Introduction | Overview of SVP | Install | Quantification of cell states using SVP | Quantification of BIOCARTA pathway or other function | Quantification of cell-type | Quantification of cell-type using the pre-established marker gene sets | Quantification of cell-type using the reference single-cell data | Spatial statistical analysis | Univariate spatial statistical analysis | Identification of spatial variable features | Identification of local spatial aggregation areas | Bivariate spatial statistical analysis | Global bivariate spatial analysis | Local bivariate spatial analysis | Session information | References
Package: SVP 1.5.0
Shuangbin Xu
SVP: Predicting cell states and their variability in single-cell or spatial omics data
SVP uses the distance between cells and cells, features and features, cells and features in the space of MCA to build nearest neighbor graph, then uses random walk with restart algorithm to calculate the activity score of gene sets (such as cell marker genes, kegg pathway, go ontology, gene modules, transcription factor or miRNA target sets, reactome pathway, ...), which is then further weighted using the hypergeometric test results from the original expression matrix. To detect the spatially or single cell variable gene sets or (other features) and the spatial colocalization between the features accurately, SVP provides some global and local spatial autocorrelation method to identify the spatial variable features. SVP is developed based on SingleCellExperiment class, which can be interoperable with the existing computing ecosystem.
Authors:
SVP_1.5.0.tar.gz
SVP_1.5.0.zip(r-4.7)SVP_1.5.0.zip(r-4.6)SVP_1.5.0.zip(r-4.5)
SVP_1.5.0.tgz(r-4.6-x86_64)SVP_1.5.0.tgz(r-4.6-arm64)SVP_1.5.0.tgz(r-4.5-x86_64)SVP_1.5.0.tgz(r-4.5-arm64)
SVP_1.5.0.tar.gz(r-4.7-arm64)SVP_1.5.0.tar.gz(r-4.7-x86_64)SVP_1.5.0.tar.gz(r-4.6-arm64)SVP_1.5.0.tar.gz(r-4.6-x86_64)
SVP_1.5.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
SVP/json (API)
| # Install 'SVP' in R: |
| install.packages('SVP', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/yulab-smu/svp/issues
Uses libs:
Datasets:
- CancerSEAEnsemble - The Gene List of Cancer Single-cell State Atlas
- CancerSEASymbol - The Gene List of Cancer Single-cell State Atlas
- CellCycle.Hs - The Cell Cycle gene set
- hpda_spe_cell_dec - An example of result of runSGSA by extracting with gsvaExp
- sceSubPbmc - A subset data of pbmck3 from SeuratData
- SenMayoSymbol - A gene set identifies senescent cells and predicts senescence-associated pathways across tissues
On BioConductor:SVP-1.5.0(bioc 3.24)SVP-1.4.0(bioc 3.23)
singlecellsoftwarespatialtranscriptomicsgenetargetgeneexpressiongenesetenrichmenttranscriptiongokeggopenblascppopenmp
Last updated from:4e36f875d9. Checks:1 NOTE, 11 WARNING, 2 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| bioc-checks | NOTE | 334 | ||
| linux-devel-arm64 | WARNING | 465 | ||
| linux-devel-x86_64 | WARNING | 589 | ||
| source / vignettes | OK | 617 | ||
| linux-release-arm64 | WARNING | 489 | ||
| linux-release-x86_64 | WARNING | 555 | ||
| macos-release-arm64 | WARNING | 338 | ||
| macos-release-x86_64 | WARNING | 601 | ||
| macos-oldrel-arm64 | WARNING | 284 | ||
| macos-oldrel-x86_64 | WARNING | 604 | ||
| windows-devel | WARNING | 658 | ||
| windows-release | WARNING | 599 | ||
| windows-oldrel | WARNING | 604 | ||
| wasm-release | OK | 328 |
Exports:as_tbl_dfcal_lisa_f1cluster.assigncoerceextract_weight_adjfast_corfscoreDffscoreDf<-fscoreDfNamesfscoreDfNames<-fscoreDfsfscoreDfs<-gsvaExpgsvaExp<-gsvaExpNamesgsvaExpNames<-gsvaExpsgsvaExps<-imgDataimgData<-LISAResultLISAscemainGsvaExpNamemainGsvaExpName<-plot_heatmap_globalbvpred.cell.signaturerunCORRrunDetectMarkerrunDetectSVGrunENCODErunGLOBALBVrunKldSVGrunLISArunLOCALBVrunMCArunSGSArunWKDEshowspatialCoordsspatialCoords<-spatialCoordsNamesspatialCoordsNames<-svDfsvDf<-svDfNamessvDfNames<-svDfssvDfs<-SVPExperiment
