Package: jazzPanda 0.99.6

Melody Jin

jazzPanda: Finding spatially relevant marker genes in image based spatial transcriptomics data

This package contains the function to find marker genes for image-based spatial transcriptomics data. There are functions to create spatial vectors from the cell and transcript coordiantes, which are passed as inputs to find marker genes. Marker genes are detected for every cluster by two approaches. The first approach is by permtuation testing, which is implmented in parallel for finding marker genes for one sample study. The other approach is to build a linear model for every gene. This approach can account for multiple samples and backgound noise.

Authors:Melody Jin [aut, cre]

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

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

Bug tracker:https://github.com/phipsonlab/jazzpanda/issues

Pkgdown site:https://bhuvad.github.io

Datasets:
  • rep1_clusters - Rep1 selected cells
  • rep1_neg - Rep1 negative control genes within the selected region.
  • rep1_sub - A small section of Xenium human breast cancer rep1.
  • rep2_clusters - Rep2 selected cells
  • rep2_neg - Rep2 negative control genes within the selected region.
  • rep2_sub - A small section of Xenium human breast cancer rep2.

On BioConductor:jazzPanda-0.99.6(bioc 3.21)

spatialgeneexpressiondifferentialexpressionstatisticalmethodtranscriptomicscorrelationlinear-modelsmarker-genesspatial-transcriptomics

5.00 score 2 stars 9 exports 133 dependencies

Last updated 12 days agofrom:7b643fe34b. Checks:1 OK, 6 NOTE. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 12 2025
R-4.5-winNOTEMar 12 2025
R-4.5-macNOTEMar 12 2025
R-4.5-linuxNOTEMar 12 2025
R-4.4-winNOTEMar 12 2025
R-4.4-macNOTEMar 12 2025
R-4.4-linuxNOTEMar 12 2025

Exports:compute_permpcreate_genesetsget_corget_full_mgget_perm_adjpget_perm_pget_top_mgget_vectorslasso_markers

Dependencies:abindaskpassBHBiobaseBiocFileCacheBiocGenericsBiocParallelbitbit64blobBumpyMatrixcachemcaretclasscliclockcodetoolscolorspacecpp11crayoncurldata.tableDBIdbplyrDelayedArraydeldirdiagramdigestdoParalleldplyre1071fansifarverfastmapfilelockforeachformatRfutile.loggerfutile.optionsfuturefuture.applygenericsGenomeInfoDbGenomeInfoDbDataGenomicRangesggplot2glmnetglobalsgluegowergtablehardhathttripredIRangesisobanditeratorsjsonliteKernSmoothlabelinglambda.rlatticelavalifecyclelistenvlubridatemagickmagrittrMASSMatrixMatrixGenericsmatrixStatsmemoisemgcvmimeModelMetricsmunsellnlmennetnumDerivopensslparallellypillarpkgconfigplogrplyrpolyclippROCprodlimprogressrproxypurrrR6RColorBrewerRcppRcppEigenrecipesreshape2rjsonrlangrpartRSQLiteS4ArraysS4VectorsscalesshapeSingleCellExperimentsnowSparseArraysparsevctrsSpatialExperimentspatstat.dataspatstat.geomspatstat.univarspatstat.utilsSQUAREMstringistringrSummarizedExperimentsurvivalsystibbletidyrtidyselecttimechangetimeDatetzdbUCSC.utilsutf8vctrsviridisLitewithrXVector

jazzPanda example

Rendered fromjazzPanda.Rmdusingknitr::rmarkdownon Mar 12 2025.

Last update: 2025-02-23
Started: 2024-07-27

Readme and manuals

Help Manual

Help pageTopics
jazzPanda: A hybrid approach to find spatially relevant marker genes in image-based spatial transcriptomics datajazzPanda-package jazzPanda
helper function to check the input of binning.check_binning
helper function to check the inputs passed to marker detection function.check_valid_input
helper function to check the names of gene/cluster/sample.check_valid_names
Compute observation statistic for permutation framework.compute_observation
Compute permutation statistics for permutation framework.compute_permutation
Convert SingleCellExperiment/SpatialExperiment/SpatialFeatureExperiment objects to list object for jazzPanda..convert_data
Create a marker gene result object for correlation approach.create_cor_mg_result
Create a marker gene result object for linear modelling approach.create_lm_mg_result
Create spatial vectors for clusters.get_cluster_vectors
Create spatial vectors for genes from count matrix and cell coordinates.get_gene_vectors_cm
Create spatial vectors for genes from transcript coordinates.get_gene_vectors_tr
help function to get lasso coefficient for every cluster for a given model.get_lasso_coef
Calculate a p-value for correlation with permutation.compute_permp
Convert the coordinates of set of genes into vectors.create_genesets
Get observed correlation cor_mg_resultget_cor
Get full lasso result from glm_mg_resultget_full_mg
Get permutation adjusted p value from cor_mg_resultget_perm_adjp
Get permutation p value from cor_mg_resultget_perm_p
Get top lasso result from glm_mg_resultget_top_mg
Vectorise the spatial coordinatesget_vectors
Find marker genes with spatial coordinateslasso_markers
Rep1 selected cellsrep1_clusters
Rep1 negative control genes within the selected region.rep1_neg
A small section of Xenium human breast cancer rep1.rep1_sub
Rep2 selected cellsrep2_clusters
Rep2 negative control genes within the selected region.rep2_neg
A small section of Xenium human breast cancer rep2.rep2_sub