Package 'carnation'

Description

Add metadata to counts data frame

Usage

add_metadata(df, coldata, exclude.intgroups)

Arguments

Value

counts data frame with added metadata

Examples

library(DESeq2)

# make example DESeq data set
dds <- makeExampleDESeqDataSet()

# extract counts and metadata
df <- assay(dds)
coldata <- colData(dds)

# get gene counts df
counts_df <- get_gene_counts(dds, paste0('gene', seq_len(10)))

# add metadata
counts_df <- add_metadata(counts_df, coldata, exclude.intgroups=NULL)

Description

This function adds a column denoting set number to a matrix generated for an upset plot with fromList.with.names()

Usage

add.set.column(df)

Arguments

Value

data.frame with added set column

Examples

# list of genes
lst <- list(group1 = c(a = "gene1", b = "gene2", c = "gene3", d = "gene4"),
            group2 = c(c = "gene3", d = "gene4"))

# binarized matrix with group membership
df <- fromList.with.names(lst)

# matrix with added set column
ldf <- add.set.column(df)

Description

UI & module to generate alluvial plots.

Usage

alluvialUI(id, panel)

alluvialServer(id, obj, res_obj, config)

Arguments

Value

UI returns tagList with plot UI server invisibly returns NULL (used for side effects)

Examples

library(shiny)

# get DESeqResults object
data(res_dex, package='carnation')

# get enrichResult object
data(eres_dex, package='carnation')

# convert to GeneTonic object

gt <- GeneTonic::shake_enrichResult(eres_dex)

obj <- reactive({
  list(l_gs = gt$l_gs,
       anno_df = gt$anno_df,
       label = 'comp1')
})

res_obj <- reactive({ res })

config <- reactiveVal(get_config())

# run simple shiny app with plot
if(interactive()){
  shinyApp(
    ui = fluidPage(
           sidebarPanel(alluvialUI('p', 'sidebar')),
           mainPanel(alluvialUI('p', 'main'))
         ),
    server = function(input, output, session){
               alluvialServer('p', obj, res_obj, config)
             }
  )
}

Description

This function takes a username and returns a list with two elements:

Usage

check_user_access(al, u, admin = "admin")

Arguments

Details

user_group: one element vector data_area: vector of data areas

Value

list of user groups and data areas

Examples

# save access details to file
home <- Sys.getenv('HOME')

# create carnation data area if it doesn't exist
carnation_home <- file.path(home, 'carnation/data')
if(!dir.exists(carnation_home)) dir.create(carnation_home)

create_access_yaml(user = 'admin',
                   user_group = 'admin',
                   data_area = carnation_home)

# get current user access details
al <- read_access_yaml()

lst <- check_user_access(al, u='admin')

Description

UI & module to generate Cnetplots.

Usage

cnetPlotUI(id, panel)

cnetPlotServer(id, obj, config)

Arguments

Value

UI returns tagList with plot UI server invisibly returns NULL (used for side effects)

Examples

library(shiny)

# get DESeqResults object
data(res_dex, package='carnation')

obj <- reactive({ res })

config <- reactiveVal(get_config())

# run simple shiny app with plot
if(interactive()){
  shinyApp(
    ui = fluidPage(
           sidebarPanel(cnetPlotUI('p', 'sidebar')),
           mainPanel(cnetPlotUI('p', 'main'))
         ),
    server = function(input, output, session){
               cnetPlotServer('p', obj, config)
             }
  )
}

Description

This function creates an access yaml file. This is primarily intended for the first run.

Usage

create_access_yaml(user, user_group, data_area)

Arguments

Value

Invisibly returns NULL. This function is primarily used for its side effect of saving a yaml file with access settings

Examples

# save access details to file
home <- Sys.getenv('HOME')

# create carnation data area if it doesn't exist
carnation_home <- file.path(home, 'carnation/data')
if(!dir.exists(carnation_home)) dir.create(carnation_home)

create_access_yaml(user = 'admin',
                   user_group = 'admin',
                   data_area = carnation_home)

Description

Module UI & server to generate pattern plots.

Usage

patternPlotUI(id, panel, tab)

patternPlotServer(id, obj, coldata, gene_scratchpad, upset_data, config)

Arguments

Value

UI returns tagList with module UI server returns reactive with selected genes for scratchpad updates

Examples

library(shiny)
library(DESeq2)

# Create reactive values to simulate app state
oobj <- make_example_carnation_object()

obj <- reactiveValues(
   dds = oobj$dds,
   rld = oobj$rld,
   res = oobj$res,
   all_dds = oobj$all_dds,
   all_rld = oobj$all_rld,
   dds_mapping = oobj$dds_mapping
)

cdata <- lapply(oobj$rld, function(x) colData(x))

coldata <- reactiveValues( all=cdata, curr=cdata )

gene_scratchpad <- reactive({ c('gene1', 'gene2') })
upset_data <- reactive({ list(genes=NULL, labels=NULL) })

config <- reactiveVal(get_config())

shinyApp(
  ui = fluidPage(
         sidebarPanel(
           patternPlotUI('p', 'sidebar', 'both'),
           conditionalPanel(condition = "input.pattern_mode == 'Plot'",
             patternPlotUI('p', 'sidebar', 'plot')
           ),
           conditionalPanel(condition = "input.pattern_mode == 'Table'",
             patternPlotUI('p', 'sidebar', 'table')
           )
         ),
         mainPanel(
           tabsetPanel(id='pattern_mode',
             tabPanel('Plot',
               patternPlotUI('p', 'plot')
             ), # tabPanel plot

             tabPanel('Cluster membership',
               patternPlotUI('p', 'table')
             ) # tabPanel cluster_membership

           ) # tabsetPanel pattern_mode
         ) # tabPanel pattern_analysis
       ),
  server = function(input, output, session){
             patternPlotServer('deg_plot', obj, coldata,
                               gene_scratchpad, upset_data, config)
           }
)

Description

A degPatterns object for differentially expressed genes in the dexamethasone treatment comparison.

Format

A degPatterns object, generated with the degPatterns function from the DEGreport package.

Details

This degPatterns object was created to test for groups of coexpressed genes in the top 100 differentially expressed genes from the dexamethasone treatment comparison.

Details on how this object has been created are included in the create_carnation_data.R script, included in the scripts folder of the Carnation package.

References

Himes BE, Jiang X, Wagner P, Hu R, Wang Q, Klanderman B, Whitaker RM, Duan Q, Lasky-Su J, Nikolos C, Jester W, Johnson M, Panettieri R Jr, Tantisira KG, Weiss ST, Lu Q. “RNA-Seq Transcriptome Profiling Identifies CRISPLD2 as a Glucocorticoid Responsive Gene that Modulates Cytokine Function in Airway Smooth Muscle Cells.” PLoS One. 2014 Jun 13;9(6):e99625. PMID: 24926665. GEO: GSE52778

Description

UI & module to generate dendrograms.

Usage

dendrogramUI(id, panel)

dendrogramServer(id, obj, config)

Arguments

Value

UI returns tagList with plot UI server invisibly returns NULL (used for side effects)

Examples

library(shiny)

# get enrichResult object
data(eres_dex, package='carnation')

# convert to GeneTonic object
gt <- GeneTonic::shake_enrichResult(eres_dex)

obj <- reactive({
  list(l_gs = gt$l_gs,
       anno_df = gt$anno_df,
       label = 'comp1')
})

config <- reactiveVal(get_config())

# run simple shiny app with plot
if(interactive()){
  shinyApp(
    ui = fluidPage(
           sidebarPanel(dendrogramUI('p', 'sidebar')),
           mainPanel(dendrogramUI('p', 'main'))
         ),
    server = function(input, output, session){
               dendrogramServer('p', obj, config)
             }
  )
}

Description

UI & module to generate distill enrichment map plots.

