Getting started with Pluto

Quick start guide

See vignette("authentication") for instructions on creating your personal API token for accessing data. To begin using pluto, load the package and log in with your personal API token:

library(pluto)

pluto_login("<YOUR API TOKEN>")

Interacting with Pluto data models

Pluto data is stored in several data models, which are described in depth in vignette("pluto_models"). For the purposes of this quick start guide, we’ll highlight the main ones:

• Project - collection of Experiments
• Experiment - a data set containing
  • type - a known/predefined assay type in Pluto (e.g. rnaseq, cutandrun, proteomics)
  • Sample Data - tabular annotations and other metadata associated with samples
  • Assay Data - tabular measurements from the assay (e.g. a counts matrix for gene expression experiments)
  • Analysis - known/predefined analyses that can be run in Pluto (e.g. differential_expression, umap)
    • Results - tabular results generated by an Analysis, format depends on the kind of analysis being run

Using these models, you can read data into your Pluto R scripts in a flexible way to serve a wide variety of needs and applications. The basic examples below are intended to illustrate some foundational building blocks when working with Pluto data models.

Fetch data for a single experiment in Pluto

For this example, we’ll use a public ChIP-seq experiment from GEO (GSE150555) that was imported to Pluto: Effect of WDR5 degrader on H3K4me2 in human cancer cell lines. The Pluto ID for this experiment (found in the url) is PLX191681.

Sample data

In Pluto, the sample annotations are stored in a tabular format with automatic enriching of metadata for fields like cell lines.

To read these sample annotations into your R script, provide the experiment ID to the pluto_read_sample_data() function:

experiment_id <- "PLX140206"
  
sample_data <- pluto_read_sample_data(experiment_id)

Assay data