Package TilePlot. February 15, 2013
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1 Package TilePlot February 15, 2013 Type Package Title Characterization of functional genes in complex microbial communities using tiling DNA microarrays Version 1.3 Date Author Ian Marshall Maintainer Ian Marshall This package is intended for processing the output from functional gene tiling DNA microarray experiments. It produces hybridization pattern plots for each gene on the array, and statistics for each gene including mean probe intensity, median probe intensity, bright probe fraction, bright segment length dependent score, bright probe mean intensity, and bright probe median intensity. Output is generated in order of bright segment length dependent score in both a latex/eps format and tab-delimited text file. The package works in two modes: single array, and comparison of two arrays. Array comparison includes array comparison statistics: median of logarithm of one array probe divided by its counterpart on the other array, median absolute deviation of that value, and the binomial test to see whether the genes are equally abundant in both arrays. License GPL-2 LazyLoad yes Repository CRAN Date/Publication :57:46 NeedsCompilation no 1
2 2 TilePlot-package R topics documented: TilePlot-package compareplot tileplot.double tileplot.single Index 7 TilePlot-package Package for analysis of Functional Gene Tiling DNA Microarrays Details : This package is intended for processing the output from functional gene tiling DNA microarray experiments. It produces hybridization pattern plots for each gene on the array, and statistics for each gene including mean probe intensity, median probe intensity, bright probe fraction, bright segment length dependent score, bright probe mean intensity, and bright probe median intensity. Output is generated in order of bright segment length dependent score in both a latex/eps format and tab-delimited text file. The package works in two modes: single array, and comparison of two arrays. Array comparison includes array comparison statistics: median of logarithm of one array probe divided by its counterpart on the other array, median absolute deviation of that value, and the binomial test to see whether the genes are equally abundant in both arrays. Package: TilePlot Type: Package Version: 1.3 Date: License: GPL-2 LazyLoad: yes Ian Marshall <ianpgm@stanford.edu>
3 compareplot 3 compareplot Function for comparing two vectors and performing linear regression Usage This function is used internally by tileplot.double to produce scatterplots with linear regression for all probes on the microarray or other whole-microarray vectors of parameters like gene median probe intensity etc. compareplot(arrayset1, arrayset2, array1label, array2label, title) Arguments arrayset1 arrayset2 The first vector of microarry data The second vector of micrarray data array1label The identifying label for array 1 array2label The identifying label for array 2 title Ian Marshall The title to be printed for the plot tileplot.double TilePlot 2-array Microarray Processing Function This function is for processing a two functional gene tiling DNA microarrays of the same design for the purposes of comparison. As input it takes array data, and its output is the hybridization plot for each gene on the array, the mean probe intensity, the median probe intensity, the bright probe fraction, the bright segment length dependent score, the mean bright probe intensity, and the median bright probe intensity. The data for array2data can be normalized to the array1data using loess - ie an assumption is made that all spots on both arrays should be approximately the same, then all array2data is adjusted to fit that assumption - the modified array2data is given as output in the graphdirectory. The output is both graphical (as a set of tex and encapsulated-postscript format files that can be assembled using latex) and a tab-delimited text file with rows corresponding to genes and columns corresponding to output parameters.
