. The algorithm (i) identifies significant coding regions, (ii) performs normalization, (iii) compares several profiles and (iv) provides matrix output suitable for further clustering analysis. License GPL (>= 2) LazyLoad yes Depends R (>= ) OS_type unix Imports IRanges, zoo, RUnit, parallel, R.utils, limma URL Repository CRAN Date/Publication :21:15 NeedsCompilation no 1

2 2 polyphemus-package R topics documented: polyphemus-package createfolderhierarchy generatewig getcodreg getincrbyg getint getlowessfitval getmvaplot getnorm getoutput getresult getswv gwcomp lowessnorm medichimerge quantilenorm stretchgene Index 18 polyphemus-package R package for comparative analysis of genome-wide RNA Polymerase II ChIP-seq data using non-linear normalisation approaches. The polyphemus package was designed to extract comparative information about PolII action at different gene loci from ChIP-seq profiling. This permits to (i) integrates TSS annotation with PolII enrichment over coding regions, (ii) normalizes signal intensity profiles using non-linear approaches to correct for technical/experimental variations and iii) generates outputs for gene classification according to differential transcription characteristics. Package: polyphemus Type: Package Version: Date: License: GPL (>=2) LazyLoad: yes

3 createfolderhierarchy 3 Martial Sankar directed by Marco Mendoza and Hinrich Gronemeyer Maintainer: Martial Sankar <martial.sankar@unil.ch> References Reiss, D. J., M. T. Facciotti, et al. (2008). "Model-based deconvolution of genome-wide DNA binding." Bioinformatics 24(3): Rozowsky, J., G. Euskirchen, et al. (2009). "PeakSeq enables systematic scoring of ChIP-seq experiments relative to controls." Nat Biotechnol 27(1): Taslim, C., J. Wu, et al. (2009). "Comparative study on ChIP-seq data: normalization and binding pattern characterization." Bioinformatics 25(18): Examples fpath1<-system.file("extdata", package="polyphemus") fpath2<-file.path(fpath1,"atra") ex <- gwcomp(initdir=fpath2, posprocparam=c(300, 0.1,NA), denomtrack="etoh", database=file.path(fpath1,"mouserefseq-genenames.txt"), normmethod="quantile", stretched =50, TSSLength = 10, window=1000, span=500,prepost=c(0,0), outinttable=true, plotmva=true) createfolderhierarchy Create hierarchy of folder. createfolderhierarchy create a hierarchy of folders. The functions takes in input a folder of pooled files (bed and wig format). One folder is then created for each chromosome. the folder name corresponds to the name of the chromosome (i.e: "chr1", "chrx"... ). The subfolders "pos" and "wig" are then created. They contain respectively the bed and wig files. createfolderhierarchy(initialdir) initialdir character the path of the folder containing the pooled files. If the folders and subfolders are already created nothing is done. Martial Sankar

4 4 generatewig Examples fpath<-system.file("extdata", package="polyphemus") createfolderhierarchy(file.path(fpath,"atra")) generatewig Generate Wig files generatewig Generate Wig files from a count files [deprecated] generatewig(countdf, col, chr, dbori, window, span, prespost = c(1000, 1000), out) countdf col chr dbori window span prespost out data.frame, two columns dataset (Start, End position) numeric, column index of the count column numeric, chromosome index data.frame, intial database (refseq) numeric, size of the sliding window numeric, span of the sliding window vector, the number of pb before and after the gene, c(int, int) character, output path and file name of the generated wwig file deprecated function.

5 getcodreg 5 getcodreg extract intensity (count) values from the wig file. getcodreg permits to define coding regions and extract intensity (count) values from the wig file. getcodreg(wiggfile, prepost, window, dbcurr, chrcurr, span = NULL) wiggfile prepost window dbcurr chrcurr span character, path and filename to a wig file. vector, contains two values, the number of bp (base pairs) before and the number of bp after the coding region. numeric, size of the sliding window (in bp). data.frame, corresponding to a subset of the database file for the processing chromosome. character, name of the processing chromosome. numeric, if NULL take the span of the wig file else take the specified span. Value returns a list. each element corresponds to the vector of intensities for the specific gene. Martial Sankar getincrbyg sub-function of generatewig getincrbyg, sub-function of generatewig. getincrbyg(gcurr, col, countdf, dbori, window, span, prespost)

