library(dplyr) library(shiny) library(shinydashboard)
library(Seurat) library(cerebroApp)
Group
source('R/class-Group.R')
Group$new( name = 'sample', levels = levels(cerebro_seurat$meta_data$sample), description = 'experimental samples', colors = c('black','red','green') )
Group$new( name = 'sample', levels = levels(cerebro_seurat$meta_data$sample), description = 'experimental samples', colors = c('black','red') )
GeneList
source('R/class-GeneList.R')
GeneList$new( name = 'test', genes = c('a','b','c'), description = 'genes to assign cell cycle' )
GeneList$new( name = 'test', genes = 1, description = 'genes to assign cell cycle' )
GeneList$new( name = 'test', genes = c('a','b') )
MetaData
source('R/class-MetaData.R')
table <- Table$new( name = 'test', table = cerebro_seurat$marker_genes$cerebro_seurat$sample, description = 'this is a description' ) table
Table
source('R/class-Table.R')
table <- Table$new( name = 'test', table = cerebro_seurat$marker_genes$cerebro_seurat$sample, description = 'this is a description' ) table
Projection
source('R/class-Projection.R')
projection <- Projection$new( name = 'test', dimensions = 2, coordinates = cerebro_seurat$projections$UMAP, description = 'this is a description' ) projection
Tree
source('R/class-Tree.R')
tree <- Tree$new( group = 'sample', tree = cerebro_seurat$trees$sample, description = 'this tree shows the relationship between samples' ) tree
Monocle_v2
pbmc_Seurat <- readRDS('~/Dropbox/GSE_stuff/data/seurat.rds')
pbmc_Seurat <- RunUMAP( pbmc_Seurat, reduction.name = 'UMAP_3D', reduction.key = 'UMAP3D_', dims = 1:30, n.components = 3, seed.use = 100, verbose = FALSE )
Idents(pbmc_Seurat) <- "sample" pbmc_Seurat <- BuildClusterTree( pbmc_Seurat, dims = 1:30, reorder = FALSE, reorder.numeric = FALSE, verbose = FALSE ) pbmc_Seurat@misc$trees$sample <- pbmc_Seurat@tools$BuildClusterTree Idents(pbmc_Seurat) <- "seurat_clusters" pbmc_Seurat <- BuildClusterTree( pbmc_Seurat, dims = 1:30, reorder = FALSE, reorder.numeric = FALSE, verbose = FALSE ) pbmc_Seurat@misc$trees$seurat_clusters <- pbmc_Seurat@tools$BuildClusterTree Idents(pbmc_Seurat) <- "cell_type_singler_blueprintencode_main" pbmc_Seurat <- BuildClusterTree( pbmc_Seurat, dims = 1:30, reorder = FALSE, reorder.numeric = FALSE, verbose = FALSE ) pbmc_Seurat@misc$trees$cell_type_singler_blueprintencode_main <- pbmc_Seurat@tools$BuildClusterTree
source('~/Research/GitHub/cerebroApp_v1.3/R/getMostExpressedGenes.R')
pbmc_Seurat@misc$most_expressed_genes$by_sample <- NULL pbmc_Seurat@misc$most_expressed_genes$by_cluster <- NULL pbmc_Seurat <- getMostExpressedGenes( pbmc_Seurat, assay = 'RNA', groups = c('sample','seurat_clusters','cell_type_singler_blueprintencode_main') )
source('~/Research/GitHub/cerebroApp_v1.3/R/getMarkerGenes.R')
pbmc_Seurat@misc$marker_genes$by_sample <- NULL pbmc_Seurat@misc$marker_genes$by_cluster <- NULL pbmc_Seurat@misc$marker_genes$parameters <- NULL pbmc_Seurat <- getMarkerGenes( pbmc_Seurat, assay = 'RNA', organism = 'hg', groups = c('sample','seurat_clusters','cell_type_singler_blueprintencode_main'), name = 'cerebro_seurat', only_pos = TRUE, min_pct = 0.7, thresh_logFC = 0.25, thresh_p_val = 0.01, test = 'wilcox', verbose = TRUE )
source('~/Research/GitHub/cerebroApp_v1.3/R/send_enrichr_query.r') source('~/Research/GitHub/cerebroApp_v1.3/R/getEnrichedPathways.R')
pbmc_Seurat@misc$enriched_pathways$enrichr <- NULL pbmc_Seurat <- getEnrichedPathways( pbmc_Seurat, marker_genes_input = 'cerebro_seurat', adj_p_cutoff = 0.01, max_terms = 100 )
source('~/Research/GitHub/cerebroApp_v1.3/R/read_GMT_file.R') source('~/Research/GitHub/cerebroApp_v1.3/R/performGeneSetEnrichmentAnalysis.R')
pbmc_Seurat@misc$enriched_pathways$GSVA <- NULL example_gene_set <- system.file("extdata/example_gene_set.gmt", package = "cerebroApp") pbmc_Seurat <- performGeneSetEnrichmentAnalysis( pbmc_Seurat, assay = 'RNA', GMT_file = example_gene_set, groups = c('sample','seurat_clusters','cell_type_singler_blueprintencode_main') )
pbmc_Seurat@misc$technical_info$cerebroApp_version <- utils::packageVersion('cerebroApp') pbmc_Seurat@misc$technical_info$Seurat <- utils::packageVersion('Seurat')
