The CytobankAPI package is designed to make interacting with Cytobank API endpoints easy via R. This document is an accompanying overview of the package to learn concepts and see basic examples. View the Cytobank API Endpoint Documentation for a comprehensive list of API endpoints for Cytobank.
Within the CytobankAPI package, there are endpoints to interact with advanced analyses via R. This documentation is an overview of the different ways to utilize advanced analyses. To find more general documentation on using the CytobankAPI package, view the Cytobank quickstart guide.
All advanced analyses are encapsulated within an object. This guide will be an overview of advanced analyses object structures:
Every advanced analysis is represented as an object. Creating a new advanced analysis will return an object that is passed to all of their other respective advanced analysis endpoints.
Important information to note:
show endpoint, or update the advanced analysis with the current settings using the update endpointviSNE_analysis <- visne.show(cyto_session, experiment_id=22, visne_id=214)
viSNE_analysis@name
#> [1] "My viSNE analysis example"
# Update the viSNE analysis object name directly
viSNE_analysis@name <- "My updated viSNE analysis name"
# Update the viSNE analysis using the 'visne.update' endpoint
updated_viSNE <- visne.update(cyto_session, viSNE_analysis)
updated_viSNE@name
#> [1] "My updated viSNE analysis name"There are common features for all advanced analyses:
There are special settings that pertain to each advanced analysis algorithm. These settings affect how the advanced analysis algorithm is ran. For each advanced analysis, you can view their respective settings and slots as shown below.
CITRUS_object <- citrus.new(cyto_session, experiment_id, citrus_name="My new Cytobank CITRUS analysis")slotNames(CITRUS_object)
#> [1] "citrus_id" "population_id"
#> [3] "file_grouping" "association_models"
#> [5] "cluster_characterization" "statistics_channels"
#> [7] "event_sampling_method" "events_per_file"
#> [9] "minimum_cluster_size" "cross_validation_folds"
#> [11] "false_discovery_rate" "normalize_scales"
#> [13] "plot_theme" "attachment_id"
#> [15] "channels" "compensation_id"
#> [17] "name" "source_experiment"
#> [19] "status" ".available_channels"
#> [21] ".available_files" ".available_populations"Learn more about CITRUS settings.
FlowSOM_object <- flowsom.new(cyto_session, experiment_id, flowsom_name="My new Cytobank FlowSOM analysis")slotNames(FlowSOM_object)
#> [1] "author"
#> [2] "type"
#> [3] "flowsom_id"
#> [4] "selected_population_name"
#> [5] "population_id"
#> [6] "num_fcs_files"
#> [7] "fcs_files"
#> [8] "event_sampling_method"
#> [9] "desired_events_per_file"
#> [10] "desired_total_events"
#> [11] "sampled_event_total"
#> [12] "num_events_to_actually_sample"
#> [13] "random_seed"
#> [14] "som_creation_method"
#> [15] "clustering_method"
#> [16] "expected_metaclusters"
#> [17] "expected_clusters"
#> [18] "iterations"
#> [19] "normalize_scales"
#> [20] "created_experiment"
#> [21] "attachment_id"
#> [22] "auto_seed"
#> [23] "external_som_analysis_info"
#> [24] "external_som_analysis_id"
#> [25] "channels_to_plot"
#> [26] "cluster_size_type"
#> [27] "fixed_cluster_size"
#> [28] "gate_set_names_to_label"
#> [29] "max_relative_cluster_size"
#> [30] "output_file_type"
#> [31] "show_background_on_legend"
#> [32] "show_background_on_channel_colored_msts"
#> [33] "show_background_on_population_pies"
#> [34] "final_result"
#> [35] "completed"
#> [36] "canceled"
#> [37] "channels"
#> [38] "compensation_id"
#> [39] "name"
#> [40] "source_experiment"
#> [41] "status"
#> [42] ".available_channels"
#> [43] ".available_files"
#> [44] ".available_populations"Learn more about FlowSOM settings.
