splitRtools: Preprocessing tools for SPLiT-seq data

Welcome to the splitRtools package!

This package is under active development and all functionality is not yet validated!!

The package may change significantly over development

⏬ Installation

The package can be installed from this github repository:

# Install devtools for github installation if not present
require(devtools)

# Install package from github repo
devtools::install_github("https://github.com/JamesOpz/splitRtools")

Overview

The splitRtools package is a collection of tools that are used to process SPLiT-seq scRNA-seq data (Rosenberg et.al, 2019).

The splitRtools package is designed to take as input data, the output from the zUMIs package (paper). The zUMIs package is used to take raw FASTQ output, assign and filter reads to barcodes and map reads to the reference genome producing a cellxcount matrix, as well as some reporting about the pipeline outputs.

A sample zUMIs pipeline with configuration to work with the Rosenberg-2019 barcode setup is available here.

Running the splitRtools pipeline

Data input directoruy structure

Executing the pipeline

The splitRtools pipeline is run through the run_split_pipe() function, which acts as a wrapper function to execute the pipeline. A basic setup for the pipeline is as follows:

# Load splitRtools
library(splitRtools)

# Run the splitRtool pipeline
# You must always point to two parent folders containing sublibrary raw FASTQ folders
# Each sublibrary is within this folder and must contain zUMIs output
run_split_pipe(mode = 'merge', # Merge sublibraries or process seperately
               n_sublibs = 2, # How many to sublibraries are present
               data_folder = "./../test_data_sp_5_miseq/", # Location of zUMIs data directory
               output_folder = "../test_data_sp_5_miseq_outputs/", # Output folder path
               filtering_mode = "knee", # Filter by knee (standard) or manual value (default 1000) transcripts
               fastq_path = "../fastq_single/", # Path to folder containing subibraru raw FastQ
               rt_bc = "../test_data_sp_5_miseq/barcodes_v1.csv", # RT barcode map
               lig_bc = "../test_data_sp_5_miseq/barcodes_v1.csv", # Ligation barcode map
               sample_map = "../test_data_sp_5_miseq/cell_metadata.xlsx" # RT plate layout file
)

Pipeline outputs

Output directory structure

Output data

The first stage of the pipeline labels converts the cell count matrix into a SingleCellExperiment object and labels each cell with various ColData with a series of well IDs based each stage of the barcoding process and the correspondence between the RT wells and the on the sample_map excel file provided. This data is then stored as an SCE or an annData object in unfiltered/ output folder for each sublibrary.

Diagnostic plots

The splitRtool pipeline will generate a set of diagnostic plots in order to evaluate the initial quality of the SPLiT-seq scRNA-seq data.

After labeling the data is filtered using either the DropletUtils package spline-fitting functionality or a user specified cutoff of transcripts. This produces the following waterfall plot along with quantifiaction of the cell types recovered by sample:




The barcoding cell data is then mapped to the respective plate locations across the 3 barcoding rounds to provide a series of heatmaps displaying cells recovered per well and median UMI per cell across all wells: