Package: seqsetvis 1.27.2

Joseph R Boyd

seqsetvis: Set Based Visualizations for Next-Gen Sequencing Data

seqsetvis enables the visualization and analysis of sets of genomic sites in next gen sequencing data. Although seqsetvis was designed for the comparison of mulitple ChIP-seq samples, this package is domain-agnostic and allows the processing of multiple genomic coordinate files (bed-like files) and signal files (bigwig files pileups from bam file). seqsetvis has multiple functions for fetching data from regions into a tidy format for analysis in data.table or tidyverse and visualization via ggplot2.

Authors:Joseph R Boyd [aut, cre]

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seqsetvis.pdf |seqsetvis.html✨
seqsetvis/json (API)
NEWS

# Install 'seqsetvis' in R:
install.packages('seqsetvis', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Datasets:

On BioConductor:seqsetvis-1.27.0(bioc 3.21)seqsetvis-1.26.0(bioc 3.20)

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

softwarechipseqmultiplecomparisonsequencingvisualization

5.97 score 77 scripts 260 downloads 2 mentions 69 exports 96 dependencies

Last updated 11 days agofrom:ae105777f8. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKDec 11 2024
R-4.5-winOKDec 11 2024
R-4.5-linuxOKDec 11 2024
R-4.4-winOKDec 11 2024
R-4.4-macOKDec 11 2024
R-4.3-winOKDec 11 2024
R-4.3-macOKDec 11 2024

Exports:add_cluster_annotationappend_ynormapplyMovingAverageapplySplineassemble_heatmap_cluster_barscalc_norm_factorscenterAtMaxcenterFixedSizeGRangescenterGRangesAtMaxclusteringKmeansclusteringKmeansNestedHclustcol2hexcollapse_grconvert_collapsed_coordcopy_clust_infocrossCorrByRleeasyLoad_bedeasyLoad_broadPeakeasyLoad_FUNeasyLoad_IDRmergedeasyLoad_narrowPeakeasyLoad_seacrexpandCigarfindMaxPosfragLen_calcStrandedfragLen_fromMacs2Xlsget_mapped_readsgetReadLengthggellipseharmonize_seqlengthsmake_clustering_matrixmerge_clustersprepare_fetch_GRangesprepare_fetch_GRanges_namesprepare_fetch_GRanges_widthquantileGRangesWidthreorder_clusters_hclustreorder_clusters_manualreorder_clusters_stepdownreverse_clusterssafeBrewsplit_clusterssvAnnotateSubjectGRangesssvConsensusIntervalSetsssvFactorizeMembTablessvFeatureBarsssvFeatureBinaryHeatmapssvFeatureEulerssvFeaturePiessvFeatureUpsetssvFeatureVennssvFetchBamssvFetchBamPEssvFetchBamPE.RNAssvFetchBigwigssvFetchGRangesssvFetchSignalssvMakeMembTablessvOverlapIntervalSetsssvSignalBandedQuantilesssvSignalClusteringssvSignalHeatmapssvSignalHeatmap.ClusterBarsssvSignalLineplotssvSignalLineplotAggssvSignalScatterplotviewGRangesWinSample_dtviewGRangesWinSummary_dtwithin_clust_sort

Dependencies:abindaskpassBHBiobaseBiocGenericsBiocIOBiocParallelBiostringsbitopsclicodetoolscolorspacecowplotcpp11crayoncurldata.tableDelayedArraydigesteulerrfansifarverformatRfsfutile.loggerfutile.optionsgenericsGenomeInfoDbGenomeInfoDbDataGenomicAlignmentsGenomicRangesGenSAggplot2ggplotifygluegridExtragridGraphicsgtablehttrIRangesisobandjsonlitelabelinglambda.rlatticelifecyclelimmamagrittrMASSMatrixMatrixGenericsmatrixStatsmgcvmimemunsellnlmeopensslpbapplypbmcapplypillarpkgconfigplyrpngpolyclippolylabelrR6RColorBrewerRcppRcppArmadilloRCurlrestfulrRhtslibrjsonrlangRsamtoolsrtracklayerS4ArraysS4VectorsscalessnowSparseArraystatmodSummarizedExperimentsystibbleUCSC.utilsUpSetRutf8vctrsviridisLitewithrXMLXVectoryamlyulab.utilszlibbioc

The seqsetvis package

Joseph R Boyd

Rendered fromseqsetvis_overview.Rmdusingknitr::rmarkdownon Dec 11 2024.