SPADE_object <- spade.new(cyto_session, experiment_id=22, spade_name="My new Cytobank SPADE analysis")slotNames(SPADE_object)
#> [1] "created_experiment" "down_sampled_events_target"
#> [3] "down_sampled_events_type" "fold_change_groups"
#> [5] "population_id" "spade_id"
#> [7] "target_number_nodes" "channels"
#> [9] "compensation_id" "name"
#> [11] "source_experiment" "status"
#> [13] ".available_channels" ".available_files"
#> [15] ".available_populations"Learn more about SPADE settings.
viSNE_object <- visne.new(cyto_session, experiment_id, visne_name="My new Cytobank viSNE analysis")slotNames(viSNE_object)
#> [1] "created_experiment" "iterations"
#> [3] "perplexity" "population_selections"
#> [5] "sampling_total_count" "sampling_target_type"
#> [7] "seed" "theta"
#> [9] "visne_id" "channels"
#> [11] "compensation_id" "name"
#> [13] "source_experiment" "status"
#> [15] ".available_channels" ".available_files"
#> [17] ".available_populations"See each section below for instructions on how to interact with the object for each advanced analysis.
Directly update CITRUS settings via their slot names.
The following slots can be updated directly:
# Set a new plot theme, association models, and compensation
CITRUS_object@plot_theme <- "black"
CITRUS_object@association_models <- c("pamr", "glmnet")
CITRUS_object@compensation_id <- 22
# Bulk update the changes made to the CITRUS object
CITRUS_object <- citrus.update(cyto_session, CITRUS_object)The core functionality of CITRUS is establishing biological explanations for why samples between two or more groups differ from each other. CITRUS file grouping is used to categorize different files into these groups. There is 1 important setting to pay attention to:
group_name: The group that each file is associated with
Directly update CITRUS file grouping data.
# Set 'file1.fcs' through 'file4.fcs' to 'Group 1' and 'file5.fcs' through 'file8.fcs' to 'Group 2'
CITRUS_object@file_grouping[CITRUS_object@file_grouping$id <= 44856,]$group_name <- "Group 1"
CITRUS_object@file_grouping[is.element(c(44857, 44858, 44859, 44860), CITRUS_object@file_grouping$id),]$group_name <- "Group 2"
View(CITRUS_object@file_grouping)| id | name | group_name |
|---|---|---|
| 44853 | file1.fcs | Group 1 |
| 44854 | file2.fcs | Group 1 |
| 44855 | file3.fcs | Group 1 |
| 44856 | file4.fcs | Group 1 |
| 44857 | file5.fcs | Group 2 |
| 44858 | file6.fcs | Group 2 |
| 44859 | file7.fcs | Group 2 |
| 44860 | file8.fcs | Group 2 |
| 44861 | file9.fcs | Unassigned |
Directly update FlowSOM settings via their slot names.
The following slots can be updated directly:
If the required channels and fcs_files slots are not present, updates will not occur to the FlowSOM analysis.
# Set a clustering method, target number of nodes, and compensation
FlowSOM_object@clustering_method <- "kmeans"
FlowSOM_object@num_expected_clusters <- 144
FlowSOM_object@compensation_id <- 22
# Update FCS file selection to the first 5 files
FlowSOM_object@fcs_files <- FlowSOM_object@.available_files$id[1:4]
# Update channel selection
FlowSOM_object@channels <- list("CD3", "CD4")
# Bulk update the changes made to the FlowSOM object
FlowSOM_object <- flowsom.update(cyto_session, FlowSOM_object)Directly update SPADE settings via their slot names.
The following slots can be updated directly:
# Set a new population, target number of nodes, and compensation
SPADE_object@population_id <- 2
SPADE_object@target_number_nodes <- 150
SPADE_object@compensation_id <- 22
# Update channels
channel_ids_list <- list(2, 3, 5, 8)
SPADE_object@channels <- channel_ids_list
# Update channels by long channel names
channel_names_list <- list("channel1", "channel2", "channel3")
SPADE_object@channels <- channel_names_list
# Bulk update the changes made to the SPADE object
SPADE_object <- spade.update(cyto_session, SPADE_object)
SPADE_object@population_id
#> [1] 2
SPADE_object@target_number_nodes
#> [1] 150
SPADE_object@compensation_id
#> [1] 22
SPADE_object@channels
#> [[1]]
#> [1] "channel1"
#>
#> [[2]]
#> ...SPADE fold change groups are used to categorize different files into separate collections that will be compared amongst each other. There are 2 important settings to pay attention to:
Directly update SPADE fold change groups data.