Dependencies:abindapeaplotaskpassassortheadbase64encbeachmatBHBiobaseBiocFileCacheBiocGenericsBiocNeighborsBiocParallelbitbit64blobbslibcachemclicodetoolscpp11curlDBIdbplyrDelayedArrayDelayedMatrixStatsdeldirdigestdplyrdqrngevaluatefarverfastmapfastmatchfilelockfontawesomefontBitstreamVerafontLiberationfontquiverformatRfsfutile.loggerfutile.optionsgdtoolsgenericsGenomicRangesggfunggiraphggplot2ggplotifyggstarggtreegluegridExtragridGraphicsgtablehighrhtmltoolshtmlwidgetshttr2IRangesisobandjquerylibjsonliteknitrlabelinglambda.rlatticelazyevallifecyclemagickmagrittrMASSMatrixMatrixGenericsmatrixStatsmemoisemimenlmeopensslpatchworkpillarpkgconfigpracmapurrrR6rappdirsRColorBrewerRcppRcppArmadilloRcppEigenRcppParallelrjsonrlangrmarkdownRSQLiteS4ArraysS4VectorsS7sassscalesSeqinfoSingleCellExperimentsitmosnowSparseArraysparseMatrixStatsSpatialExperimentstringistringrSummarizedExperimentsyssystemfontstibbletidyrtidyselecttidytreetinytextreeioutf8vctrsviridisLitewithrxfunXVectoryamlyulab.utils
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| convert the square matrix to long tidy table | as_tbl_df |
| calculate the F1 value based on LISA result in the specified category. | cal_lisa_f1 cal_lisa_f1,SingleCellExperiment cal_lisa_f1,SingleCellExperiment-method |
| the Cell Cycle gene set | CellCycle.Hs data_CellCycle.Hs |
| clusting and assign the label for each feature(specify the gene sets). | cluster.assign cluster.assign,SingleCellExperiment cluster.assign,SingleCellExperiment-method cluster.assign,SVPExperiment cluster.assign,SVPExperiment-method |
| The Gene List of Cancer Single-cell State Atlas (CancerSEA) | CancerSEAEnsemble CancerSEASymbol data_CacerSEA data_CancerSEA |
| an example of result of runSGSA by extracting with gsvaExp | data_hpda_spe_cell_dec hpda_spe_cell_dec |
| a subset data of pbmck3 from SeuratData | data_sceSubPbmc sceSubPbmc |
| A gene set identifies senescent cells and predicts senescence-associated pathways across tissues | data_SenMayo SenMayoSymbol |
| extract the cell adjacent matrix from spatial space or reduction space | extract_weight_adj extract_weight_adj,SingleCellExperiment extract_weight_adj,SingleCellExperiment-method |
| Calculation of correlations and associated p-values | fast_cor |
| features score matrix extract method | fscoreDf fscoreDf,SingleCellExperiment,character-method fscoreDf,SingleCellExperiment,missing-method fscoreDf,SingleCellExperiment,numeric-method fscoreDf<- fscoreDf<-,SingleCellExperiment,character-method fscoreDf<-,SingleCellExperiment,missing-method fscoreDf<-,SingleCellExperiment,numeric-method fscoreDfNames fscoreDfNames,SingleCellExperiment-method fscoreDfNames<- fscoreDfNames<-,SingleCellExperiment,character-method fscoreDfs fscoreDfs,SingleCellExperiment-method fscoreDfs<- fscoreDfs<-,SingleCellExperiment-method |
| Gene Set Variation Analysis Experiment methods | c,SCEByColumn-method gsvaExp gsvaExp,SVPExperiment,character-method gsvaExp,SVPExperiment,missing-method gsvaExp,SVPExperiment,numeric-method gsvaExp<- gsvaExp<-,SVPExperiment,character-method gsvaExp<-,SVPExperiment,missing-method gsvaExp<-,SVPExperiment,numeric-method gsvaExpNames gsvaExpNames,SVPExperiment-method gsvaExpNames<- gsvaExpNames<-,SVPExperiment,character-method gsvaExps gsvaExps,SVPExperiment-method gsvaExps<- gsvaExps<-,SVPExperiment-method length,SCEByColumn-method mainGsvaExpName mainGsvaExpName,SVPExperiment-method mainGsvaExpName<- mainGsvaExpName<-,SVPExperiment,character_OR_NULL-method names,SCEByColumn-method names<-,SCEByColumn-method [,SCEByColumn,ANY,ANY,ANY-method [<-,SCEByColumn,ANY,ANY,ANY-method |