6 6 getint gcurr col countdf dbori window span prespost String, gene name. numeric, column index. data.frame, two columns dataset (Start, End position). data.frame, intial database (refseq). numeric, size of the sliding window. numeric, span of the sliding window. vector, the number of pb before and after the gene, c(int, int). generatewig getint Extract intensities and start the comparative analysis. getint permits to extract intensities from files in one chromosome folder and start the comparative analysis. getint(folderpath, medichiproc = c(null, NULL, NULL), prepost = c(500, 2000), window = 250, denomtra folderpath medichiproc prepost window denomtrack databasefile puttss span character, path to the wig folder. vector, c(window, pvalue filters, String path and name for output (NULL default)). see the details section. vector, contains two values, the number of bp (base pair) before and the number of bp after the coding region. numeric, size of the sliding window (in bp). character, the denominator track name (string contained in the filename). character, path and name of the database file. logical, if TRUE, detect the putative TSS inside the coding region. (not yet implemented). numeric, if NULL take the span of the wig file else take the specified span.

7 getlowessfitval 7 Value The denomtrack argument is the exact String of the denominator track contained in the filename. Its intensity values will be used as steady denominator when calculating the ratio between tracks. The medichiproc argument is a vector that contains three elements. The first is the window size where the TSS is centered. The second is a filter on the pvalue. The third is the file path and name of the output table. if NULL, the output file is not provided. The getint function is also called by the wrapper gwcomp. The getint function returns an object (list) of, at least, four elements. It can be more depending on the number of tracks in the experiments. gene chr denominator numerator character, the gene annotation. character, the chromosome name. numeric, intensities value for the denominator track. numeric, intensities value for the first numerator track.... numeric, intensities value for the other track. Martial Sankar See also gwcomp for genome wide analysis (in all chromosomes). See also getcodreg. Examples fpath1<-system.file("extdata", package="polyphemus") fpath2<-file.path(file.path(fpath1,"atra"),"chr19") x <- getint(folderpath=fpath2, # path of the parent folder of the wig directory for the chr19 medichiproc = c(300, 0.1, NULL), prepost = c(0, 0), window = 1000, denomtrack="etoh", puttss = FALSE, databasefile=file.path(fpath1,"mouserefseq-genenames.txt"), span = 500) getlowessfitval get fitted values from a lowess regression. getlowessfitval, return a list of table where it was extracted the fit values from a lowess regression, getlowessfitval(id, list, toplot = FALSE)

8 8 getmvaplot id list toplot character, geneid from the final output of polyphemus character, splitted polyphemus output (by ID) Boolean, whether the the fitted values should be added to the mva plot subfunction of getoutput See also getoutput. getmvaplot Display the MVA plot getmvaplot, call by lowessnorm & quantilenorm function, display the mva plot getmvaplot(matraw, matnorm, normmeth = NULL) matraw matnorm normmeth matrix, initial matrix, contains the raw intensity matrix, normalized matrix character, normalization methods subfunction of quantilenorm and lowessnorm. See also quantilenorm and lowessnorm.

9 getnorm 9 getnorm Normalization The getnorm permits the normalization of the intensity (count) values. getnorm(objint, norm = c("linear", "lowess", "quantile"), lowessspan = NULL, mva = NULL) objint norm lowessspan mva list, the intensity object. Output of the getint function. character, the normalization method : "linear" for linear normalization, "lowess" and "quantile" for local normalization. See details section. numeric, the smoother span. This gives the proportion of points in the plot which influence the smooth at each value. Larger values give more smoothness but requires more computing time. if NULL, lowess span = 2/3. logical, if TRUE, the MVA figures are plotted. Polyphemus provides three normalization procedure. Choosing a "linear" normalization, a correction factor is applied such that the sum of the intensities for a track is the same for all the tracks. Choosing a local normalisation, "lowess" or "quantile", a correction factor is locally calculating and applied to the track intensities. This turns to be more reliable than classic "linear" normalisation. Taslim et al. already mentionned it to normalize PolII ChIP-seq data. However when dealing with more than two samples, the "quatile" procedure turns to be more suitable as it requires less computing time. The "lowess" normalization used the lowess implementation of the R base package. The "quantile" procedure used the normalizequantiles of the limma package. getnorm is called in the polyphemus wrapper gwcomp. Value returns an object (list) containing three elements. gene chr matrix character, the gene annotation. chr, the chromosome name. matrix,the matrix of normalized intensity values. Martial Sankar