# Set 'file6.fcs' and 'file7.fcs' as the baseline for 'Group 1'
SPADE_object@fold_change_groups[grep("my_file6|my_file7",
SPADE_object@fold_change_groups$name),]$baseline <- TRUE
# Set 'file2.fcs', 'file4.fcs', and 'file8.fcs' as part of 'Group 2', and set 'file2.fcs' as the baseline
SPADE_object@fold_change_groups[grep("file2|file4|file8",
SPADE_object@fold_change_groups$name),]$group_name <- "Group 2"
SPADE_object@fold_change_groups[SPADE_object@fold_change_groups$name=="file2.fcs",]$baseline <- TRUE
View(SPADE_object@fold_change_groups)| id | name | baseline | group_name |
|---|---|---|---|
| 44853 | file1.fcs | FALSE | Group 1 |
| 44854 | file2.fcs | TRUE | Group 2 |
| 44855 | file3.fcs | FALSE | Group 1 |
| 44856 | file4.fcs | FALSE | Group 2 |
| 44857 | file5.fcs | FALSE | Group 1 |
| 44858 | file6.fcs | TRUE | Group 1 |
| 44859 | file7.fcs | TRUE | Group 1 |
| 44860 | file8.fcs | FALSE | Group 2 |
Directly update viSNE settings via their slot names.
The following slots can be updated directly:
The following slots must be updated via helper functions:
# Set a new sampling target type, sampling total count, and compensation
viSNE_object@sampling_target_type <- "equal"
viSNE_object@sampling_total_count <- 150000
viSNE_object@compensation_id <- 22
# Bulk update the changes made to the viSNE object
viSNE_object <- visne.update(cyto_session, viSNE_object)Adding viSNE population selections is slightly more difficult because the same file can be used in the analysis in combination with multiple populations. Because of this complexity, the visne.helper.set_populations helper function is used to set files for a selected population.
Parameters for visne.helper.set_populations:
.available_populations slot)Set files for a specific population through the visne.helper.set_populations helper function.
Setting files for a specific population will overwrite the files previously set for the population in question.
# Set files for different populations
viSNE_object <- visne.helper.set_populations(viSNE_object, population_id=1, fcs_files=c(44853))
viSNE_object <- visne.helper.set_populations(viSNE_object, population_id=2, fcs_files=c(44867,44868))
viSNE_object <- visne.helper.set_populations(viSNE_object, population_id=4, fcs_files=unlist(visne@.available_files[grep("file4|file5|file6", visne@.available_files$filename),]$id))
# Overwrite 'population_id=2' FCS file selection, note that 'file1.fcs' and 'file2.fcs' are in both 'Population 1', as well as 'Population 2'
viSNE_object <- visne.helper.set_populations(viSNE_object, population_id=2, fcs_files=c(44854,44855, 44853, 44867))
# Update the changes made to viSNE population selections
viSNE_object <- visne.update(cyto_session, viSNE_object)
View(viSNE_object@population_selections)| id | name | samplingCount | eventCount | populationId | populationName |
|---|---|---|---|---|---|
| 44853 | file1.fcs | NA | NA | 1 | Population 1 |
| 44856 | file4.fcs | NA | NA | 4 | Population 4 |
| 44857 | file5.fcs | NA | NA | 4 | Population 4 |
| 44858 | file6.fcs | NA | NA | 4 | Population 4 |
| 44854 | file2.fcs | NA | NA | 2 | Population 2 |
| 44855 | file3.fcs | NA | NA | 2 | Population 2 |
| 44853 | file1.fcs | NA | NA | 2 | Population 2 |
| 44856 | file4.fcs | NA | NA | 2 | Population 2 |