Usage

distillPlotUI(id, panel)

distillPlotServer(id, obj, args, config)

Arguments

Value

UI returns tagList with plot UI server returns reactive with number of plotted terms

Examples

library(GeneTonic)
library(shiny)

# get DESeqResults object
data(res_dex, package='carnation')

# get enrichResult object
data(eres_dex, package='carnation')

# preprocess & convert to GeneTonic object
eres2 <- GeneTonic::shake_enrichResult(eres_dex)
gt <- enrich_to_genetonic(eres_dex, res_dex)

# get distilled results
df <- distill_enrichment(
        eres2,
        res_dex,
        gt$anno_df,
        n_gs = 10,
        cluster_fun = "cluster_markov"
      )

# number of plotted terms
args <- reactive({ list(numcat=10) })

config <- reactiveVal(get_config())

# run simple shiny app with plot
if(interactive()){
  shinyApp(
    ui = fluidPage(
           sidebarPanel(distillPlotUI('p', 'sidebar')),
           mainPanel(distillPlotUI('p', 'main'))
         ),
    server = function(input, output, session){
               numcat <- observe({
                 distillPlotServer('p',
                                   reactive({ df }),
                                   args,
                                   config)
               })
             }
  )
}

Description

Module UI & server for download buttons.

Usage

downloadButtonUI(id)

downloadButtonServer(id, outplot, plot_type)

Arguments

Value

UI returns tagList with download button UI. Server invisibly returns NULL (used for side effects).

Examples

library(shiny)
library(ggplot2)

# get example object
obj <- make_example_carnation_object()
res <- as.data.frame(obj$res[[1]])

# make MA plot
p <- ggplot(res, aes(x=baseMean, y=log2foldChange)) +
       geom_point(color='black', alpha=0.5)

outplot <- reactive({ p })

# app with a single button to download a plot
if(interactive()){
  shinyApp(
    ui = fluidPage(
           downloadButtonUI('p')
         ),
    server = function(input, output, session){
               downloadButtonServer('p', outplot, 'maplot')
             }
  )
}

Description

Make dummy GeneTonic object

Usage

dummy_genetonic(eres)

Arguments

Value

GeneTonic object

Description

UI & module to generate enrichment map plots.

Usage

enrichmapUI(id, panel)

enrichmapServer(id, obj, res_obj, config)

Arguments

Value

UI returns tagList with plot UI server invisibly returns NULL (used for side effects)

Examples

library(shiny)

# get DESeqResults object
data(res_dex, package='carnation')

# get enrichResult object
data(eres_dex, package='carnation')

# convert to GeneTonic object
gt <- GeneTonic::shake_enrichResult(eres_dex)

obj <- reactive({
  list(l_gs = gt$l_gs,
       anno_df = gt$anno_df,
       label = 'comp1')
})

res_obj <- reactive({ res })

config <- reactiveVal(get_config())

# run simple shiny app with plot
if(interactive()){
  shinyApp(
    ui = fluidPage(
           sidebarPanel(enrichmapUI('p', 'sidebar')),
           mainPanel(enrichmapUI('p', 'main'))
         ),
    server = function(input, output, session){
               enrichmapServer('p', obj, res_obj, config)
             }
  )
}

Description

This function takes an enrichResult object and DE analysis results and creates a GeneTonic object.

Usage

enrich_to_genetonic(enrich, res)

Arguments

Value

GeneTonic object

Examples

# get enrich & res objects
data(res_dex, package="carnation")
data(eres_dex, package="carnation")

# convert to GeneTonic object
gt <- enrich_to_genetonic(eres_dex, res_dex)

Description

An enrichResult object for differentially expressed genes in the cell line comparison.

Format

An enrichResult object, generated with the enrichGO function from the clusterProfiler package.

Details

This enrichResult object was created to test for functional enrichment using the GO Biological Process (BP) ontology on the top 100 differentially expressed genes from the cell line comparison.

Details on how this object has been created are included in the create_carnation_data.R script, included in the scripts folder of the Carnation package.

References

Himes BE, Jiang X, Wagner P, Hu R, Wang Q, Klanderman B, Whitaker RM, Duan Q, Lasky-Su J, Nikolos C, Jester W, Johnson M, Panettieri R Jr, Tantisira KG, Weiss ST, Lu Q. “RNA-Seq Transcriptome Profiling Identifies CRISPLD2 as a Glucocorticoid Responsive Gene that Modulates Cytokine Function in Airway Smooth Muscle Cells.” PLoS One. 2014 Jun 13;9(6):e99625. PMID: 24926665. GEO: GSE52778

Description

An enrichResult object for differentially expressed genes in the dexamethasone treatment comparison.

Format

An enrichResult object, generated with the enrichGO function from the clusterProfiler package.

Details

This enrichResult object was created to test for functional enrichment using the GO Biological Process (BP) ontology on the top 100 differentially expressed genes from the dexamethasone treatment comparison.

Details on how this object has been created are included in the create_carnation_data.R script, included in the scripts folder of the Carnation package.

References

Himes BE, Jiang X, Wagner P, Hu R, Wang Q, Klanderman B, Whitaker RM, Duan Q, Lasky-Su J, Nikolos C, Jester W, Johnson M, Panettieri R Jr, Tantisira KG, Weiss ST, Lu Q. “RNA-Seq Transcriptome Profiling Identifies CRISPLD2 as a Glucocorticoid Responsive Gene that Modulates Cytokine Function in Airway Smooth Muscle Cells.” PLoS One. 2014 Jun 13;9(6):e99625. PMID: 24926665. GEO: GSE52778

Description

This function works by grouping long lists of genes into groups of a specified size. Each group is collapsed using commas, while groups are separated by spaces so that datatable formatting is tricked into separating space-separated groups and not comma-separated groups

Usage

format_genes(g, sep = "\\/", genes.per.line = 6)

Arguments

Value

vector of gene names prettified for data.table output

Examples

# string with genes separated by '/'
g <- "gene1/gene2/gene3/gene4/gene5/gene6/gene7"

gg <- format_genes(g, genes.per.line=3)

Description

Prepare list for UpSet plots, but include rownames

Usage

fromList.with.names(lst)

Arguments

Value

data.frame of 1 and 0 showing which genes are in which sets

Examples

# list of genes
lst <- list(group1 = c(a = "gene1", b = "gene2", c = "gene3", d = "gene4"),
            group2 = c(c = "gene3", d = "gene4"))

# binarized matrix with group membership
df <- fromList.with.names(lst)

Description

UI & module to show functional enrichment tables & plots.

Usage

enrichUI(id, panel, tab = "none")

enrichServer(id, obj, upset_table, gene_scratchpad, reset_genes, config)

Arguments

Value

UI returns tagList with plot UI server returns reactive with gene selected from functional enrichment tables.