source('~/Research/GitHub/cerebroApp_v1.3/R/extractMonocleTrajectory.R')
monocle <- readRDS('~/Dropbox/GSE_stuff/data/monocle.rds') pbmc_Seurat <- extractMonocleTrajectory(monocle, pbmc_Seurat, 'highly_variable_genes')
source('~/Research/GitHub/cerebroApp_v1.3/R/class-Cerebro_v1.3.R') source('~/Research/GitHub/cerebroApp_v1.3/R/exportFromSeurat.R') source('~/Research/GitHub/cerebroApp_v1.3/R/exportFromSCE.R')
dgCMatrix
exportFromSeurat( pbmc_Seurat, assay = 'SCT', slot = 'data', file = '~/Dropbox/Cerebro_development/pbmc_Seurat_dgCMatrix.crb', experiment_name = 'pbmc_Seurat', organism = 'hg', groups = c('sample','seurat_clusters','cell_type_singler_blueprintencode_main'), cell_cycle = c('cell_cycle_seurat'), nUMI = 'nCount_RNA', nGene = 'nFeature_RNA', add_all_meta_data = TRUE, use_delayed_array = FALSE, verbose = TRUE )
RleMatrix
exportFromSeurat( pbmc_Seurat, assay = 'SCT', slot = 'data', file = '~/Dropbox/Cerebro_development/pbmc_Seurat_RleMatrix.crb', experiment_name = 'pbmc_Seurat', organism = 'hg', groups = c('sample','seurat_clusters','cell_type_singler_blueprintencode_main'), cell_cycle = c('cell_cycle_seurat'), nUMI = 'nCount_RNA', nGene = 'nFeature_RNA', add_all_meta_data = TRUE, use_delayed_array = TRUE, verbose = TRUE )
cerebro_seurat <- readRDS('~/Dropbox/Cerebro_development/pbmc_Seurat_dgCMatrix.crb') cerebro_seurat # class: Cerebro_v1.3 # cerebroApp version: 1.3.0 # experiment name: pbmc_Seurat # organism: hg # date of analysis: 2020-02-19 # date of export: 2020-08-20 # number of cells: 5,697 # number of genes: 15,907 # grouping variables (3): sample, seurat_clusters, cell_type_singler_blueprintencode_main # cell cycle variables (1): cell_cycle_seurat # projections (2): UMAP, UMAP_3D # trees (3): sample, seurat_clusters, cell_type_singler_blueprintencode_main # most expressed genes: sample, seurat_clusters, cell_type_singler_blueprintencode_main # marker genes: # - cerebro_seurat (3): sample, seurat_clusters, cell_type_singler_blueprintencode_main, # - test (1): sample # enriched pathways: # - cerebro_seurat_enrichr (3): sample, seurat_clusters, cell_type_singler_blueprintencode_main, # - cerebro_GSVA (3): sample, seurat_clusters, cell_type_singler_blueprintencode_main # trajectories: # - monocle2 (1): highly_variable_genes
dgCMatrix
Cerebro.options <- list( "mode" = "open", "crb_file_to_load" = "~/Dropbox/Cerebro_development/pbmc_Seurat_dgCMatrix.crb", "cerebro_root" = "~/Research/GitHub/cerebroApp_v1.3/inst/" )
RleMatrix
Cerebro.options <- list( "mode" = "open", "crb_file_to_load" = "~/Dropbox/Cerebro_development/pbmc_Seurat_RleMatrix.crb", "cerebro_root" = "~/Research/GitHub/cerebroApp_v1.3/inst/" )
Cerebro.options <- list( "mode" = "open", "crb_file_to_load" = "~/Research/GitHub/cerebroApp_v1.3/inst/extdata/v1.3/example.crb", "cerebro_root" = "~/Research/GitHub/cerebroApp_v1.3/inst" )
Cerebro.options <- list( "mode" = "open", "cerebro_root" = "~/Research/GitHub/cerebroApp_v1.3/inst" )
options(shiny.maxRequestSize = 800 * 1024^2) source("~/Research/GitHub/cerebroApp_v1.3/inst/shiny/v1.3/shiny_UI.R") source("~/Research/GitHub/cerebroApp_v1.3/inst/shiny/v1.3/shiny_server.R") shiny::shinyApp(ui = ui, server = server)
options(shiny.maxRequestSize = 800 * 1024^2) source("~/Research/GitHub/cerebroApp_v1.3/inst/shiny/v1.3/shiny_UI.R") source("~/Research/GitHub/cerebroApp_v1.3/inst/shiny/v1.3/shiny_server.R") profvis::profvis(shiny::runApp(shiny::shinyApp(ui = ui, server = server)))
library(reactlog) reactlog_enable() options(shiny.maxRequestSize = 800 * 1024^2) source("~/Research/GitHub/cerebroApp_v1.3/inst/shiny/v1.3/shiny_UI.R") source("~/Research/GitHub/cerebroApp_v1.3/inst/shiny/v1.3/shiny_server.R") shiny::shinyApp(ui = ui, server = server)
shiny::reactlogShow()
cerebroApp
devtools::document() devtools::document() devtools::install_local( '.', force = TRUE, upgrade = FALSE, build_vignettes = FALSE ) cerebroApp::launchCerebro( version = "v1.3", crb_file_to_load = "~/Dropbox/Cerebro_development/pbmc_Seurat_dgCMatrix.crb" )
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