Last update: 2024-05-15
Started: 2018-03-29

Readme and manuals

Help Manual

Help pageTopics
easy awesome peak set vis TESTING seqsetvis allows you to...seqsetvis-package seqsetvis
Expand intermediate bam fetch by cigar codes.expand_cigar_dt
Expand intermediate bam fetch by cigar codes.expand_cigar_dt_recursive
Remove duplicate reads based on stranded start position. This is an over-simplification. For better duplicate handling, duplicates must be marked in bam and flag passed to fetchBam() ... for ScanBamParam.rm_dupes
Remove duplicate paired-end reads based on start and end position. This is an over-simplification. For better duplicate handling, duplicates must be marked in bam and flag passed to fetchBamPE() ... for ScanBamParam.rm_dupesPE
add_cluster_annotationadd_cluster_annotation
append_ynormappend_ynorm
applyMovingAverageapplyMovingAverage
applies a spline smoothing to a tidy data.table containing x and y values.applySpline
assemble_heatmap_cluster_barsassemble_heatmap_cluster_bars
4 random peaks for paired-end dataBcell_peaks
calc_norm_factorscalc_norm_factors
centers profile of x and y. default is to center by region but across all samples.centerAtMax
Transforms set of GRanges to all have the same size.centerFixedSizeGRanges
Centers query GRanges at maximum signal in prof_dt.centerGRangesAtMax
MCF10A CTCF profiles at 20 windows per chromHMM state, hg38.chromHMM_demo_bw_states_gr
URL to download hg19ToHg38 liftover chain from UCSCchromHMM_demo_chain_url
chromHMM state segmentation in the MCF7 cell linechromHMM_demo_data
overlap of MCF10A CTCF with MCF7 chromHMM states, hg38.chromHMM_demo_overlaps_gr
URL to download hg19 MCF7 chromHMM segmentationchromHMM_demo_segmentation_url
original state name to color mappings stored in segmentation bedchromHMM_demo_state_colors
state name to total width mappings, hg38chromHMM_demo_state_total_widths
perform kmeans clustering on matrix rows and return reordered matrix along with order matched cluster assignments. clusters are sorted using hclust on centersclusteringKmeans
perform kmeans clustering on matrix rows and return reordered matrix along with order matched cluster assignments clusters are sorted using hclust on centers the contents of each cluster are sorted using hclustclusteringKmeansNestedHclust
converts a valid r color name ("black", "red", "white", etc.) to a hex valuecol2hex
collapse_grcollapse_gr
convert_collapsed_coordconvert_collapsed_coord
copy_clust_infocopy_clust_info
Calculate cross correlation by using shiftApply on read coverage RlecrossCorrByRle
FTP URL path for vignette data.CTCF_in_10a_bigWig_urls
CTCF ChIP-seq in breast cancer cell linesCTCF_in_10a_data
list of GRanges that results in 100 random subset when overlappedCTCF_in_10a_narrowPeak_grs
FTP URL path for vignette data. fromCTCF_in_10a_narrowPeak_urls
100 randomly selected regions from overlapping CTCF peaks in 10a cell ChIP-seqCTCF_in_10a_overlaps_gr
Profiles for 100 randomly selected regions from overlapping CTCF peaks in 10a cell ChIP-seq Results from fetching bigwigs with CTCF_in_10a_overlaps_gr.CTCF_in_10a_profiles_dt
Profiles for 100 randomly selected regions from overlapping CTCF peaks in 10a cell ChIP-seq Results from CTCF_in_10a_overlaps_grCTCF_in_10a_profiles_gr
easyLoad_bed takes a character vector of file paths to bed plus files and returning named list of GRanges.easyLoad_bed
easyLoad_broadPeak takes a character vector of file paths to narrowPeak files from MACS2 and returns a named list of GRanges.easyLoad_broadPeak
easyLoad_FUN takes a character vector of file paths run an arbitrary function defined in load_FUNeasyLoad_FUN
easyLoad_IDRmerged loads "overlapped-peaks.txt" from IDR.easyLoad_IDRmerged
easyLoad_narrowPeak takes a character vector of file paths to narrowPeak files from MACS2 and returns a named list of GRanges.easyLoad_narrowPeak
easyLoad_seacr takes a character vector of file paths to seacr output bed files and returns a named list of GRanges.easyLoad_seacr
Expand cigar codes to GRangesexpandCigar
fetch a bam file pileup with the ability to consider read extension to fragment size (fragLen)fetchBam
findMaxPosfindMaxPos
calculate fragLen from a bam file for specified regionsfragLen_calcStranded
parse fragLen from MACS2 outputfragLen_fromMacs2Xls
get_mapped_readsget_mapped_reads
determine the most common read length for input bam_file. uses 50 randomly selected regions from query_gr. If fewer than 20 reads are present, loads all of query_gr.getReadLength
