Package: aCGH 1.85.0
Peter Dimitrov
aCGH: Classes and functions for Array Comparative Genomic Hybridization data
Functions for reading aCGH data from image analysis output files and clone information files, creation of aCGH S3 objects for storing these data. Basic methods for accessing/replacing, subsetting, printing and plotting aCGH objects.
Authors:
aCGH_1.85.0.tar.gz
aCGH_1.85.0.zip(r-4.5)aCGH_1.85.0.zip(r-4.4)aCGH_1.85.0.zip(r-4.3)
aCGH_1.85.0.tgz(r-4.4-x86_64)aCGH_1.85.0.tgz(r-4.4-arm64)aCGH_1.85.0.tgz(r-4.3-x86_64)aCGH_1.85.0.tgz(r-4.3-arm64)
aCGH_1.85.0.tar.gz(r-4.5-noble)aCGH_1.85.0.tar.gz(r-4.4-noble)
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aCGH.pdf |aCGH.html✨
aCGH/json (API)
| # Install 'aCGH' in R: |
| install.packages('aCGH', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org')) |
- colorectal - Colorectal array CGH dataset
- ex.acgh.hmm - Class aCGH
On BioConductor:aCGH-1.85.0(bioc 3.21)aCGH-1.84.0(bioc 3.20)
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
copynumbervariationdataimportgeneticscpp
Last updated 2 months agofrom:061926db3d. Checks:OK: 9. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 28 2024 |
| R-4.5-win-x86_64 | OK | Nov 28 2024 |
| R-4.5-linux-x86_64 | OK | Nov 28 2024 |
| R-4.4-win-x86_64 | OK | Nov 28 2024 |
| R-4.4-mac-x86_64 | OK | Nov 28 2024 |
| R-4.4-mac-aarch64 | OK | Nov 28 2024 |
| R-4.3-win-x86_64 | OK | Nov 28 2024 |
| R-4.3-mac-x86_64 | OK | Nov 28 2024 |
| R-4.3-mac-aarch64 | OK | Nov 28 2024 |
Exports:[.aCGHaCGH.processaCGH.read.SprocsaCGH.testchangeProp.funcchangeProp.overall.funcclone.namesclone.names<-clones.infoclusterGenomecol.names.aCGHcol.names<-.aCGHcolnames.aCGHcolnames<-.aCGHcombine.funccomputeSD.funccomputeSD.Samplescornacreate.aCGHcreate.resTdim.aCGHdotify.namesextract.clones.infofga.funcfind.genomic.eventsfind.hmm.statesfindAber.funcfindAmplif.funcfindOutliers.funcfindTrans.funcfloorFuncgainLossgenomic.eventsgenomic.events<-heatmaphmmhmm.mergedhmm.merged<-hmm.run.funchmm<-human.chrom.info.Jul03human.chrom.info.May04impute.HMMimpute.lowessis.aCGHis.evenis.oddlengthGain.nalengthLoss.nalengthNumFunclog2.ratioslog2.ratios.imputedlog2.ratios.imputed<-maPalettemaxdiff.funcmaxnamergeFuncmergeHmmStatesmergeLevelsmincorr.funcminnancol.aCGHnrow.aCGHnum.chromosomesnum.clonesnum.samplesphenotypephenotype<-plot.aCGHplotCGH.funcplotCGH.hmm.funcplotChromplotChrom.grey.samples.funcplotChrom.hmm.funcplotChrom.samples.funcplotfreq.givenstat.final.colors.funcplotfreq.stat.chrom.final.funcplotfreq.stat.final.funcplotFreqStatplotFreqStatColorsplotFreqStatGreyplotGeneplotGeneSignplotGenomeplotHmmStatesplotHmmStatesNewplotSummaryProfileplotValChromplotvalChrom.funcplotValGenomeprint.aCGHprop.napropGain.napropLoss.napropNumFuncread.Sproc.filesrow.names.aCGHrow.names<-.aCGHrownames.aCGHrownames<-.aCGHsample.namessample.names<-sd.samplessd.samples<-states.hmm.funcsubset.hmmsubset.hmm.mergedsummarize.clonessummary.aCGHtable.bac.functhreshold.func
Dependencies:BiobaseBiocGenericsclustergenericslatticeMASSMatrixmulttestsurvival
