In romanhaa/cerebroApp: Interactive visualization of scRNA-seq data with Cerebro

Scenarios to test

Cerebro modes: open and closed
Absence of most expressed genes.
Absence of marker genes.
Absence of enriched pathways.
Absence of trajectories.
Expression data is present in "normal" sparse matrix.

Test several cases of custom, missing or incomplete data

`NA` values in grouping variables

pbmc_Seurat_copy <- pbmc_Seurat
pbmc_Seurat_copy@meta.data$sample[sample(seq(nrow(pbmc_Seurat_copy@meta.data)), 1000, replace = FALSE)] <- NA
exportFromSeurat(
  pbmc_Seurat_copy,
  assay = 'SCT',
  slot = 'data',
  file = '~/Dropbox/Cerebro_development/pbmc_Seurat.crb',
  experiment_name = 'pbmc_Seurat',
  organism = 'hg',
  groups = c('sample','seurat_clusters','cell_type_singler_blueprintencode_main'),
  nUMI = 'nCount_RNA',
  nGene = 'nFeature_RNA',
  add_all_meta_data = TRUE,
  verbose = TRUE
)

No experiment info

pbmc_Seurat_copy <- pbmc_Seurat
pbmc_Seurat_copy@misc$experiment <- list()
exportFromSeurat(
  pbmc_Seurat_copy,
  assay = 'SCT',
  slot = 'data',
  file = '~/Dropbox/Cerebro_development/pbmc_Seurat.crb',
  experiment_name = 'pbmc_Seurat',
  organism = 'hg',
  groups = c('sample','seurat_clusters','cell_type_singler_blueprintencode_main'),
  nUMI = 'nCount_RNA',
  nGene = 'nFeature_RNA',
  add_all_meta_data = TRUE,
  verbose = FALSE
)

No cell cycle assignments

exportFromSeurat(
  pbmc_Seurat,
  assay = 'SCT',
  slot = 'data',
  file = '~/Dropbox/Cerebro_development/pbmc_Seurat.crb',
  experiment_name = 'pbmc_Seurat',
  organism = 'hg',
  groups = c('sample','seurat_clusters','cell_type_singler_blueprintencode_main'),
  nUMI = 'nCount_RNA',
  nGene = 'nFeature_RNA',
  add_all_meta_data = TRUE,
  verbose = FALSE
)

No phylogenetic trees

pbmc_Seurat_copy <- pbmc_Seurat
pbmc_Seurat_copy@misc$trees <- list()
exportFromSeurat(
  pbmc_Seurat_copy,
  assay = 'SCT',
  slot = 'data',
  file = '~/Dropbox/Cerebro_development/pbmc_Seurat.crb',
  experiment_name = 'pbmc_Seurat',
  organism = 'hg',
  groups = c('sample','seurat_clusters','cell_type_singler_blueprintencode_main'),
  nUMI = 'nCount_RNA',
  nGene = 'nFeature_RNA',
  add_all_meta_data = TRUE,
  verbose = FALSE
)

Most expressed genes

Data is missing

pbmc_Seurat_copy <- pbmc_Seurat
pbmc_Seurat_copy@misc$most_expressed_genes <- NULL
exportFromSeurat(
  pbmc_Seurat_copy,
  assay = 'SCT',
  slot = 'data',
  file = '~/Dropbox/Cerebro_development/pbmc_Seurat.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,
  verbose = FALSE
)

Marker genes

Data is missing

pbmc_Seurat_copy <- pbmc_Seurat
pbmc_Seurat_copy@misc$marker_genes <- NULL
exportFromSeurat(
  pbmc_Seurat_copy,
  assay = 'SCT',
  slot = 'data',
  file = '~/Dropbox/Cerebro_development/pbmc_Seurat.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,
  verbose = FALSE
)

No marker genes found

pbmc_Seurat_copy <- pbmc_Seurat
pbmc_Seurat_copy@misc$marker_genes$cerebro_seurat$sample <- "no_markers_found"
exportFromSeurat(
  pbmc_Seurat_copy,
  assay = 'SCT',
  slot = 'data',
  file = '~/Dropbox/Cerebro_development/pbmc_Seurat.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,
  verbose = FALSE
)

Custom table

pbmc_Seurat_copy <- pbmc_Seurat
pbmc_Seurat_copy@misc$marker_genes$test <- list(
  "test1" = tibble(
    a = "this",
    b = "is",
    c = "a",
    d = "test"
  )
)

exportFromSeurat(
  pbmc_Seurat_copy,
  assay = 'SCT',
  slot = 'data',
  file = '~/Dropbox/Cerebro_development/pbmc_Seurat.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,
  verbose = FALSE
)
# class: Cerebro_v1.3
# cerebroApp version: 1.3.0
# experiment name: pbmc_Seurat
# organism: hg
# date of analysis: 2020-02-19
# date of export: 2020-08-30
# 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): test1
# 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

Enriched pathways

Data is missing

pbmc_Seurat_copy <- pbmc_Seurat
pbmc_Seurat_copy@misc$enriched_pathways <- NULL
exportFromSeurat(
  pbmc_Seurat_copy,
  assay = 'SCT',
  slot = 'data',
  file = '~/Dropbox/Cerebro_development/pbmc_Seurat.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,
  verbose = FALSE
)

