Introduction

salzmanlab/spliz is a bioinformatics best-practise analysis pipeline for calculating the splicing z-score for single cell RNA-seq analysis.

This pipeline uses code and infrastructure developed and maintained by the nf-core community, reused here under the MIT license.

The nf-core framework for community-curated bioinformatics pipelines.

Philip Ewels, Alexander Peltzer, Sven Fillinger, Harshil Patel, Johannes Alneberg, Andreas Wilm, Maxime Ulysse Garcia, Paolo Di Tommaso & Sven Nahnsen.

Nat Biotechnol. 2020 Feb 13. doi: 10.1038/s41587-020-0439-x.

Quick Start

1. Install nextflow (>=20.04.0) and conda.

2. Download environment file.

   wget https://raw.githubusercontent.com/salzmanlab/SpliZ/main/environment.yml

3. Create conda environment and activate.

   conda env create --name spliz_env --file=environment.yml
   conda activate spliz_env

4. Run the pipeline on the test data set. You may need to modify the executor scope in the config file, in accordance to your compute needs.

   nextflow run salzmanlab/spliz \
       -r main \
       -latest \
       -profile small_test_data

   Sherlock users should use the sherlock profile:

    nextflow run salzmanlab/spliz \
        -r main \
        -latest \
        -profile small_test_data,sherlock
   

5. Run the pipeline on your own dataset.

   1. Edit your config file with the parameters below. (You can use /small_data/small.config as a template, be sure to include any memory or time paramters.)
   2. Run with your config file:

   nextflow run salzmanlab/spliz \
       -r main \
       -latest \
       -c YOUR_CONFIG_HERE.conf
   

See usage docs for all of the available options when running the pipeline.

Pipeline Summary

By default, the pipeline currently performs the following:

• Calculate the SpliZ scores for:
  • Identifying variable splice sites
  • Identifying differential splicing between cell types.

Input Parameters

Argument	Description	Example Usage
dataname	Descriptive name for SpliZ run	"Tumor_5"
run_analysis	If the pipeline will perform splice site identifcation and differential splicing analysis	true, false
input_file	File to be used as SpliZ input	tumor_5_with_postprocessing.txt
SICILIAN	If input_file is output from SICILIAN	true, false
pin_S	Bound splice site residuals at this quantile (e.g. values in the lower pin_S quantile and the upper 1 - pin_S quantile will be rounded to the quantile limits)	0.1
pin_z	Bound SpliZ scores at this quantile (e.g. values in the lower pin_z quantile and the upper 1 - pin_z quantile will be rounded to the quantile limits)	0
bounds	Only include cell/gene pairs that have more than this many junctional reads for the gene	5
light	Only output the minimum number of columns	true, false
svd_type	Type of SVD calculation	normdonor, normgene
n_perms	Number of permutations	100
grouping_level_1	Metadata column by which the data is intially partitioned	"tissue"
grouping_level_2	Metadata column by which the partitioned data is grouped	"compartment"
libraryType	Library prepration method of the input data	10X, SS2

Optional Parameters for non-SICILIAN Inputs (SICILIAN = false)

Argument	Description	Example Usage
samplesheet	If input files are in BAM format, this file specifies the locations of the input bam files. Samplesheet formatting is specified below.	Tumor_5_samplesheet.csv
annotator_pickle	Genome-specific annotation file for gene names	hg38_refseq.pkl
exon_pickle	Genome-specific annotation file for exon boundaries	hg38_refseq_exon_bounds.pkl
splice_pickle	Genome-specific annotation file for splice sites	hg38_refseq_splices.pkl
gtf	GTF file used as the reference annotation file for the genome assembly	GRCh38_genomic.gtf
meta	If input files are in BAM format, this file contains per-cell annotations. This file must contain columns for grouping_level_1 and grouping_level_2.	metadata_tumor_5.tsv

Samplesheets

The samplesheet must be in comma-separated value(CSV) format. The file must be without a header. The sampleID must be a unique identifier for each bam file entry.

For non-SICILIAN samples, samplesheets must have 2 columns: sampleID and path to the bam file.

Tumor_5_S1,tumor_5_S1_L001.bam
Tumor_5_S2,tumor_5_S2_L002.bam
Tumor_5_S3,tumor_5_S3_L003.bam

For SICILIAN SS2 samples, amplesheets must have 3 columns: sampleID, read 1 bam file, and read 2 bam file.

Tumor_5_S1,tumor_5_S1_L001_R1.bam,tumor_5_S1_L001_R2.bam
Tumor_5_S2,tumor_5_S2_L002_R1.bam,tumor_5_S2_L002_R2.bam
Tumor_5_S3,tumor_5_S3_L003_R1.bam,tumor_5_S3_L003_R2.bam

Credits

salzmanlab/spliz was originally written by Salzman Lab.

Contributions and Support

If you would like to contribute to this pipeline, please see the contributing guidelines.

Citations

This repositiory contains code to perform the analyses in this paper:

The SpliZ generalizes “Percent Spliced In” to reveal regulated splicing at single-cell resolution

Julia Eve Olivieri*, Roozbeh Dehghannasiri*, Julia Salzman.

Nature Methods 2022 Mar 3. doi: https://www.nature.com/articles/s41592-022-01400-x.

You can cite the nf-core publication as follows:

The nf-core framework for community-curated bioinformatics pipelines.

Philip Ewels, Alexander Peltzer, Sven Fillinger, Harshil Patel, Johannes Alneberg, Andreas Wilm, Maxime Ulysse Garcia, Paolo Di Tommaso & Sven Nahnsen.

Nat Biotechnol. 2020 Feb 13. doi: 10.1038/s41587-020-0439-x.