Man pages for jmonlong/PopSV
Population-based detection of structural variants from Read-Depth signal
| aneuploidy.flag | Flag chromosomal aneuploidy |
| bin.bam | Get read counts from BAM file |
| bin.bam.2d | Get read counts between pairs of bins from BAM file |
| bin.bw | Get read counts from BigWig coverage file |
| breakpoint.finder.interactive | Interactive breakpoint finder |
| call.abnormal.cov | Call abnormal bins |
| chrplot | Chromosom plot of CNVs |
| chunk.bin | Split the bins in chunks for parallel normalization |
| cluster.regions | Cluster samples using their abnormal regions |
| cn.plot | Plot copy-number estimates in a region. |
| comp.index.files | Compress and index BED-like files |
| correct.GC | Correct GC bias |
| coverage.plot | Plot the bin coverage in a region |
| coverage.plot.raw | Plot the raw coverage in a region |
| createEmptyDF | Create empty data.frame |
| draw.controls | Select control genomic regions for enrichment analysis |
| fdrtool.quantile | P-values estimation |
| fdrtool.quantile.2N | P-values estimation from mixture of 2 centered normal |
| filter.noncovered.bins | Filter non-covered bins |
| find.comparable.bins | Find comparable bins between two sample cohort. |
| fragment.genome | Fragment a genome |
| fragment.genome.hg19 | Fragment hg19 genome |
| freq.range | Frequency computation for ranges |
| gender.predict | Predict the gender of sample(s) |
| getGC | GC content computation for specific bins |
| getGC.hg19 | GC content computation for specific bins |
| init.filenames | Init file names for analysis |
| localMax | Local maximum |
| mean.sd.outlierR | Mean and standard diviation after outlier removal |
| med.norm | Global median normalization |
| medvar.norm | Median-variance normalization of bin counts |
| medvar.norm.internal | Median-variance normalization of bin counts |
| mergeConsBin.cbs | Segmentation using CBS (NOT READY) |
| mergeConsBin.reduce | Merge nearby bins |
| mergeConsBin.simple | Simple merge of abnormal bins |
| mergeConsBin.z | Merge abnormal consecutive bins |
| normQC | Normalized bin count QC metrics for normalization benchmark |
| norm.tm.opt | Trimmed-Mean normalization optimized |
| pair.discordant.reads | Pair discordant reads |
| pca.norm | PCA-based normalization of bin counts |
| PopSV-package | Population-based detection of structural variants from... |
| qc.sample | QC samples |
| qc.samples | Join and QC the reference samples |
| qc.samples.cluster | QC sample : clustering |
| qc.zshift.unaffected | QC: Z-score bias in unaffected samples |
| quant.norm | Quantile normalization |
| quick.count | Counts reads across samples in a small number of bins |
| read.bedix | Retrieve a subset of an indexed BED file. |
| samples.merge | Merge data.frame across samples |
| sv.summary | Abnormal regions summary |
| sv.summary.interactive | Interactive summary of the calls |
| tmm.norm | Trimmed-Mean M normalization |
| tnK.norm | Weighted targeted normalization using K-mean optimization. |
| tn.norm | Targeted-normalization of bin counts |
| tn.norm.qc | QC graphs for Targeted Normalization |
| tn.norm.qc.div | Targeted normalization QC: diversity of the supporting bins |
| tn.test.sample | Single sample targeted normalization and test |
| weight.ref.pca | Weight sample difference from a set of reference samples |
| winsor | Winsorize a vector |
| write.split.samples | Split and write results in one file per sample |
| writeVcf | Write VCF file |
| z.comp | Z-score computation |
| z.norm | Z-score normalization |
| z.thres.cons.bins | Z-score thresholding using bin consecutiveness. |