10 10 getoutput References Taslim, C., J. Wu, et al. (2009). "Comparative study on ChIP-seq data: normalization and binding pattern characterization." Bioinformatics 25(18): gwcomp. getoutput get cleaner output from POLYPHEMUS results data postprocessing function, return a list of table with 3 columns (ID, ratio, approx), used by default in getresult function. getoutput(inputtable = NULL, filename = NULL, norm, stretch, tssl, chr, outputdir, denostring, time, inputtable data.frame, polyphemus output, if NULL filename is used. filename norm stretch tssl chr outputdir denostring time toplot subfunction of getresult. String, the path and name of polyphemus output if NULL the inputtable is used. String, normalization method numeric, if a length value is specified, the ChIP seq ratio profiles will be "stretched" or compacted to reach the length value. if NULL is specified the median length of the overall profiles is used. numeric, estimated length for the TSS region. The TSS part of the coding region will be not "stretched". numeric, the chromosome index. numeric, the chromosome index. String, column name for the pairwise comparison. String, time generated by Sys.time function Boolean, whether to plot lowess fit values

11 getresult 11 as getresult getresult final result output the getresult function permits to get the final table output suitable for further clustering analysis. getresult(objint, iter = "", outinttable = FALSE, out, norm = "quantile", stretchval, tsslength, den objint iter outinttable out norm stretchval tsslength denostring list, is the object output of the getnorm function. character. not used. logical, if TRUE save the intermediate intensity tables. character, is the path of the output folder. character, is the used normalization method. numeric, is the length value to use to uniformize the coding region length. See section (units: number of span). numeric, is the approximate length of the TSS to used to separate TSS and body regions (units : number of span). character, is the name of the denominator track. The stretchval and tsslength arguments takes a numerical value in -number of span- units. The span length (in bp) is an argument of the gwcomp and getint functions. Users can easily determine the number of span length to define the TSSlength. if one assumes the TSS length is 1000 bp and he uses a span of 10 bp. The TSSLength arguments will be 100. The same method can be used to calculate the stretchval argument. Value returns one file by ratio between tracks. These output files can then reload in R or used in external program for clustering analysis. Martial Sankar See also gwcomp, getint, getnorm.

12 12 getswv getswv get the Sliding Windows values by genes getswv, get the Sliding Windows Values by genes. Values are obtained using the median of the tags counts. getswv(gid, wiggtable, commontable, dbcurr, window = 250, span) gid wiggtable commontable dbcurr window span numeric, the Gene ID. data.frame,the entire wiggtable. data.frame, the results matrix of overlap function. data.frame, the entire database. numeric, the sliding window size. numeric, if NULL take the span of the WIGG. subfunction of getcodreg. Value return average inside the sliding window intensity. as getcodreg

13 gwcomp 13 gwcomp Polyphemus Wrapper The gwcomp function is the polyphemus wrapper. This calls the sub-fonctions requires to performs the comparative analysis of the ChIP-seq profiles (multi-states analysis) in a genome wide manner (whole genome analysis). gwcomp(initdir, posprocparam = c(null, NULL, NULL), prepost = c(500, 2000), window = 250, denomtrack InitDir posprocparam prepost window denomtrack database puttss span normmethod stretched TSSLength outinttable plotmva logfile character the path of the folder containing the pooled files. If the folder already contained hierarchized folder. nothing is done. vector, are the post-processing parameters c(window, pvalue filters, String path and name for output (NULL default)). vector, contains two values, the number of bp (base pair) before and the number of bp after the coding region. numeric, size of the sliding window (in bp). character, the denominator track name (string contained in the filename) character, path and name of the database file. logical, if TRUE, detect the putative TSS inside the coding region. (not yet implemented). numeric, if NULL take the span of the wig file else take the specified span. character, the normalization method : "linear" for linear normalization, "lowess" and "quantile" for local normalization. numeric, is the length value to use to uniformize the coding region length. See section (units: number of span). numeric, is the approximate length of the TSS to used to separate TSS and body regions (units : number of span). logical, if TRUE save the intermediate intensity tables. character, path and name to save the MVA plots. if NULL, the MVA figures are not plotted. logical, if TRUE a log file is save in the current folder.