Examples

library(shiny)
library(DESeq2)

# Create reactive values to simulate app state
oobj <- make_example_carnation_object()

obj <- reactiveValues(
   dds = oobj$dds,
   rld = oobj$rld,
   res = oobj$res,
   all_dds = oobj$all_dds,
   all_rld = oobj$all_rld,
   dds_mapping = oobj$dds_mapping
)

upset_table <- reactiveValues(tbl=NULL, intersections=NULL, set_labels=NULL)

gene_scratchpad <- reactive({ c('gene1', 'gene2') })

config <- reactiveVal(get_config())

shinyApp(
  ui = fluidPage(
         sidebarPanel(
           conditionalPanel(condition = "input.func == 'Table'",
             enrichUI('p', 'sidebar', 'table')
           ),
           conditionalPanel(condition = "input.func == 'Plot'",
             enrichUI('p', 'sidebar', 'plot')
           ),
           conditionalPanel(condition = "input.func == 'Compare results'",
             enrichUI('p', 'sidebar', 'compare_results')
           )
         ),
         mainPanel(
             tabsetPanel(id='func',
               tabPanel('Table',
                 enrichUI('p', 'main', 'table')
               ), # tabPanel table

               tabPanel('Plot',
                 enrichUI('p', 'main', 'plot')
               ), # tabPanel plot

               tabPanel('Compare results',
                 enrichUI('p', 'main', 'compare_results')
               ) # tabPanel compare_results

             ) # tabsetPanel func
           ) # tabPanel
         ),
  server = function(input, output, session){
             enrich_data <- enrichServer('p', obj,
                                         upset_table,
                                         gene_scratchpad,
                                         reactive({ FALSE }),
                                         config)
           }
)

Description

UI & module to generate fuzzy enrichment map plots.

Usage

fuzzyPlotUI(id, panel)

fuzzyPlotServer(id, obj, args, config)

Arguments

Value

UI returns tagList with plot UI server returns reactive with number of plotted terms

Examples

library(shiny)

# get enrichResult object
data(eres_dex, package='carnation')

# preprocess & convert to GeneTonic object
gt <- GeneTonic::shake_enrichResult(eres_dex)

# get distilled results
df <- GeneTonic::gs_fuzzyclustering(gt[seq_len(10),],
        similarity_threshold = 0.35,
        fuzzy_seeding_initial_neighbors = 3,
        fuzzy_multilinkage_rule = 0.5)

# number of plotted terms
args <- reactive({ list(numcat=10) })

config <- reactiveVal(get_config())

# run simple shiny app with plot
if(interactive()){
  shinyApp(
    ui = fluidPage(
           sidebarPanel(fuzzyPlotUI('p', 'sidebar')),
           mainPanel(fuzzyPlotUI('p', 'main'))
         ),
    server = function(input, output, session){
               numcat <- observe({
                 fuzzyPlotServer('p',
                                 reactive({ df }),
                                 args,
                                 config)
               })
             }
  )
}

Description

UI & server for module to create gene plot

Usage

genePlotUI(id, panel)

genePlotServer(id, obj, coldata, plot_args, config)

Arguments

Value

UI returns tagList with gene plot UI. Server invisibly returns NULL (used for side effects).

Examples

library(shiny)
library(DESeq2)

# Create reactive values to simulate app state
oobj <- make_example_carnation_object()

obj <- reactiveValues(
   dds = oobj$dds,
   rld = oobj$rld,
   res = oobj$res,
   all_dds = oobj$all_dds,
   all_rld = oobj$all_rld,
   dds_mapping = oobj$dds_mapping
)

# Set up coldata structure that the module expects
coldata <- reactiveValues(
  curr = list(
    all_samples = colData(oobj$dds$main),
    main = colData(oobj$dds$main)
  )
)

plot_args <- reactive({
  list(
    gene.to.plot = c("gene1", "gene2"),
    gene.id = rownames(oobj$dds$main),
    comp_all = "comp1"
  )
})

config <- reactiveVal(get_config())

shinyApp(
  ui = fluidPage(
         sidebarPanel(genePlotUI('p', 'sidebar')),
         mainPanel(genePlotUI('p', 'main'))
       ),
  server = function(input, output, session){
             genePlotServer('p', obj, coldata, plot_args, config)
           }
)

Description

This function checks for an environment variable 'CARNATION_ACCESS_YAML' to specify directory to save access yaml. If env variable does not exist uses home directory as save location.

Usage

get_access_path()

Value

path to access yaml

Examples

p <- get_access_path()

Description

This function reads the bundled package config and returns it. If a local config yaml exists, only supported user-editable settings are merged into the returned config.

Usage

get_config(config_path = NULL)

Arguments

Value

list containing config items

Examples

cfg <- get_config()

Description

This function checks for an environment variable CARNATION_CONFIG_YAML to specify the local config yaml path. If the variable is not set, a default path in the home directory is used.

Usage

get_config_path()

Value

path to local config yaml

Examples

p <- get_config_path()

Description

This function plots a degPatterns object.

Usage

get_degplot(
  obj,
  time,
  color = NULL,
  cluster_column = "cluster",
  cluster_to_show,
  x_order,
  points = TRUE,
  boxes = TRUE,
  smooth = "smooth",
  lines = TRUE,
  facet = TRUE,
  prefix_title = "Cluster ",
  genes_to_label = NULL
)

Arguments

Value

ggplot handle

Examples

# get degpatterns object
data(degpatterns_dex, package = 'carnation')

# get pattern plot
all_clusters <- unique(degpatterns_dex$normalized$cluster)

dp <- get_degplot(degpatterns_dex, time='dex',
                  cluster_to_show=all_clusters,
                  x_order=c('untrt','trt'))

Description

This is a simple function to obtain read counts for a specified gene, based on the DESeq2::plotCounts function.

Usage

get_gene_counts(dds, gene, intgroup = "condition", norm_method = "libsize")

Arguments

Value

data.frame with gene counts

Examples

# make example DESeq data set
dds <- DESeq2::makeExampleDESeqDataSet()

# get counts for gene1
gg <- get_gene_counts(dds, 'gene1')

Description

This function takes in a path to an RDS file and returns a string to be used as project name

Usage

get_project_name_from_path(
  x,
  depth = 2,
  end_offset = 0,
  staging_dir = "dev",
  fsep = .Platform$file.sep
)

Arguments

Value

project name parsed from path to object

Examples

# path to carnation object
obj_path <- "/path/to/project/test/main.rnaseq.rds"

# parsed project name
get_project_name_from_path(obj_path, depth = 2, end_offset = 0)

Description

Generate upset plot table

Usage

get_upset_table(gene.lists, comp_split_pattern = ";")

Arguments

Value

list with upset table elements

Examples

lst <- list(group1 = c(a = "gene1", b = "gene2", c = "gene3", d = "gene4"),
            group2 = c(b = "gene2", d = "gene4", e = "gene5"),
            group3 = c(d = "gene4", e = "gene5", f = "gene6"))

df <- get_upset_table(lst)
str(df)

Description

Get initial y-axis limits

Usage

get_y_init(df, y_delta, pseudocount)

Arguments

Value

min and max limits for count column, padded for visualization

Examples

# make example DESeq dataset
dds <- DESeq2::makeExampleDESeqDataSet()

# get gene counts
df <- get_gene_counts(dds, gene = c('gene1', 'gene2'))

# get y axis limits
get_y_init(df, y_delta = 0.01, pseudocount = 1)

Description

This function creates the gene plot.

Usage

getcountplot(
  df,
  intgroup = "group",
  factor.levels,
  title = NULL,
  ylab = "Normalized counts",
  color = "gene",
  nrow = 2,
  ymin = NULL,
  ymax = NULL,
  log = TRUE,
  freey = FALSE,
  trendline = "smooth",
  facet = NULL,
  legend = TRUE,
  boxes = TRUE,
  rotate_x_labels = 30
)

Arguments

Value

ggplot handle

Examples

# make example DESeq dataset
dds <- DESeq2::makeExampleDESeqDataSet()

# get gene counts
df <- get_gene_counts(dds, gene = c('gene1', 'gene2'))

# standard gene plot
p <- getcountplot(df, intgroup = "condition", factor.levels = c("A", "B"))

# with genes faceted
p1 <- getcountplot(df, intgroup = "condition", factor.levels = c("A", "B"), facet = "gene")

Description

This is a copy of gs_radar from GeneTonic where the labels of gene sets are converted to parameters

Usage

gs_radar(
  res_enrich,
  res_enrich2 = NULL,
  label1 = "scenario 1",
  label2 = "scenario 2",
  n_gs = 20,
  p_value_column = "gs_pvalue"
)

Arguments

Value

ggplot handle

Examples

library(GeneTonic)

# get DESeqResults object
data(res_dex, package='carnation')

# get enrichResult object
data(eres_dex, package='carnation')

# convert to GeneTonic object
gt <- shake_enrichResult(eres_dex)

# get annotation df
idx <- match(c('gene','symbol'), tolower(colnames(res_dex)))
anno_df <- res_dex[,idx]
colnames(anno_df) <- c('gene_id', 'gene_name')

# add aggregate score columns
gt <- get_aggrscores(gt, res_dex, anno_df)

# make radar plot
p <- gs_radar(gt)

Description

Module UI & server to generate heatmap.