| LISAResult | LISAResult |
| convert LISA result to SVPExperiment. | LISAsce LISAsce,SingleCellExperiment LISAsce,SingleCellExperiment-method |
| the marker genes of mouse olfactory bulb | mob_marker_genes |
| the single cell gene profiler of a mouse olfactory bulb | mob_sce |
| plot_heatmap_globalbv | plot_heatmap_globalbv |
| predict the cell signature according the gene sets or pathway activity score. | pred.cell.signature pred.cell.signature,SingleCellExperiment pred.cell.signature,SingleCellExperiment-method pred.cell.signature,SVPExperiment pred.cell.signature,SVPExperiment-method |
| runCORR | runCORR runCORR,SingleCellExperiment runCORR,SingleCellExperiment-method runCORR,SVPExperiment runCORR,SVPExperiment-method |
| Detecting the specific cell features with nearest distance of cells in MCA space | runDetectMarker runDetectMarker,SingleCellExperiment runDetectMarker,SingleCellExperiment-method |
| Detecting the spatially or single cell variable features with Moran's I or Geary's C | runDetectSVG runDetectSVG,SingleCellExperiment runDetectSVG,SingleCellExperiment-method runDetectSVG,SVPExperiment runDetectSVG,SVPExperiment-method |
| One hot encode for the specified cell category. | runENCODE runENCODE,SingleCellExperiment runENCODE,SingleCellExperiment-method |
| Global Bivariate analysis for spatial autocorrelation | runGLOBALBV runGLOBALBV,SingleCellExperiment runGLOBALBV,SingleCellExperiment-method runGLOBALBV,SVPExperiment runGLOBALBV,SVPExperiment-method |
| Detecting the spatially or single cell variable features with Kullback–Leibler divergence of 2D weighted kernel density estimation | runKldSVG runKldSVG,SingleCellExperiment runKldSVG,SingleCellExperiment-method runKldSVG,SVPExperiment runKldSVG,SVPExperiment-method |
| Local indicators of spatial association analysis | runLISA runLISA,SingleCellExperiment runLISA,SingleCellExperiment-method runLISA,SVPExperiment runLISA,SVPExperiment-method |
| Local Bivariate analysis with spatial autocorrelation | runLOCALBV runLOCALBV,SingleCellExperiment runLOCALBV,SingleCellExperiment-method runLOCALBV,SVPExperiment runLOCALBV,SVPExperiment-method |
| Run Multiple Correspondence Analysis | runMCA runMCA,SingleCellExperiment runMCA,SingleCellExperiment-method |
| Calculate the activity of gene sets in spatial or single-cell data with restart walk with restart and hyper test weighted. | runSGSA runSGSA,SingleCellExperiment runSGSA,SingleCellExperiment-method |
| Calculating the 2D Weighted Kernel Density Estimation | runWKDE runWKDE,SingleCellExperiment runWKDE,SingleCellExperiment-method runWKDE,SVPExperiment runWKDE,SVPExperiment-method |
| spatial or single cell variable features matrix extract method | svDf svDf,SingleCellExperiment,character-method svDf,SingleCellExperiment,missing-method svDf,SingleCellExperiment,numeric-method svDf<- svDf<-,SingleCellExperiment,character-method svDf<-,SingleCellExperiment,missing-method svDf<-,SingleCellExperiment,numeric-method svDfNames svDfNames,SingleCellExperiment-method svDfNames<- svDfNames<-,SingleCellExperiment,character-method svDfs svDfs,SingleCellExperiment-method svDfs<- svDfs<-,SingleCellExperiment-method |
| Some accessor functions to get the internal slots of SVPExperiment | imgData,SVPExperiment-method imgData<-,SVPExperiment,DataFrame-method imgData<-,SVPExperiment,NULL-method show,SVPExperiment-method spatialCoords,SVPExperiment-method spatialCoords<-,SVPExperiment spatialCoords<-,SVPExperiment,matrix_Or_NULL-method spatialCoordsNames,SVPExperiment-method spatialCoordsNames<-,SVPExperiment,character-method SVP-accessors |
| The SVPExperiment class | coerce,SingleCellExperiment,SVPExperiment-method SVPExperiment SVPExperiment-class |
