In romanhaa/cerebroApp: Interactive visualization of scRNA-seq data with Cerebro
Prepararation
Load packages
library(dplyr)
library(shiny)
library(shinydashboard)
library(Seurat)
library(cerebroApp)
Classes
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$sample,
description = 'this tree shows the relationship between samples'
)
tree
Monocle_v2
Load data
pbmc_Seurat <- readRDS('~/Dropbox/GSE_stuff/data/seurat.rds')
Process data
Dimensional reduction
pbmc_Seurat <- RunUMAP(
pbmc_Seurat,
reduction.name = 'UMAP_3D',
reduction.key = 'UMAP3D_',
dims = 1:30,
n.components = 3,
seed.use = 100,
verbose = FALSE
)
Trees
Idents(pbmc_Seurat) <- "sample"
pbmc_Seurat <- BuildClusterTree(
pbmc_Seurat,
dims = 1:30,
reorder = FALSE,
reorder.numeric = FALSE,
verbose = FALSE
)
pbmc_Seurat@misc$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
Most expressed genes
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')
)
Marker genes
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
)
Enriched pathways
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
)
Gene set enrichment
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')
)
Meta data
pbmc_Seurat@misc$technical_info$cerebroApp_version <- utils::packageVersion('cerebroApp')
pbmc_Seurat@misc$technical_info$Seurat <- utils::packageVersion('Seurat')
Trajectory
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')
Export data
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
)
Load data to check
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
Launch Cerebro
Set options
Pre-load with 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/"
)
Pre-load with 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/"
)
Pre-load with example data set
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"
)
No pre-loading
Cerebro.options <- list(
"mode" = "open",
"cerebro_root" = "~/Research/GitHub/cerebroApp_v1.3/inst"
)
Start Cerebro
Without profvis
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)
With provfis
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)))
With reactlog
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()
Install 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"
)
romanhaa/cerebroApp documentation built on Nov. 25, 2021, 5:29 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Prepararation
Load packages
library(dplyr) library(shiny) library(shinydashboard)
library(Seurat) library(cerebroApp)
Classes
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$sample, description = 'this tree shows the relationship between samples' ) tree
Monocle_v2
Load data
pbmc_Seurat <- readRDS('~/Dropbox/GSE_stuff/data/seurat.rds')
Process data
Dimensional reduction
pbmc_Seurat <- RunUMAP( pbmc_Seurat, reduction.name = 'UMAP_3D', reduction.key = 'UMAP3D_', dims = 1:30, n.components = 3, seed.use = 100, verbose = FALSE )
Trees
Idents(pbmc_Seurat) <- "sample" pbmc_Seurat <- BuildClusterTree( pbmc_Seurat, dims = 1:30, reorder = FALSE, reorder.numeric = FALSE, verbose = FALSE ) pbmc_Seurat@misc$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
Most expressed genes
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') )
Marker genes
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 )
Enriched pathways
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 )
Gene set enrichment
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') )
Meta data
pbmc_Seurat@misc$technical_info$cerebroApp_version <- utils::packageVersion('cerebroApp') pbmc_Seurat@misc$technical_info$Seurat <- utils::packageVersion('Seurat')
Trajectory
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')
Export data
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 )
Load data to check
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
Launch Cerebro
Set options
Pre-load with 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/" )
Pre-load with 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/" )
Pre-load with example data set
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" )
No pre-loading
Cerebro.options <- list( "mode" = "open", "cerebro_root" = "~/Research/GitHub/cerebroApp_v1.3/inst" )
Start Cerebro
Without profvis
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)
With provfis
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)))
With reactlog
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()
Install 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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