ggellipseggellipse
harmonize_seqlengthsharmonize_seqlengths
make_clustering_matrixmake_clustering_matrix
merge_clustersmerge_clusters
prepares GRanges for windowed fetching.prepare_fetch_GRanges
Creates a named version of input GRanges using the same method seqsetvis uses internally to ensure consistency.prepare_fetch_GRanges_names
prepares GRanges for windowed fetching.prepare_fetch_GRanges_width
Quantile width determination strategyquantileGRangesWidth
reorder_clusters_hclustreorder_clusters_hclust
reorder_clusters_manualreorder_clusters_manual
reorder_clusters_stepdownreorder_clusters_stepdown
reverse_clustersreverse_clusters
safeBrewsafeBrew
convert a list of sets, each list item should be a character vector denoting items in setsset_list2memb
orients the relative position of x's zero value and extends ranges to be contiguousshift_anchor
split_clustersplit_cluster
ssv_mclapplyssv_mclapply
ssvAnnotateSubjectGRangesssvAnnotateSubjectGRanges ssvAnnotateSubjectGRanges,GRanges-method ssvAnnotateSubjectGRanges,GRangesList-method ssvAnnotateSubjectGRanges,list-method
Intersect a list of GRanges to create a single GRanges object of merged ranges including metadata describing overlaps per input GRanges.ssvConsensusIntervalSets
Convert any object accepted by ssvMakeMembTable to a factor To avoid ambiguity,ssvFactorizeMembTable
bar plots of set sizesssvFeatureBars
ssvFeatureBinaryHeatmapssvFeatureBinaryHeatmap
Try to load a bed-like file and convert it to a GRanges objectssvFeatureEuler
ssvFeaturePiessvFeaturePie
ssvFeatureUpsetssvFeatureUpset
ssvFeatureVennssvFeatureVenn
Iterates a character vector (ideally named) and calls 'ssvFetchBam.single' on each. Appends grouping variable to each resulting data.table and uses rbindlist to efficiently combine resultsssvFetchBam
fetch a windowed version of a bam file, returns GRangesssvFetchBam.single
ssvFetchBam for paired-end ChIP-seq files. Only concordant reads are considered, but this has been minimally tested, please verify.ssvFetchBamPE
ssvFetchBamPE.RNAssvFetchBamPE.RNA
fetch a windowed version of a paired-end bam file, returns GRanges In contrast to ssvFetchBam, extension of reads to estimated fragment size is not an issue as each read pair represents a fragment of exact size.ssvFetchBamPE.single
Iterates a character vector (ideally named) and calls 'ssvFetchBigwig.single' on each. Appends grouping variable to each resulting data.table and uses rbindlist to efficiently combine results.ssvFetchBigwig
Fetch values from a bigwig appropriate for heatmaps etc.ssvFetchBigwig.single
Fetch coverage values for a list of GRanges.ssvFetchGRanges
signal loading frameworkssvFetchSignal
generic for methods to convert various objects to a logical matrix indicating membership of items (rows) in sets (columns)ssvMakeMembTable ssvMakeMembTable,data.frame-method ssvMakeMembTable,DataFrame-method ssvMakeMembTable,GRanges-method ssvMakeMembTable,GRangesList-method ssvMakeMembTable,list-method ssvMakeMembTable,matrix-method
Intersect a list of GRanges to create a single GRanges object of merged ranges including metadata describing overlaps per input GRangesssvOverlapIntervalSets
plot profiles from bigwigsssvSignalBandedQuantiles
Clustering as for a heatmap. This is used internally by 'ssvSignalHeatmap' but can also be run before calling ssvSignalHeatmap for greater control and access to clustering results directly.ssvSignalClustering
heatmap style representation of membership table. instead of clustering, each column is sorted starting from the left.ssvSignalHeatmap
heatmap style representation of membership table. instead of clustering, each column is sorted starting from the left.ssvSignalHeatmap.ClusterBars
construct line type plots where each region in each sample is representedssvSignalLineplot
aggregate line signals in a single line plotssvSignalLineplotAgg
maps signal from 2 sample profiles to the x and y axis. axes are standard or "volcano" min XY vs fold-change Y/XssvSignalScatterplot
4 random peaks for single-end data and 4 control regions 30kb downstream from each peak.test_peaks
get a windowed sampling of score_grviewGRangesWinSample_dt
Summarizes signal in bins. The same number of bins per region in qgr is used and widths can vary in qgr, in contrast to 'viewGRangesWinSample_dt' where width must be constant across regions.viewGRangesWinSummary_dt
within_clust_sortwithin_clust_sort