Usage

heatmapUI(id, panel)

heatmapServer(id, obj, coldata, plot_args, gene_scratchpad, config)

Arguments

Value

UI returns tagList with heatmap UI. Server invisibly returns NULL (used for side effects).

Examples

library(shiny)
library(DESeq2)

# Create reactive values to simulate app state
oobj <- make_example_carnation_object()

obj <- reactiveValues(
   dds = oobj$dds,
   rld = oobj$rld,
   res = oobj$res,
   all_dds = oobj$all_dds,
   all_rld = oobj$all_rld,
   dds_mapping = oobj$dds_mapping
)

cdata <- lapply(oobj$rld, function(x) colData(x))

coldata <- reactiveValues( all=cdata, curr=cdata )

plot_args <- reactive({
  list(
    fdr.thres=0.1,
    fc.thres=0,
    upset_data=list(genes=NULL, labels=NULL)
  )
})

gene_scratchpad <- reactive({ c('gene1', 'gene2') })

config <- reactiveVal(get_config())

shinyApp(
  ui = fluidPage(
         sidebarPanel(heatmapUI('p', 'sidebar')),
         mainPanel(heatmapUI('p', 'sidebar'))
       ),
  server = function(input, output, session){
             heatmapServer('p', obj, coldata,
                           plot_args, gene_scratchpad, config)
           }
)

Description

Module UI & server for help buttons.

Usage

helpButtonUI(id)

helpButtonServer(id, ...)

Arguments

Value

UI returns tagList with help button UI. Server invisibly returns NULL (used for side effects).

Examples

library(shiny)

# app with a single help button to show DE summary table details
if(interactive()){
  shinyApp(
    ui = fluidPage(
           helpButtonUI('de_summary_help')
         ),
    server = function(input, output, session){
               helpButtonServer('de_summary_help')
             }
  )
}

Description

This generates a modal dialog that includes text from a markdown file.

Usage

helpModal(mdfile, title = NULL, ...)

Arguments

Value

Modal dialog with help documentation.

Description

UI & module to generate horizon plots.

Usage

horizonUI(id, panel)

horizonServer(id, obj, config)

Arguments

Value

UI returns tagList with plot UI server invisibly returns NULL (used for side effects)

Examples

library(shiny)

# get enrichResult object
data(eres_dex, package='carnation')

# convert to GeneTonic object
gt <- GeneTonic::shake_enrichResult(eres_dex)

# get second enrichResult object
data(eres_cell, package='carnation')

# convert to GeneTonic object
gt1 <- GeneTonic::shake_enrichResult(eres_cell)

obj <- reactive({
  list(
    obj1 = list(l_gs = gt$l_gs,
             anno_df = gt$anno_df,
             label = 'comp1'),
    obj2 = list(l_gs = gt1$l_gs,
             anno_df = gt1$anno_df,
             label = 'comp2')
  )
})

config <- reactiveVal(get_config())

# run simple shiny app with plot
if(interactive()){
  shinyApp(
    ui = fluidPage(
           sidebarPanel(horizonUI('p', 'sidebar')),
           mainPanel(horizonUI('p', 'main'))
         ),
    server = function(input, output, session){
               horizonServer('p', obj, config)
             }
  )
}

Description

is user is in admin group?

Usage

in_admin_group(u)

Arguments

Value

TRUE/FALSE to indicate if the user is part of the admin group

Examples

# save access details to file
home <- Sys.getenv('HOME')

# create carnation data area if it doesn't exist
carnation_home <- file.path(home, 'carnation/data')
if(!dir.exists(carnation_home)) dir.create(carnation_home)

create_access_yaml(user = 'admin',
                   user_group = 'admin',
                   data_area = carnation_home)

check <- in_admin_group('user')

Description

This function copies the bundled package config to a user-writable local config yaml. This is intended for users who want to customize the supported config settings without editing the installed package.

Usage

init_local_config(config_path = get_config_path(), overwrite = FALSE)

Arguments

Value

Path to the local config yaml, invisibly.

Examples

cfg_out <- tempfile(fileext = ".yaml")
init_local_config(cfg_out)

Description

This function installs 'plotly' and 'kaleido' python packages in an environment to allow PDF downloads from plotly plots.

Usage

install_carnation(envname, ...)

Arguments

Value

NULL, invisibly. The function is called for its side effects.

Examples

if(interactive()){
    install_carnation()
}

Description

is user an admin?

Usage

is_site_admin(u)

Arguments

Value

boolean to indicate is user is in admin group

Examples

# check if default user is admin
yy <- is_site_admin(u='admin')

Description

Validate the schema for a single degpatterns analysis element used by the pattern analysis module.

Usage

is_valid_pattern_obj(pattern_obj, require_symbol = FALSE)

Arguments

Value

Returns TRUE when validation succeeds, otherwise returns FALSE after emitting a message describing the issue.

Examples

data(degpatterns_dex, package = "carnation")

is_valid_pattern_obj(degpatterns_dex)

Description

Module UI & server to load new data

Usage

loadDataUI(id)

loadDataServer(id, username, config, rds = NULL)

Arguments

Value

UI returns tagList with module UI Server returns reactive with app reload trigger

Examples

library(shiny)

username <- 'admin'

config <- reactiveVal(get_config())

obj <- make_example_carnation_object()

rds <- reactive({ obj=obj })

shinyApp(
  ui = fluidPage(
         loadDataUI('p')
       ),
  server = function(input, output, session){
             loadDataServer('p', username=username, config, rds)
           }
)

Description

Returns example carnation object used in examples & testing

Usage

make_example_carnation_object()

Value

reactiveValues object containing carnation object

Examples

obj <- make_example_carnation_object()

Description

This function takes an uploaded object and sanitizes it to make sure it is suitable for internal use along with other additions:

• adds a 'dds_mapping' element that maps dds_list keys to res_list objects.

• if there are multiple dds_list objects, it adds a 'all_dds' element combining all samples.

Usage

make_final_object(obj)

Arguments

Value

final carnation object with additional pre-processing

Examples

library(DESeq2)

# make example DESeq dataset
dds <- makeExampleDESeqDataSet()

# run DE analysis
dds <- DESeq(dds)

# extract comparison of interest
res <- results(dds, contrast = c("condition", "A", "B"))

# perform VST normalization
rld <- varianceStabilizingTransformation(dds, blind = TRUE)

# build minimal object
obj <- list(
           res_list = list(
                          comp = list(
                              res = res,
                              dds = "main",
                              label = "A vs B"
                          )
                      ),
           dds_list = list(main = dds),
           rld_list = list(main = rld)
       )

# final object
final_obj <- make_final_object(obj)

Description

Most of the parameters are just placeholders and the dataframe must contain the columns 'ID' and 'geneID'

Usage

makeEnrichResult(
  df,
  split = "/",
  keytype = "UNKNOWN",
  ontology = "UNKNOWN",
  type = "enrichResult"
)

Arguments

Value

enrichResult object

Examples

# get enrichResult object
data(eres_dex, package='carnation')

# extract the results
df <- as.data.frame(eres_dex)

# convert to a stripped down enrichResult object
eres2 <- makeEnrichResult(df)

Description

UI & server for module to create MA plot

Usage

maPlotUI(id, panel)

maPlotServer(id, obj, plot_args, config)

Arguments

Value

UI returns tagList with MA plot UI. Server invisibly returns NULL (used for side effects).