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Class aCGH | aCGH clone.names clone.names<- clones.info col.names.aCGH col.names<-.aCGH colnames.aCGH colnames<-.aCGH corna create.aCGH dim.aCGH ex.acgh.hmm floorFunc genomic.events genomic.events<- hmm hmm.merged hmm.merged<- hmm<- is.aCGH is.even is.odd lengthNumFunc log2.ratios log2.ratios.imputed log2.ratios.imputed<- maxna minna ncol.aCGH nrow.aCGH num.chromosomes num.clones num.samples phenotype phenotype<- plot.aCGH print.aCGH propNumFunc row.names.aCGH row.names<-.aCGH rownames.aCGH rownames<-.aCGH sample.names sample.names<- sd.samples sd.samples<- subset.hmm subset.hmm.merged summary.aCGH [.aCGH |
| Process data in aCGH object | aCGH.process |
| Create object of class "aCGH" from Sproc files | aCGH.read.Sprocs dotify.names extract.clones.info maxdiff.func mincorr.func read.Sproc.files |
| Testing association of aCGH clones with censored or continuous outcomes | aCGH.test mt.maxT mt.minP |
| clustering and heatmap | clusterGenome plotChrom plotValChrom plotvalChrom.func plotValGenome |
| Colorectal array CGH dataset | clones.info.ex colorectal log2.ratios.ex pheno.type.ex |
| Function to estimate experimental variability of a sample | computeSD.func computeSD.Samples |
| Function to compute fraction of genome altered for each sample | fga.func |
| Finds the genomic events associated with each of the array CGH samples | find.genomic.events |
| Determines states of the clones | as.matrix.dist find.hmm.states hmm.run.func plotCGH.hmm.func plotChrom.grey.samples.func plotChrom.hmm.func plotChrom.samples.func smoothData.func thresholdData.func |
| Function to determines focal aberrations | findAber.func |
| Function to determine high level amplifications | findAmplif.func |
| Function to identify outlier clones | findOutliers.func |
| Funtion identifying the transitions | findTrans.func |
| Function to compute proportion of gains and losses for each clones | gainLoss |
| Creates heatmap array CGH objects | heatmap |
| Basic Chromosomal Information for UCSC Human Genome Assembly July 2003 freeze | human.chrom.info.Jul03 |
| Basic Chromosomal Information for UCSC Human Genome Assembly May 2004 freeze | human.chrom.info.May04 |
| Imputing log2 ratios using HMM | impute.HMM |
| Imputing log2 ratios | impute.lowess |
| Funtion to merge states based on their state means | mergeFunc mergeHmmStates |
| mergeLevels | combine.func mergeLevels |
| frequency plots and significance analysis | changeProp.func changeProp.overall.func create.resT lengthGain.na lengthLoss.na plotfreq.givenstat.final.colors.func plotfreq.stat.chrom.final.func plotfreq.stat.final.func plotFreqGivenStat plotfreqGivenStatFinalColors plotFreqStat plotFreqStatColors plotFreqStatGrey prop.na propGain.na propLoss.na table.bac.func |
| Plots the genome | maPalette plotCGH.func plotGene plotGeneSign plotGenome |
| Plotting the estimated hmm states and log2 ratios for each sample. | plotHmmStates plotHmmStatesNew |
| plotSummaryProfile | plotSummaryProfile |
| A function to fit unsupervised Hidden Markov model | states.hmm.func |
| Extracting summary information for all clones | summarize.clones |
| Function to indicate gain or loss for each clone for each sample | threshold.func |