Enrichr: No marker genes found or no pathways enriched

pbmc_Seurat_copy <- pbmc_Seurat
pbmc_Seurat_copy@misc$enriched_pathways$cerebro_seurat_enrichr$sample <- "no_markers_found"
pbmc_Seurat_copy@misc$enriched_pathways$cerebro_seurat_enrichr_2 <- list(
  sample = "no_pathways_found"
)
exportFromSeurat(
  pbmc_Seurat_copy,
  assay = 'SCT',
  slot = 'data',
  file = '~/Dropbox/Cerebro_development/pbmc_Seurat.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,
  verbose = FALSE
)

GSVA: No gene sets enriched

pbmc_Seurat_copy <- pbmc_Seurat
pbmc_Seurat_copy@misc$enriched_pathways$cerebro_GSVA$sample <- "no_gene_sets_enriched"
exportFromSeurat(
  pbmc_Seurat_copy,
  assay = 'SCT',
  slot = 'data',
  file = '~/Dropbox/Cerebro_development/pbmc_Seurat.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,
  verbose = FALSE
)

Custom table

pbmc_Seurat_copy <- pbmc_Seurat

pbmc_Seurat_copy@misc$enriched_pathways$test <- list(
  "test2" = tibble(
    a = "this",
    b = "is",
    c = "a",
    d = "test"
  )
)

exportFromSeurat(
  pbmc_Seurat_copy,
  assay = 'SCT',
  slot = 'data',
  file = '~/Dropbox/Cerebro_development/pbmc_Seurat.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,
  verbose = FALSE
)
# class: Cerebro_v1.3
# cerebroApp version: 1.3.0
# experiment name: pbmc_Seurat
# organism: hg
# date of analysis: 2020-02-19
# date of export: 2020-08-30
# 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,
# 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,
#   - test (1): test2
# trajectories:
#   - monocle2 (1): highly_variable_genes

No trajectories

pbmc_Seurat_copy <- pbmc_Seurat

pbmc_Seurat_copy@misc$trajectories <- NULL

exportFromSeurat(
  pbmc_Seurat_copy,
  assay = 'SCT',
  slot = 'data',
  file = '~/Dropbox/Cerebro_development/pbmc_Seurat.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,
  verbose = FALSE
)

Extra material

Table

pbmc_Seurat_copy <- pbmc_Seurat

custom_table <- tibble(
  a = "this",
  b = "is",
  c = "a",
  d = "test"
)

pbmc_Seurat_copy@misc$extra_material$tables <- list(
  "test" = custom_table
)

exportFromSeurat(
  pbmc_Seurat_copy,
  assay = 'SCT',
  slot = 'data',
  file = '~/Dropbox/Cerebro_development/pbmc_Seurat.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,
  verbose = FALSE
)

Plot

library(ggplot2)

pbmc_Seurat_copy <- pbmc_Seurat

custom_plot <- ggplot(iris, aes(Sepal.Length, Sepal.Width, color = Species)) +
  geom_point()

pbmc_Seurat_copy@misc$extra_material$plots <- list(
  "iris" = custom_plot
)

exportFromSeurat(
  pbmc_Seurat_copy,
  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,
  verbose = FALSE
)

Format of expression data

`dgCMatrix`

cerebro_seurat <- readRDS('~/Dropbox/Cerebro_development/pbmc_Seurat.crb')
str(cerebro_seurat$expression)
cell_names <- colnames(cerebro_seurat$expression)
gene_names <- rownames(cerebro_seurat$expression)
cerebro_seurat$expression <- as(cerebro_seurat$expression, 'dgCMatrix')
str(cerebro_seurat$expression)
colnames(cerebro_seurat$expression) <- cell_names
rownames(cerebro_seurat$expression) <- gene_names
str(cerebro_seurat$expression)
saveRDS(cerebro_seurat, '~/Dropbox/Cerebro_development/pbmc_Seurat.crb')

romanhaa/cerebroApp documentation built on Nov. 25, 2021, 5:29 p.m.

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romanhaa/cerebroApp
Interactive visualization of scRNA-seq data with Cerebro

In romanhaa/cerebroApp: Interactive visualization of scRNA-seq data with Cerebro

Scenarios to test

Test several cases of custom, missing or incomplete data

`NA` values in grouping variables

No experiment info

No cell cycle assignments

No phylogenetic trees

Most expressed genes

Data is missing

Marker genes

Data is missing

No marker genes found

Custom table

Enriched pathways

Data is missing

Enrichr: No marker genes found or no pathways enriched

GSVA: No gene sets enriched

Custom table

No trajectories

Extra material

Table

Plot

Format of expression data

`dgCMatrix`

R Package Documentation

Browse R Packages

We want your feedback!

romanhaa/cerebroApp Interactive visualization of scRNA-seq data with Cerebro

In romanhaa/cerebroApp: Interactive visualization of scRNA-seq data with Cerebro

Scenarios to test

Test several cases of custom, missing or incomplete data

NA values in grouping variables

No experiment info

No cell cycle assignments

No phylogenetic trees

Most expressed genes

Data is missing

Marker genes

Data is missing

No marker genes found

Custom table

Enriched pathways

Data is missing

Enrichr: No marker genes found or no pathways enriched

GSVA: No gene sets enriched

Custom table

No trajectories

Extra material

Table

Plot

Format of expression data

dgCMatrix

R Package Documentation

Browse R Packages

We want your feedback!

romanhaa/cerebroApp
Interactive visualization of scRNA-seq data with Cerebro

`NA` values in grouping variables

`dgCMatrix`