Examples

library(shiny)
library(DESeq2)

# Create reactive values to simulate app state
oobj <- make_example_carnation_object()

obj <- reactiveValues(
   dds = oobj$dds,
   rld = oobj$rld,
   res = oobj$res,
   all_dds = oobj$all_dds,
   all_rld = oobj$all_rld,
   dds_mapping = oobj$dds_mapping
)

# Set up coldata structure that the module expects
coldata <- reactiveValues(
  curr = list(
    all_samples = colData(oobj$dds$main),
    main = colData(oobj$dds$main)
  )
)

plot_args <- reactive({
  list(
    fdr.thres=0.1,
    fc.thres=0,
    gene.to.plot=c('gene1', 'gene2')
  )
})

config <- reactiveVal(get_config())

shinyApp(
  ui = fluidPage(
         sidebarPanel(maPlotUI('p', 'sidebar')),
         mainPanel(maPlotUI('p', 'main'))
       ),
  server = function(input, output, session){
             maPlotServer('p', obj, plot_args, config)
           }
)

Description

This function materializes expensive derived pieces for a validated carnation object, including DESeqDataSet creation from raw count matrices, variance-stabilized counts, and GeneTonic conversions.

Usage

materialize_carnation_object(obj, config = NULL, cores = NULL)

Arguments

Value

The input object with materialized dds_list, rld_list, and optional genetonic slots.

Examples

# Minimal example with DE results and counts
library(DESeq2)

# Create example data
dds <- makeExampleDESeqDataSet()
dds <- DESeq(dds)
res <- results(dds, contrast = c("condition", "A", "B"))
rld <- varianceStabilizingTransformation(dds, blind = TRUE)

# Validate object inputs
obj <- validate_carnation_object(
  res_list = list(
    comp1 = list(
      res = as.data.frame(res),
      dds = "main",
      label = "A vs B"
    )
  ),
  dds_list = list(main = dds),
  rld_list = list(main = rld)
)

materialized <- materialize_carnation_object(obj, cores = 1)

Description

This module generates the metadata tab that allows users to view the metadata associated with the loaded carnation object.

Usage

metadataUI(id, panel)

metadataServer(id, obj, cols.to.drop)

Arguments

Value

UI returns tagList with metadata UI. Server returns reactive object with metadata.

Examples

library(shiny)

# Create reactive values to simulate app state
oobj <- make_example_carnation_object()

obj <- reactiveValues(
   dds = oobj$dds,
   rld = oobj$rld,
   res = oobj$res,
   all_dds = oobj$all_dds,
   all_rld = oobj$all_rld,
   dds_mapping = oobj$dds_mapping
)

config <- get_config()
cols.to.drop <- config$server$cols.to.drop

shinyApp(
  ui = fluidPage(
         sidebarPanel(metadataUI('p', 'sidebar')),
         mainPanel(metadataUI('p', 'main'))
       ),
  server = function(input, output, session){
             # reactiveVal to save updates
             saved_data <- reactiveVal()

             cdata <- metadataServer('p', obj, cols.to.drop)

             observeEvent(cdata(), {
               saved_data(cdata())
             })
           }
)

Description

summary(res) prints out info; this function captures it into a dataframe

Usage

my.summary(res, dds, alpha, lfc.thresh = 0)

Arguments

Value

Dataframe of summarized results

Examples

n_genes <- 100

#  make mock dds list
dds <- DESeq2::makeExampleDESeqDataSet(n=n_genes)

# make mock results df
res <- data.frame(
         baseMean = runif(n_genes, 10, 1000),
         log2FoldChange = rnorm(n_genes, 0, 2),
         lfcSE = runif(n_genes, 0.1, 0.5),
         stat = rnorm(n_genes, 0, 3),
         pvalue = runif(n_genes, 0, 1),
         padj = runif(n_genes, 0, 1),
         symbol = paste0("GENE", 1:n_genes),
         row.names = paste0("gene", 1:n_genes)
       )

# get summary
df <- my.summary(res, dds, alpha=0.1)

Description

Module UI + server to generate a pca plot.

Usage

pcaPlotUI(id, panel)

pcaPlotServer(id, obj, coldata, config)

Arguments

Value

UI returns tagList with PCA plot UI. Server invisibly returns NULL (used for side effects).

Examples

library(shiny)
library(DESeq2)

# Create reactive values to simulate app state
oobj <- make_example_carnation_object()

obj <- reactiveValues(
   dds = oobj$dds,
   rld = oobj$rld,
   res = oobj$res,
   all_dds = oobj$all_dds,
   all_rld = oobj$all_rld,
   dds_mapping = oobj$dds_mapping
)

# Set up coldata structure that the module expects
coldata <- reactiveValues(
  curr = list(
    all_samples = colData(oobj$dds$main),
    main = colData(oobj$dds$main)
  )
)

config <- reactiveVal(get_config())

shinyApp(
  ui = fluidPage(
         sidebarPanel(pcaPlotUI('p', 'sidebar')),
         mainPanel(pcaPlotUI('p', 'main'))
       ),
  server = function(input, output, session){
             pcaPlotServer('p', obj, coldata, config)
           }
)

Description

This function creates an MA plot from a data.frame containing DE analysis results.

Usage

plotMA.label(
  res,
  fdr.thres = 0.01,
  fc.thres = 0,
  fc.lim = NULL,
  lab.genes = NULL,
  tolower.cols = c("SYMBOL", "ALIAS")
)

Arguments

Value

ggplot handle

Examples

# make mock results df
n_genes <- 100
res <- data.frame(
         baseMean = runif(n_genes, 10, 1000),
         log2FoldChange = rnorm(n_genes, 0, 2),
         lfcSE = runif(n_genes, 0.1, 0.5),
         stat = rnorm(n_genes, 0, 3),
         pvalue = runif(n_genes, 0, 1),
         padj = runif(n_genes, 0, 1),
         symbol = paste0("GENE", 1:n_genes),
         row.names = paste0("gene", 1:n_genes)
       )

plotMA.label(res, lab.genes = c("gene1", "gene2"))

Description

This function creates an MA plot from a data.frame containing DE analysis results using plot_ly

Usage

plotMA.label_ly(
  res,
  fdr.thres = 0.01,
  fc.thres = 0,
  fc.lim = NULL,
  lab.genes = NULL,
  tolower.cols = c("SYMBOL", "ALIAS")
)

Arguments

Value

plotly handle

Examples

# make mock results df
n_genes <- 100
res <- data.frame(
         baseMean = runif(n_genes, 10, 1000),
         log2FoldChange = rnorm(n_genes, 0, 2),
         lfcSE = runif(n_genes, 0.1, 0.5),
         stat = rnorm(n_genes, 0, 3),
         pvalue = runif(n_genes, 0, 1),
         padj = runif(n_genes, 0, 1),
         symbol = paste0("GENE", 1:n_genes),
         row.names = paste0("gene", 1:n_genes)
       )

plotMA.label_ly(res, lab.genes = c("gene1", "gene2"))

Description

Plot an interactive PCA plot

Usage

plotPCA.ly(rld, intgroup)

Arguments

Value

Handle to ggplot with added label field in aes_string() for plotting with ggplotly()

