Package: jazzPanda 1.5.0

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]

jazzPanda_1.5.0.tar.gz
jazzPanda_1.5.0.zip(r-4.7)jazzPanda_1.5.0.zip(r-4.6)jazzPanda_1.5.0.zip(r-4.5)
jazzPanda_1.5.0.tgz(r-4.6-any)jazzPanda_1.5.0.tgz(r-4.5-any)
jazzPanda_1.5.0.tar.gz(r-4.7-any)jazzPanda_1.5.0.tar.gz(r-4.6-any)
jazzPanda_1.5.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
jazzPanda/json (API)

# 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/docs 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-1.5.0(bioc 3.24)jazzPanda-1.4.0(bioc 3.23)

spatial geneexpression differentialexpression statisticalmethod transcriptomics correlation linear-models marker-genes spatial-transcriptomics

5.38 score 4 stars 3 scripts 9 exports 125 dependencies

Last updated from:91c746f417. Checks:8 NOTE, 2 OK. Indexed: yes.

Target	Result	Time
bioc-checks	NOTE	321
linux-devel-x86_64	NOTE	773
source / vignettes	OK	526
linux-release-x86_64	NOTE	620
macos-release-arm64	NOTE	311
macos-oldrel-arm64	NOTE	276
windows-devel	NOTE	548
windows-release	NOTE	498
windows-oldrel	NOTE	543
wasm-release	OK	315

Exports:compute_permp create_genesets get_cor get_full_mg get_perm_adjp get_perm_p get_top_mg get_vectors lasso_markers

Dependencies:abind askpass BH Biobase BiocFileCache BiocGenerics BiocParallel bit bit64 blob BumpyMatrix cachem caret class cli clock codetools cpp11 curl data.table DBI dbplyr DelayedArray deldir diagram digest doParallel dplyr e1071 farver fastmap filelock foreach formatR futile.logger futile.options future future.apply generics GenomicRanges ggplot2 glmnet globals glue gower gtable hardhat httr2 ipred IRanges isoband iterators KernSmooth labeling lambda.r lattice lava lifecycle listenv lubridate magick magrittr MASS Matrix MatrixGenerics matrixStats memoise ModelMetrics nlme nnet numDeriv openssl parallelly pillar pkgconfig plyr polyclip pROC prodlim progressr proxy purrr R6 rappdirs RColorBrewer Rcpp RcppEigen recipes reshape2 rjson rlang rpart RSQLite S4Arrays S4Vectors S7 scales Seqinfo shape SingleCellExperiment snow SparseArray sparsevctrs SpatialExperiment spatstat.data spatstat.geom spatstat.univar spatstat.utils SQUAREM stringi stringr SummarizedExperiment survival sys tibble tidyr tidyselect timechange timeDate tzdb utf8 vctrs viridisLite withr XVector

jazzPanda example

Rendered fromjazzPanda.Rmdusingknitr::rmarkdown

Last update: 2025-08-21
Started: 2024-07-27

Help page	Topics
jazzPanda: A hybrid approach to find spatially relevant marker genes in image-based spatial transcriptomics data	jazzPanda-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_result	get_cor
Get full lasso result from glm_mg_result	get_full_mg
Get permutation adjusted p value from cor_mg_result	get_perm_adjp
Get permutation p value from cor_mg_result	get_perm_p
Get top lasso result from glm_mg_result	get_top_mg
Vectorise the spatial coordinates	get_vectors
Find marker genes with spatial coordinates	lasso_markers
Rep1 selected cells	rep1_clusters
Rep1 negative control genes within the selected region.	rep1_neg
A small section of Xenium human breast cancer rep1.	rep1_sub
Rep2 selected cells	rep2_clusters
Rep2 negative control genes within the selected region.	rep2_neg
A small section of Xenium human breast cancer rep2.	rep2_sub

Package: jazzPanda 1.5.0

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

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