Package: singIST 1.0.0

Aitor Moruno-Cuenca

singIST: comparative single-cell transcriptomics between disease models and a human condition

Provides with toolkits to implement a full singIST analysis with pseudobulked Seurat objects of disease models and human data.

Authors:Aitor Moruno-Cuenca [aut, cre], Dr. Sergio Picart-Armada [rev], Dr. Alexandre Perera-Lluna [ths], Dr. Francesc Fernández-Albert [ths]

singIST_1.0.0.tar.gz
singIST_1.0.0.zip(r-4.7)singIST_1.0.0.zip(r-4.6)singIST_1.0.0.zip(r-4.5)
singIST_1.0.0.tgz(r-4.6-any)singIST_1.0.0.tgz(r-4.5-any)
singIST_1.0.0.tar.gz(r-4.7-any)singIST_1.0.0.tar.gz(r-4.6-any)
singIST_1.0.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
singIST/json (API)
NEWS

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

Bug tracker:https://github.com/datasciencerd-almirall/singist/issues

On BioConductor:singIST-1.1.0(bioc 3.24)singIST-1.0.0(bioc 3.23)

singlecellclassificationtranscriptomics

3.04 score 63 downloads 74 exports 270 dependencies

Last updated from:b25b58e2ec (on RELEASE_3_23). Checks:1 NOTE, 6 ERROR, 2 WARNING, 1 OK. Indexed: no.

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Exports:asmbPLSDA.cv.kcvasmbPLSDA.cv.loobiological_link_functioncalculate_pvaluescelltype_mappingcelltype_recapcenter_scalecheck_hyperparameterscheck_mapping_organismcheck_pathwaycheck_superpathwaycheck_superpathway_inputCIP_GIPCIP_GIP_testclean_mfa_datacompute_final_measurescompute_IC95compute_permutation_statscompute_pvaluecompute_validation_metricscreate_fit_modelcreate_hyperparameterscreate_mapping_organismcreate_pathwaycreate_superpathwaycreate_superpathway_inputdeflate_predictionderive_contributionsderive_scoresdetect_gene_typediff_expressedevaluate_performanceevaluate_quantile_combinationsexecute_parallel_cvexecute_sequential_cvFCtoExpressionfit_mfa_imputerfit_permuted_modelfitOptimalgene_contribgenerate_null_distributionsget_indicesget_measure_indexget_train_val_setsinitialize_resultsjackknife_CIP_GIPmatrixToBlockmultiple_checkmultiple_fitOptimalmultiple_singISTrecapitulationsorthology_mappingperform_cvperformance_measurespermut_asmbplsdapermut_asmbplsda_kcvpermute_X_matrixpermute_Y_matrixpredict_mfa_imputerpullGeneSetquantile_computationrender_multiple_outputsrestore_removed_columnsResults_comparison_measureretrieve_one2one_orthologsselect_optimal_PLSselect_samplessetGeneSetsCelltypesingIST_treatsingISTrecapitulationssubsampling_CIP_GIPsuperpathway_recapupdate_blockupdate_group_sizeswilcox_CIP_GIP

Dependencies:abindannotateAnnotationDbiAnnotationHubaskpassasmbPLSassortheadbackportsbase64encbeachmatBHBiobaseBiocBaseUtilsBiocFileCacheBiocGenericsBiocManagerBiocNeighborsBiocParallelBiocSingularBiocVersionbiomaRtBiostringsbitbit64bitopsblobblusterbootbroombslibcachemcarcarDatacaToolscheckmateclicliprclustercodetoolscolorspacecommonmarkcorrplotcowplotcpp11crayoncrosstalkcurldata.tableDBIdbplyrDelayedArraydeldirDerivdigestdoBydoParalleldotCall64dplyrdqrngDTedgeRellipseemmeansestimabilityevaluateExperimentHubFactoMineRfarverfastDummiesfastmapfilelockfitdistrplusflashClustFNNfontawesomeforcatsforeachforecastformatRFormulafracdifffsfutile.loggerfutile.optionsfuturefuture.applygenericsGenomicRangesggplot2ggpubrggrepelggridgesggsciggsignifglmnetglobalsgluegoftestgplotsgraphgridExtraGSEABasegtablegtoolshavenherehighrhmshtmltoolshtmlwidgetshttpuvhttrhttr2icaigraphIRangesirlbaisobanditeratorsjomojquerylibjsonliteKEGGRESTKernSmoothknitrlabelinglambda.rlaterlatticelazyevalleapslifecyclelimmalistenvlme4lmtestlocfitmagrittrMASSMatrixMatrixGenericsMatrixModelsmatrixStatsmemoisemetapodmgcvmicemicrobenchmarkmimeminiUIminqamissMDAmitmlmodelrmsigdbmultcompViewmvtnormnlmenloptrnnetnumDerivopensslordinalorg.Hs.eg.dborg.Mm.eg.dbotelpanparallellypatchworkpbapplypbkrtestpillarpkgconfigplotlyplyrpngpolyclippolynomprettyunitsprogressprogressrpromisespurrrquantregR6RANNrappdirsrbibutilsRColorBrewerRcppRcppAnnoyRcppArmadilloRcppEigenRcppHNSWRcppProgressRcppTOMLRdpackreadrreformulasreshape2reticulaterlangrmarkdownROCRrpartrprojrootRSpectraRSQLiterstatixrsvdRtsneS4ArraysS4VectorsS7sassScaledMatrixscalesscattermorescatterplot3dscransctransformscuttleSeqinfoSeuratSeuratObjectshapeshinySingleCellExperimentsitmosnowsourcetoolsspspamSparseArraySparseMspatstat.dataspatstat.explorespatstat.geomspatstat.randomspatstat.sparsespatstat.univarspatstat.utilsstatmodstringistringrSummarizedExperimentsurvivalsystensortibbletidyrtidyselecttimeDatetinytextzdbucminfurcautf8uwotvctrsviridisLitevroomwithrxfunXMLxml2xtableXVectoryamlzoo

