Package: scGPS 1.21.0

Quan Nguyen

scGPS: A complete analysis of single cell subpopulations, from identifying subpopulations to analysing their relationship (scGPS = single cell Global Predictions of Subpopulation)

The package implements two main algorithms to answer two key questions: a SCORE (Stable Clustering at Optimal REsolution) to find subpopulations, followed by scGPS to investigate the relationships between subpopulations.

Authors:Quan Nguyen [aut, cre], Michael Thompson [aut], Anne Senabouth [aut]

scGPS_1.21.0.tar.gz
scGPS_1.21.0.zip(r-4.5)scGPS_1.21.0.zip(r-4.4)scGPS_1.21.0.zip(r-4.3)
scGPS_1.21.0.tgz(r-4.4-x86_64)scGPS_1.21.0.tgz(r-4.4-arm64)scGPS_1.21.0.tgz(r-4.3-x86_64)scGPS_1.21.0.tgz(r-4.3-arm64)
scGPS_1.21.0.tar.gz(r-4.5-noble)scGPS_1.21.0.tar.gz(r-4.4-noble)
scGPS_1.21.0.tgz(r-4.4-emscripten)scGPS_1.21.0.tgz(r-4.3-emscripten)
scGPS.pdf |scGPS.html✨
scGPS/json (API)
NEWS

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

Peer review:

Bug tracker:https://github.com/imb-computational-genomics-lab/scgps/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
Datasets:

On BioConductor:scGPS-1.21.0(bioc 3.21)scGPS-1.20.0(bioc 3.20)

singlecellclusteringdataimportsequencingcoverageopenblascpp

5.20 score 4 stars 7 scripts 201 downloads 2 mentions 38 exports 116 dependencies

Last updated 2 months agofrom:03c5e1764d. Checks:OK: 1 NOTE: 8. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 30 2024
R-4.5-win-x86_64NOTENov 30 2024
R-4.5-linux-x86_64NOTENov 30 2024
R-4.4-win-x86_64NOTENov 30 2024
R-4.4-mac-x86_64NOTENov 30 2024
R-4.4-mac-aarch64NOTENov 30 2024
R-4.3-win-x86_64NOTENov 30 2024
R-4.3-mac-x86_64NOTENov 30 2024
R-4.3-mac-aarch64NOTENov 30 2024

Exports:annotate_clustersbootstrap_parallelbootstrap_predictioncalcDistcalcDistArmaclusteringclustering_baggingCORE_baggingCORE_clusteringCORE_subclusterdistvecfind_markersfind_optimal_stabilityfind_stabilitymean_cppnew_scGPS_objectnew_summarized_scGPS_objectPCAplot_COREplot_optimal_COREplot_reducedpredictingPrinComp_cpprand_indexrcpp_Eucl_distance_NotParrcpp_parallel_distancereformat_LASSOsub_clusteringsubset_cppsummary_accuracysummary_deviancesummary_prediction_lassosummary_prediction_ldatop_vartp_cpptrainingtSNEvar_cpp

Dependencies:abindaskpassBHBiobaseBiocGenericsBiocParallelcaretclasscliclockcodetoolscolorspacecpp11crayoncurldata.tableDelayedArrayDESeq2diagramdigestdplyrdynamicTreeCute1071fansifarverfastclusterforeachformatRfutile.loggerfutile.optionsfuturefuture.applygenericsGenomeInfoDbGenomeInfoDbDataGenomicRangesggplot2glmnetglobalsgluegowergtablehardhathttripredIRangesisobanditeratorsjsonliteKernSmoothlabelinglambda.rlatticelavalifecyclelistenvlocfitlubridatemagrittrMASSMatrixMatrixGenericsmatrixStatsmgcvmimeModelMetricsmunsellnlmennetnumDerivopensslparallellypillarpkgconfigplyrpROCprodlimprogressrproxypurrrR6RColorBrewerRcppRcppArmadilloRcppEigenRcppParallelrecipesreshape2rlangrpartS4ArraysS4VectorsscalesshapeSingleCellExperimentsnowSparseArraySQUAREMstringistringrSummarizedExperimentsurvivalsystibbletidyrtidyselecttimechangetimeDatetzdbUCSC.utilsutf8vctrsviridisLitewithrXVectorzlibbioc

scGPS introduction

Quan Nguyen and Michael Thompson

Rendered fromvignette.Rmdusingknitr::rmarkdownon Nov 30 2024.

Last update: 2020-12-02
Started: 2017-11-30

Readme and manuals

Help Manual

Help pageTopics
add_importadd_import
annotate_clusters functionally annotates the identified clustersannotate_clusters
BootStrap runs for both scGPS training and prediction with parallel optionbootstrap_parallel
BootStrap runs for both scGPS training and predictionbootstrap_prediction
Compute Euclidean distance matrix by rowscalcDist
Compute Euclidean distance matrix by rowscalcDistArma
HC clustering for a number of resolutionsclustering
HC clustering for a number of resolutionsclustering_bagging
Main clustering SCORE (CORE V2.0) Stable Clustering at Optimal REsolution with bagging and bootstrappingCORE_bagging
Main clustering CORE V2.0 updatedCORE_clustering
sub_clustering (optional) after running CORE 'test'CORE_subcluster
One of the two example single-cell count matrices to be used for training 'scGPS' modelday_2_cardio_cell_sample
One of the two example single-cell count matrices to be used for 'scGPS' predictionday_5_cardio_cell_sample
Compute Distance between two vectorsdistvec
find marker genesfind_markers
Find the optimal clusterfind_optimal_stability
Calculate stability indexfind_stability
Calculate meanmean_cpp
new_scGPS_objectnew_scGPS_object
new_summarized_scGPS_objectnew_summarized_scGPS_object
PCAPCA
Plot dendrogram tree for CORE resultplot_CORE
plot one single tree with the optimal clustering resultplot_optimal_CORE
plot reduced dataplot_reduced
Main prediction function applying the optimal ElasticNet and LDA modelspredicting
Principal component analysisPrinComp_cpp
Calculate rand indexrand_index
Function to calculate Eucledean distance matrix without parallelisationrcpp_Eucl_distance_NotPar
distance matrix using C++rcpp_parallel_distance
summarise bootstrap runs for Lasso model, from 'n' bootstrapsreformat_LASSO
sub_clustering for selected cellssub_clustering
Subset a matrixsubset_cpp
get percent accuracy for Lasso model, from 'n' bootstrapssummary_accuracy
get percent deviance explained for Lasso model, from 'n' bootstrapssummary_deviance
get percent deviance explained for Lasso model, from 'n' bootstrapssummary_prediction_lasso
get percent deviance explained for LDA model, from 'n' bootstrapssummary_prediction_lda
select top variable genestop_var
Transpose a matrixtp_cpp
Main model training function for finding the best model that characterises a subpopulationtraining
Input gene list for training 'scGPS', e.g. differentially expressed genestraining_gene_sample
tSNEtSNE
Calculate variancevar_cpp