Examples

# make example dds object
dds <- DESeq2::makeExampleDESeqDataSet()

# normalize
rld <- DESeq2::varianceStabilizingTransformation(dds, blind=TRUE)

# make pca plot
p <- plotPCA.ly(rld, intgroup='condition')

Description

Create a PCA plot with specified PCs on x- and y-axis

Usage

plotPCA.san(
  object,
  intgroup = "group",
  pcx,
  pcy,
  pcz = NULL,
  ntop = 500,
  samples = NULL,
  loadings = FALSE,
  loadings_ngenes = 10
)

Arguments

Value

ggplot handle

Examples

# make example dds object
dds <- DESeq2::makeExampleDESeqDataSet()

# normalize
rld <- DESeq2::varianceStabilizingTransformation(dds, blind=TRUE)

# make pca plot
p <- plotPCA.san(rld, intgroup='condition', pcx='PC1', pcy='PC2')

Description

Plot a scatterplot to compare two contrasts

Usage

plotScatter.label(
  compare,
  df,
  label_x,
  label_y,
  lim.x,
  lim.y,
  color.palette,
  lab.genes = NULL,
  plot_all = "no",
  name.col = "geneid",
  lines = c("yes", "yes", "yes"),
  alpha = 1,
  size = 4,
  show.grid = "yes"
)

Arguments

Value

ggplot handle

Examples

# make mock results df
n_genes <- 100
res1 <- data.frame(
          baseMean = runif(n_genes, 10, 1000),
          log2FoldChange = rnorm(n_genes, 0, 2),
          lfcSE = runif(n_genes, 0.1, 0.5),
          stat = rnorm(n_genes, 0, 3),
          pvalue = runif(n_genes, 0, 1),
          padj = runif(n_genes, 0, 1),
          symbol = paste0("GENE", 1:n_genes),
          row.names = paste0("gene", 1:n_genes)
        )

res2 <- data.frame(
          baseMean = runif(n_genes, 10, 1000),
          log2FoldChange = rnorm(n_genes, 0, 2),
          lfcSE = runif(n_genes, 0.1, 0.5),
          stat = rnorm(n_genes, 0, 3),
          pvalue = runif(n_genes, 0, 1),
          padj = runif(n_genes, 0, 1),
          symbol = paste0("GENE", 1:n_genes),
          row.names = paste0("gene", 1:n_genes)
        )

# add geneid column
res1 <- cbind(geneid=rownames(res1), res1)
res2 <- cbind(geneid=rownames(res2), res2)

# make merged df from the two comparisons
cols.sub <- c('log2FoldChange', 'padj', 'geneid')
df_full <- dplyr::inner_join(
  dplyr::select(as.data.frame(res1), all_of(cols.sub)),
  dplyr::select(as.data.frame(res2), all_of(cols.sub)),
  by = 'geneid',
  suffix = c('.x', '.y')
)

# calculate x & y limits for log2FoldChange
xlim <- range(df_full[[ 'log2FoldChange.x' ]])
ylim <- range(df_full[[ 'log2FoldChange.y' ]])

# get color palette
color.palette <- RColorBrewer::brewer.pal(n=5, name='Set2')

# add significance column
sig.x <- df_full$padj.x < 0.1 & !is.na(df_full$padj.x)
sig.y <- df_full$padj.y < 0.1 & !is.na(df_full$padj.y)
up.x <- df_full$log2FoldChange.x >= 0
up.y <- df_full$log2FoldChange.y >= 0
significance <- rep('None', nrow(df_full))
significance[ sig.x & sig.y & ((up.x & up.y) | (!up.x & !up.y)) ] <- 'Both - same LFC sign'
significance[ sig.x & sig.y & ((up.x & !up.y) | (!up.x & up.y)) ] <- 'Both - opposite LFC sign'
significance[ sig.x & !sig.y ] <- 'A vs B'
significance[ !sig.x & sig.y ] <- 'B vs A'
df_full$significance <- significance

# generate scatter plot
p <- plotScatter.label(compare = 'log2FoldChange',
                       df = df_full,
                       label_x = 'A vs B',
                       label_y = 'B vs A',
                       lim.x = xlim,
                       lim.y = ylim,
                       color.palette = color.palette)

Description

Plot an interactive scatterplot to compare two contrasts

Usage

plotScatter.label_ly(
  compare,
  df,
  label_x,
  label_y,
  lim.x,
  lim.y,
  color.palette,
  lab.genes = NULL,
  name.col = "geneid",
  lines = c("yes", "yes", "yes"),
  alpha = 1,
  size = 4,
  show.grid = "yes",
  source = "A"
)

Arguments

Value

plotly handle

Examples

# make mock results df
n_genes <- 100
res1 <- data.frame(
          baseMean = runif(n_genes, 10, 1000),
          log2FoldChange = rnorm(n_genes, 0, 2),
          lfcSE = runif(n_genes, 0.1, 0.5),
          stat = rnorm(n_genes, 0, 3),
          pvalue = runif(n_genes, 0, 1),
          padj = runif(n_genes, 0, 1),
          symbol = paste0("GENE", 1:n_genes),
          row.names = paste0("gene", 1:n_genes)
        )

res2 <- data.frame(
          baseMean = runif(n_genes, 10, 1000),
          log2FoldChange = rnorm(n_genes, 0, 2),
          lfcSE = runif(n_genes, 0.1, 0.5),
          stat = rnorm(n_genes, 0, 3),
          pvalue = runif(n_genes, 0, 1),
          padj = runif(n_genes, 0, 1),
          symbol = paste0("GENE", 1:n_genes),
          row.names = paste0("gene", 1:n_genes)
        )

# add geneid column
res1 <- cbind(geneid=rownames(res1), res1)
res2 <- cbind(geneid=rownames(res2), res2)

# make merged df from the two comparisons
cols.sub <- c('log2FoldChange', 'padj', 'geneid')
df_full <- dplyr::inner_join(
  dplyr::select(as.data.frame(res1), all_of(cols.sub)),
  dplyr::select(as.data.frame(res2), all_of(cols.sub)),
  by = 'geneid',
  suffix = c('.x', '.y')
)

# calculate x & y limits for log2FoldChange
xlim <- range(df_full[[ 'log2FoldChange.x' ]])
ylim <- range(df_full[[ 'log2FoldChange.y' ]])

# get color palette
color.palette <- RColorBrewer::brewer.pal(n=5, name='Set2')

# add significance column
sig.x <- df_full$padj.x < 0.1 & !is.na(df_full$padj.x)
sig.y <- df_full$padj.y < 0.1 & !is.na(df_full$padj.y)
up.x <- df_full$log2FoldChange.x >= 0
up.y <- df_full$log2FoldChange.y >= 0
significance <- rep('None', nrow(df_full))
significance[ sig.x & sig.y & ((up.x & up.y) | (!up.x & !up.y)) ] <- 'Both - same LFC sign'
significance[ sig.x & sig.y & ((up.x & !up.y) | (!up.x & up.y)) ] <- 'Both - opposite LFC sign'
significance[ sig.x & !sig.y ] <- 'A vs B'
significance[ !sig.x & sig.y ] <- 'B vs A'
df_full$significance <- significance

# generate scatter plot
p <- plotScatter.label_ly(compare = 'log2FoldChange',
                          df = df_full,
                          label_x = 'A vs B',
                          label_y = 'B vs A',
                          lim.x = xlim,
                          lim.y = ylim,
                          color.palette = color.palette)

Description

UI & module to generate radar plots.