14 14 gwcomp The denomtrack argument is the exact String of the denominator track contained in the filename. Its intensity values will be used as steady denominator when calculating the ratio between tracks. The posprocparam argument is a vector that contains three elements. The first is the window size where the TSS is centered. The second is a filter on the pvalue. The third is the file path and name of the output table. if NULL, the output file is not provided. The Polyphemus package provides three normalization procedure. Choosing a "linear" normalization, a correction factor is applied such that the sum of the intensities for a track is the same for all the tracks. Choosing a local normalisation, "lowess" or "quantile", a correction factor is locally calculating and applied to the track intensities. This turns to be more reliable than classic "linear" normalisation. Taslim et al. already mentionned it to normalize PolII ChIP-seq data. However when dealing with more than two samples, the "quatile" procedure turns to be more suitable as it requires less computing time. The "lowess" normalization used the lowess implementation of the R base package. The "quantile" procedure used the normalizequantiles of the limma package. The stretched and tsslength arguments takes a numerical value in -number of span- units. The span length (in bp) is an argument of the gwcomp and getint functions. Users can easily determine the number of span length to define the TSSlength. if one assumes the TSS length is 1000 bp and he uses a span of 10 bp. The TSSLength arguments will be 100. The same method can be used to calculate the stretched argument. Value returns one file by ratio between tracks. These output files can then reload in R or used in external program for clustering analysis. Martial Sankar References Taslim, C., J. Wu, et al. (2009). "Comparative study on ChIP-seq data: normalization and binding pattern characterization." Bioinformatics 25(18): See also the wrapped functions : getint, getnorm, getresult. Examples fpath1<-system.file("extdata", package="polyphemus") fpath2<-file.path(fpath1,"atra") ex <- gwcomp(initdir=fpath2, posprocparam=c(300, 0.1,NA), denomtrack="etoh", database=file.path(fpath1,"mouserefseq-genenames.txt"), normmethod="quantile", stretched =50, TSSLength = 10, window=1000, span=500,prepost=c(0,0), outinttable=true, plotmva=true)

15 lowessnorm 15 lowessnorm lowess normalization lowessnorm, perform the lowess normalisation. Called by the getnorm function. lowessnorm(listint, ff = 2/3, plotmva = NULL) listint ff plotmva numeric, vector of intensity (SWI) values. numeric, the span value for lowess. see lowess help. Boolean, whether to display the MVA plot (on the X). subfunction of getnorm. lowess, getnorm medichimerge Merge MeDiChi files. medichimerge, merge all medichi files for one chromosome & return the list of peaks and their position. medichimerge(files, windo, cutoff = 0.01, write = NA) files windo cutoff write vector, vector of String containing the path and name of each files. numeric, the length of the window range numeric, filers on the cutoff values of MediChi[1] String, path and file name for the output merged file. if NA, not output.

16 16 quantilenorm subfunction in getint References [1] Reiss, D. J., M. T. Facciotti, et al. (2008). "Model-based deconvolution of genome-wide DNA binding." Bioinformatics 24(3): getint quantilenorm quantile normalization lowessnorm, perform the quantile normalisation. Called by the getnorm function. quantilenorm(listint, plotmva = FALSE) listint plotmva numeric, vector of intensity (SWI) values. Boolean, whether to display the MVA plot (on the X). subfunction of getnorm. getnorm

17 stretchgene 17 stretchgene "stretch" genes. return a table with stretched body of the profile stretchgene(genetab, tsscut, lengthref) genetab tsscut lengthref data.frame, the gene table (id, logratio, fittedval). numeric,number of line considered as part of the TSS region. numeric, the length of the reference gene. subfunction of getoutput. Value data frame of "streched" or "compacted" body region of the gene. getoutput

18 Index Topic Wig generatewig, 4 Topic count getint, 6 Topic hierarchy createfolderhierarchy, 3 Topic intensity getint, 6 Topic normalisation getnorm, 9 Topic normalization getmvaplot, 8 getnorm, 9 lowessnorm, 15 quantilenorm, 16 Topic package polyphemus-package, 2 Topic ratio getoutput, 10 getresult, 11 Topic wig getincrbyg, 5 Topic wrapper gwcomp, 13 lowessnorm, 8, 15 medichimerge, 15 polyphemus (polyphemus-package), 2 polyphemus-package, 2 quantilenorm, 8, 16 stretchgene, 17 createfolderhierarchy, 3 generatewig, 4 getcodreg, 5, 7, 12 getincrbyg, 5 getint, 6, 9, 11, 14, 16 getlowessfitval, 7 getmvaplot, 8 getnorm, 9, 11, getoutput, 8, 10, 17 getresult, 11, 11, 14 getswv, 12 gwcomp, 7, 10, 11, 13, 14 lowess, 9, 14, 15 18