Readme and manuals

Help Manual

Help pageTopics
Ensure all celltype–sample combinations are present in the pseudobulkmatrixadd_missing_psb_rows
K‐fold × Repeated Cross‐Validation for asmbPLS-DAasmbPLSDA.cv.kcv
Leave-one-out Cross-validationasmbPLSDA.cv.loo
Biological link functionbiological_link_function
Cell type mappingcelltype_mapping
Derive cell type recapitulationcelltype_recap
Validate superpathway fit modelcheck_fit_model
Validate asmbPLS-DA hyperparameterscheck_hyperparameters
Validate mapping organism inputcheck_mapping_organism
Validate pathway fieldscheck_pathway
Validate superpathway gene setscheck_superpathway
Check superpathway input for asmbPLS-DAcheck_superpathway_input
Compute Cell Importance Projection (CIP) and Gene Importance Projection (GIP)CIP_GIP
Cell and Gene Importance Projections statistical significanceCIP_GIP_test
Clean a predictor matrix for multiblock MFAclean_mfa_data
Create superpathway fit model objectcreate_fit_model
Create asmbPLS-DA hyperparameters objectcreate_hyperparameters
Create mapping organism objectcreate_mapping_organism
Create pathway objectcreate_pathway
Create superpathway gene sets objectcreate_superpathway
Create superpathway input for asmbPLS-DAcreate_superpathway_input
Derive superpathway score, cell type contribution and gene contributionderive_contributions
Compute predictor scoresderive_scores
Compute differentially expressed genes with FindMarkers/findMarkers and descriptive point estimates of log2FCdiff_expressed
Fit a multiblock‐MFA imputer on training datafit_mfa_imputer
Cross validation and fit of asmbPLSDAfitOptimal
Derive gene contribution to cell type recapitulationgene_contrib
Update block of predictor matrices in matrixToBlock()calculate_pvalues center_scale compute_final_measures compute_IC95 compute_permutation_stats compute_pvalue compute_validation_metrics deflate_prediction detect_gene_type evaluate_performance evaluate_quantile_combinations execute_parallel_cv execute_sequential_cv FCtoExpression fit_permuted_model generate_null_distributions get_indices get_measure_index get_train_val_sets helpers initialize_results jackknife_CIP_GIP performance_measures perform_cv permute_X_matrix permute_Y_matrix quantile_computation retrieve_one2one_orthologs select_optimal_PLS select_samples subsampling_CIP_GIP update_block
Build predictor and response blocks with superpathway inputmatrixToBlock
Check if parameter format is consistentmultiple_check
Multiple Cross validation and fit of asmbPLSDAmultiple_fitOptimal
Compute singIST recapitulations for multiple superpathwaysmultiple_singISTrecapitulations
Orthology mappingorthology_mapping
Permutation test for asmbPLSDA global significance for LOOpermut_asmbplsda
Permutation test for asmbPLS-DA global significance (LOO or KCV)permut_asmbplsda_kcv
Impute new samples using a fitted MFA imputerpredict_mfa_imputer
Pull Gene Set from MsigDBpullGeneSet
Render multiple singISTrecapitulation outputsrender_multiple_outputs
Restore columns removed during MFA cleaning into the imputed matrixrestore_removed_columns
Compute performance metrics of predicted asmbPLSDAResults_comparison_measure
Set gene sets per cell type in a superpathwaysetGeneSetsCelltype
Derive singIST treated samplessingIST_treat
Compute singIST recapitulationssingISTrecapitulations
Derive superpathway recapitulationsuperpathway_recap
Update block‐size vector after cleaningupdate_group_sizes
Mann-Whitney Wilcoxon test p-valuewilcox_CIP_GIP