Usage

radarUI(id, panel, type = "")

radarServer(id, obj, config, type = "")

Arguments

Value

UI returns tagList with plot UI server invisibly returns NULL (used for side effects)

Examples

library(shiny)

# get enrichResult object
data(eres_dex, package='carnation')

# convert to GeneTonic object
gt <- GeneTonic::shake_enrichResult(eres_dex)

obj <- reactive({
  list(l_gs = gt$l_gs,
       anno_df = gt$anno_df,
       label = 'comp1')
})

config <- reactiveVal(get_config())

# run simple shiny app with plot
if(interactive()){
  shinyApp(
    ui = fluidPage(
           sidebarPanel(radarUI('p', 'sidebar')),
           mainPanel(radarUI('p', 'main'))
         ),
    server = function(input, output, session){
               radarServer('p', obj, config)
             }
  )
}

Description

This function reads the access yaml file and returns user groups and data areas as a list of data frames.

Usage

read_access_yaml()

Value

return carnation access settings from yaml file

Examples

# save access details to file
home <- Sys.getenv('HOME')

# create carnation data area if it doesn't exist
carnation_home <- file.path(home, 'carnation/data')
if(!dir.exists(carnation_home)) dir.create(carnation_home)

create_access_yaml(user = 'admin',
                   user_group = 'admin',
                   data_area = carnation_home)

al <- read_access_yaml()

Description

A DESeqResults object testing the difference between two cell lines of smooth muscle cells

Format

A DESeqResults object, generated in the DESeq2 framework

Details

This DESeqResults object on the data from the airway package has been created comparing two smooth muscle cell lines, accounting for the effect of dexamethasone treatment.

Details on how this object has been created are included in the create_carnation_data.R script, included in the scripts folder of the Carnation package.

References

Himes BE, Jiang X, Wagner P, Hu R, Wang Q, Klanderman B, Whitaker RM, Duan Q, Lasky-Su J, Nikolos C, Jester W, Johnson M, Panettieri R Jr, Tantisira KG, Weiss ST, Lu Q. “RNA-Seq Transcriptome Profiling Identifies CRISPLD2 as a Glucocorticoid Responsive Gene that Modulates Cytokine Function in Airway Smooth Muscle Cells.” PLoS One. 2014 Jun 13;9(6):e99625. PMID: 24926665. GEO: GSE52778

Description

A DESeqResults object testing the effect of dexamethasone on smooth muscle cells

Format

A DESeqResults object, generated in the DESeq2 framework

Details

This DESeqResults object on the data from the airway package has been created comparing dexamethasone treated vs untreated samples, accounting for the different cell lines included.

Details on how this object has been created are included in the create_carnation_data.R script, included in the scripts folder of the Carnation package.

References

Himes BE, Jiang X, Wagner P, Hu R, Wang Q, Klanderman B, Whitaker RM, Duan Q, Lasky-Su J, Nikolos C, Jester W, Johnson M, Panettieri R Jr, Tantisira KG, Weiss ST, Lu Q. “RNA-Seq Transcriptome Profiling Identifies CRISPLD2 as a Glucocorticoid Responsive Gene that Modulates Cytokine Function in Airway Smooth Muscle Cells.” PLoS One. 2014 Jun 13;9(6):e99625. PMID: 24926665. GEO: GSE52778

Description

Interactive shiny dashboard for exploring RNA-Seq analysis.

Usage

run_carnation(
  credentials = NULL,
  passphrase = NULL,
  enable_admin = TRUE,
  config_path = NULL,
  ...
)

Arguments

Value

shinyApp object

Examples

if(interactive()){
    shiny::runApp(
        run_carnation()
    )
}

Description

This function saves access details (user groups and data areas) to the designated access yaml file.

Usage

save_access_yaml(lst)

Arguments

Value

save access settings to yaml file

Examples

# save access details to file
home <- Sys.getenv('HOME')

# create carnation data area if it doesn't exist
carnation_home <- file.path(home, 'carnation/data')
if(!dir.exists(carnation_home)) dir.create(carnation_home)

create_access_yaml(user = 'admin',
                   user_group = 'admin',
                   data_area = carnation_home)

# read access yaml
lst <- read_access_yaml()

# add new user
lst$user_group$admin <- c(lst$user_group$admin, 'user1')

# save to access settings
save_access_yaml(lst)

Description

Module UI & server to save carnation object.

Usage

saveUI(id)

saveServer(id, original, current, coldata, pattern, username, config)

Arguments

Value

UI returns actionButton Server returns reactive with trigger to refresh the app

Examples

library(shiny)
library(DESeq2)

# default username
username <- reactive({ NULL })

# internal carnation config
config <- reactiveVal(get_config())

# regex to find carnation files
pattern <- reactive({ config()$server$pattern })

# get example object
obj <- make_example_carnation_object()

# make reactive with obj & path
original <- reactiveValues( obj = obj, path = "/path/to/carnation/obj.rds" )

# extract metadata
coldata <- reactive({ lapply(obj$dds, colData) })

# edit metadata
coldata_edit <- lapply(coldata, function(x){
                  x$type <- 'new'; x
                })

# add to object
edit_obj <- obj
for(name in names(edit_obj$dds)){
  colData(edit_obj$dds[[ name ]]) <- coldata_edit[[ name ]]
}

# run simple shiny app with plot
shinyApp(
  ui = fluidPage(
         saveUI('p')
       ),
  server = function(input, output, session){
             save_event <- saveServer('save_object',
                                      original=original,
                                      current=reactive({ edit_obj }),
                                      coldata=coldata,
                                      pattern=pattern(),
                                      username=username,
                                      config)
           }
)

Description

Module UI + server for generating scatter plots.

Usage

scatterPlotUI(id, panel)

scatterPlotServer(id, obj, plot_args, gene_scratchpad, reset_genes, config)

Arguments

Value

UI returns tagList with scatter plot UI. Server invisibly returns NULL (used for side effects).

Examples

library(shiny)

# Create reactive values to simulate app state
oobj <- make_example_carnation_object()

obj <- reactiveValues(
   dds = oobj$dds,
   rld = oobj$rld,
   res = oobj$res,
   all_dds = oobj$all_dds,
   all_rld = oobj$all_rld,
   dds_mapping = oobj$dds_mapping
)

plot_args <- reactive({
  list(
    fdr.thres=0.1,
    fc.thres=0
  )
})

gene_scratchpad <- reactive({ c('gene1', 'gene2') })
reset_genes <- reactiveVal()

config <- reactiveVal(get_config())

shinyApp(
  ui = fluidPage(
         sidebarPanel(scatterPlotUI('p', 'sidebar')),
         mainPanel(scatterPlotUI('p', 'sidebar'))
       ),
  server = function(input, output, session){
             scatter_data <- scatterPlotServer('p', obj, plot_args,
                               gene_scratchpad, reset_genes, config)
           }
)

Description

This function updates a limited subset of the package config YAML. Only stable user-facing settings are writable; style settings and other internal options are intentionally left untouched.

Usage

set_config(
  config_path = get_config_path(),
  de_analysis = NULL,
  fdr_threshold = NULL,
  log2fc_threshold = NULL,
  max_upload_size = NULL,
  cores = NULL,
  pattern = NULL
)

Arguments

Value

Updated config list, invisibly.

Examples

cfg_out <- tempfile(fileext = ".yaml")

set_config(
  config_path = cfg_out,
  de_analysis = list(
    column_names = list(
      padj = "qvalue",
      log2FoldChange = c("logFC", "avg_log2FC")
    )
  ),
  fdr_threshold = 0.05,
  log2fc_threshold = 1,
  max_upload_size = 50,
  cores = 2,
  pattern = "carnation"
)

Description

Module UI & server for user access details interface.