4 4 tileplot.double Usage tileplot.double(genesonchip, array1data, array2data, annotationslist, cutoff, cutoff_multiplier = 3, Arguments genesonchip array1data Path to a text file containing a list of all gene identifiers (I try to use IMG/M identifiers) on the microarray. Path to a tab-delimited text file for the first array containing all probe identifiers in the first (left-hand) column and probe intensities in the second (right-hand) column. File must be ordered in a specific way (see below). array2data Path to a tab-delimited text file for the second array containing all probe identifiers in the first (left-hand) column and probe intensities in the second (righthand) column. File must be ordered in a specific way (see below). annotationslist Path to a text file containing a list of all gene annotations. Each annotation must contain the gene identifier mentioned in genesonchip cutoff An integer cutoff value for distinguishing dark probes from bright probes based on probe intensity value. This is optional - the default value is three times the median value for all probes on Array 1. cutoff_multiplier Alternatively the cutoff can be expressed as a multiple of the median value for all probes on the array. The default is 3, but in case you wish to trade off specificity for sensitivity (or vice versa) in complex samples then you can set the multiplier lower (or higher) using this parameter. outputfile Path leading to desired output filename (a.tex file for input into latex). graphdirectory A directory for output of plots in encapsulated postscript format. outputtable Path leading to desired output filename (a.tdt file for further analysis of statistics for each individual gene). array1name Identifier for Array 1 (Default is "Array 1"). array2name Identifier for Array 2 (Default is "Array 2"). loess Use loess normalization? TRUE or FALSE. TRUE is the default. smoothing_factor Smoothing factor for BPF recommendation. The default of 6 is usually OK. Details All probe names must have the following format: geneidentifier-probenumber. array1data must be sorted so that the probes for each gene are in the correct order (ie from probe 1 to the final probe). This is easily achieved with the unix sort command (sort -n -k2 -t- filename > sorted\_filename). The output file should be processed with LaTeX ( to obtain a PDF. Ian Marshall
5 tileplot.single 5 Examples #This example code will deposit tex, tdt, and eps output into your R session directory tileplot.double(genesonchip=system.file("allgenesonchip.id", package="tileplot"), array1data=system.file("arra tileplot.single TilePlot Single Microarray Processing Function Usage This function is for processing a single functional gene tiling DNA microarray. As input it takes array data, and its output is the hybridization plot for each gene on the array, the mean probe intensity, the median probe intensity, the bright probe fraction, the bright segment length dependent score, the mean bright probe intensity, and the median bright probe intensity. The output is both graphical (as a set of tex and encapsulated-postscript format files that can be assembled using latex) and a tab-delimited text file with rows corresponding to genes and columns corresponding to output parameters. tileplot.single(genesonchip, array1data, annotationslist, cutoff, cutoff_multiplier = 3, outputfile, Arguments genesonchip Path to a text file containing a list of all gene identifiers (I try to use IMG/M identifiers) on the microarray. array1data Path to a tab-delimited text file containing all probe identifiers in the first (lefthand) column and probe intensities in the second (right-hand) column. File must be ordered in a specific way (see below). annotationslist Path to a text file containing a list of all gene annotations. Each annotation must contain the gene identifier mentioned in genesonchip cutoff An integer cutoff value for distinguishing dark probes from bright probes based on probe intensity value. This is optional - the default value is three times the median value for all probes on the array. cutoff_multiplier Alternatively the cutoff can be expressed as a multiple of the median value for all probes on the array. The default is 3, but in case you wish to trade off specificity for sensitivity (or vice versa) in complex samples then you can set the multiplier lower (or higher) using this parameter. outputfile Path leading to desired output filename (a.tex file for input into latex). graphdirectory A directory for output of plots in encapsulated postscript format.
6 6 tileplot.single Details outputtable Path leading to desired output filename (a.tdt file for further analysis of statistics for each individual gene). array1name Identifier for Array 1 (Default is "Array 1"). smoothing_factor Smoothing factor for BPF recommendation. The default of 6 is usually OK. All probe names must have the following format: geneidentifier-probenumber. array1data must be sorted so that the probes for each gene are in the correct order (ie from probe 1 to the final probe). This is easily achieved with the unix sort command (sort -n -k2 -t- filename > sorted\_filename). The output file should be processed with LaTeX ( to obtain a PDF. Ian Marshall Examples #This example code will deposit tex, tdt, and eps output into your R session directory tileplot.single(genesonchip=system.file("allgenesonchip.id", package="tileplot"), array1data=system.file("arra
7 Index compareplot, 3 TilePlot (TilePlot-package), 2 tileplot (TilePlot-package), 2 TilePlot-package, 2 tileplot.double, 3 tileplot.single, 5 7
Package TilePlot. April 8, 2011
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