Server code for settings module

Usage

settingsUI(id, panel, username)

settingsServer(id, details, depth, end_offset, assay_fun, config)

Arguments

Value

UI returns tagList with module UI Server returns reactive with list containing user access details

Examples

library(shiny)

# default username
username <- reactive({ NULL })

# internal carnation config
config <- reactiveVal(get_config())

# regex to find carnation files
pattern <- reactive({ config()$server$pattern })

# access permissions
assay.list <- reactiveValues(l=read_access_yaml())

if(interactive()){
  shinyApp(
    ui = fluidPage(
           sidebarPanel(uiOutput('settings_sidebar')),
           mainPanel(uiOutput('settings_main'))
         ),
    server = function(input, output, session){
               output$settings_main <- renderUI({
                 settingsUI('settings', panel='main', username=username)
               })

               output$settings_sidebar <- renderUI({
                 settingsUI('settings', panel='sidebar', username=username)
               })

               settings <- settingsServer('p',
                                          details=reactive({
                                                    list(username=username,
                                                         where=NULL)
                                                  }),
                                          depth=2,
                                          end_offset=0,
                                          assay_fun=function(x)
                                            sub(paste0(pattern(), '\\.rds$'), '',
                                                basename(x),
                                                ignore.case=TRUE),
                                          config
                                          )
             }
  )
}

Description

Combine everything in the results list into a single table

Usage

summarize_res_list(
  res.list,
  dds.list,
  dds_mapping,
  alpha,
  lfc.thresh,
  labels = NULL
)

Arguments

Value

Dataframe

Examples

n_genes <- 100

#  make mock dds list
dds_list <- list(main=DESeq2::makeExampleDESeqDataSet(n=n_genes))

# make mock results df
res1 <- data.frame(
          baseMean = runif(n_genes, 10, 1000),
          log2FoldChange = rnorm(n_genes, 0, 2),
          lfcSE = runif(n_genes, 0.1, 0.5),
          stat = rnorm(n_genes, 0, 3),
          pvalue = runif(n_genes, 0, 1),
          padj = runif(n_genes, 0, 1),
          symbol = paste0("GENE", 1:n_genes),
          row.names = paste0("gene", 1:n_genes)
        )

res2 <- data.frame(
          baseMean = runif(n_genes, 10, 1000),
          log2FoldChange = rnorm(n_genes, 0, 2),
          lfcSE = runif(n_genes, 0.1, 0.5),
          stat = rnorm(n_genes, 0, 3),
          pvalue = runif(n_genes, 0, 1),
          padj = runif(n_genes, 0, 1),
          symbol = paste0("GENE", 1:n_genes),
          row.names = paste0("gene", 1:n_genes)
        )

# make list of results
res_list <- list(
              comp1=res1,
              comp2=res2
            )

# make dds mapping
dds_mapping <- list(comp1='main', comp2='main')

# get summary
df <- summarize_res_list(res_list, dds_list, dds_mapping, alpha=0.1, lfc.thresh=0)

Description

UI & module to generate summary overview plots.

Usage

sumovPlotUI(id, panel, type = "")

sumovPlotServer(id, obj, config, type = "")

Arguments

Value

UI returns tagList with plot UI server invisibly returns NULL (used for side effects)

Examples

library(shiny)

# get enrichResult object
data(eres_dex, package='carnation')

# convert to GeneTonic object
gt <- GeneTonic::shake_enrichResult(eres_dex)

obj <- reactive({
  list(l_gs = gt$l_gs,
       anno_df = gt$anno_df,
       label = 'comp1')
})

config <- reactiveVal(get_config())

# run simple shiny app with plot
if(interactive()){
  shinyApp(
    ui = fluidPage(
           sidebarPanel(sumovPlotUI('p', 'sidebar')),
           mainPanel(sumovPlotUI('p', 'main'))
         ),
    server = function(input, output, session){
               sumovPlotServer('p', obj, config)
             }
  )
}

Description

Get top DE genes by log2FoldChange or adjusted p-value

Usage

top.genes(res, fdr.thres = 0.01, fc.thres = 0, n = 10, by = "log2FoldChange")

Arguments

Value

vector of gene symbols

Examples

# get DE results
data(res_dex, package='carnation')

g <- top.genes(res_dex)

Description

Module UI & server to generate upset plots.

Usage

upsetPlotUI(id, panel)

upsetPlotServer(id, obj, plot_args, gene_scratchpad, reset_genes, config)

Arguments

Value

UI returns tagList with upset plot UI. Server returns reactive with list containing upset table, intersections & selected genes.

Examples

library(shiny)

oobj <- make_example_carnation_object()

obj <- reactiveValues(
   dds = oobj$dds,
   rld = oobj$rld,
   res = oobj$res,
   all_dds = oobj$all_dds,
   all_rld = oobj$all_rld,
   dds_mapping = oobj$dds_mapping
)

plot_args <- reactive({
  list(
    fdr.thres=0.1,
    fc.thres=0
  )
})

gene_scratchpad <- reactive({ c('gene1', 'gene2') })

reset_genes <- reactiveVal()

config <- reactiveVal(get_config())

shinyApp(
  ui = fluidPage(
         sidebarPanel(upsetPlotUI('p', 'sidebar')),
         mainPanel(upsetPlotUI('p', 'sidebar'))
       ),
  server = function(input, output, session){
             upset_data <- upsetPlotServer('p', obj, plot_args,
                                           gene_scratchpad,
                                           reset_genes, config)
           }
)

Description

This function takes various input data types (DE results, counts, enrichment, pattern analysis) and validates them according to carnation's requirements, returning a normalized intermediate object. Expensive derived-object creation steps such as variance-stabilized counts and GeneTonic conversion are handled separately by materialize_carnation_object().

Usage

validate_carnation_object(
  res_list,
  dds_list,
  rld_list = NULL,
  labels = NULL,
  enrich_list = NULL,
  degpatterns = NULL,
  metadata = NULL,
  dds_mapping = NULL,
  config = NULL
)

Arguments

Details

This function performs comprehensive validation of all input data:

• DE results: Checks for required columns (with support for DESeq2, edgeR, limma), ensures gene and symbol columns exist

• Counts: Validates structure, checks sample name matching with metadata

• Enrichment: Validates clusterProfiler format (OR or GSEA)

• Pattern analysis: Checks for required columns (genes, value, cluster)

If validation fails, the function will stop with an informative error message.

Value

A validated list with canonical slots res_list, dds_list, optional rld_list, labels, dds_mapping, enrich_list, degpatterns, and metadata when supplied.

A list containing normalized inputs with elements res_list, dds_list, optional rld_list, labels, dds_mapping, and optional enrich_list, degpatterns, and metadata.

Examples

# Minimal example with DE results and counts
library(DESeq2)

# Create example data
dds <- makeExampleDESeqDataSet()
dds <- DESeq(dds)
res <- results(dds, contrast = c("condition", "A", "B"))
rld <- varianceStabilizingTransformation(dds, blind = TRUE)

# Validate object inputs
obj <- validate_carnation_object(
  res_list = list(
    comp1 = list(
      res = as.data.frame(res),
      dds = "main",
      label = "A vs B"
    )
  ),
  dds_list = list(main = dds),
  rld_list = list(main = rld)
)

materialized <- materialize_carnation_object(obj, cores = 1)
final_obj <- make_final_object(materialized)

# Save for use with carnation
saveRDS(final_obj, "my_analysis.rds")

# Alternative: start from count matrix and metadata
counts <- as.data.frame(counts(dds))
counts$gene <- rownames(counts)
counts <- counts[, c(ncol(counts), 1:(ncol(counts)-1))]
metadata <- as.data.frame(colData(dds))
metadata$sample <- rownames(metadata)
metadata <- metadata[, c(ncol(metadata), 1:(ncol(metadata)-1))]

obj <- validate_carnation_object(
  res_list = list(comp1 = as.data.frame(res)),
  dds_list = list(main = counts),
  metadata = metadata,
  dds_mapping = list(comp1 = "main")
)