nf-core/scrnaseq
A single-cell RNAseq pipeline for 10X genomics data
2.7.1
). The latest
stable release is
3.0.0
.
Introduction
This document describes the output produced by the pipeline. Most of the plots are taken from the MultiQC report, which summarises results at the end of the pipeline.
Pipeline overview
The pipeline is built using Nextflow and processes data using the following steps:
FastQC
See FastQC for details about FastQC.
The pipeline analyzes the raw data and generates for each file a FastQC report. All report are collected in MultiQC.
Output directory: results/fastqc
.html
- Contains the FastQC report.
.zip
- Contains additional information, such as individual plots, and FastQC raw data.
The FastQC plots displayed in the MultiQC report shows the unprocessed reads. Depending on the protocol, they may contain barcodes, UMIs and adapter sequences.
Kallisto & Bustools Results
See Kallisto for details about Kallisto and Bustools for more information on BusTools.
The pipeline can analyze data from single cell rnaseq experiments and generates a set of folders with respective outputs from various steps of the analysis. For a detailed summary what the pipeline does specifically, please follow the excellent tutorial that also describes specific steps for downstream analysis of the generated matrices.
Output directory: results/kallisto
raw_bus
- Contains the unconverted BUS formatted pseudo aligned data
sort_bus
- Contains the same BUS formatted data, sorted and corrected with the supplied barcode whitelist
kallisto_gene_map
- Contains the converted GTF gene map that is used by BUSTools for downstream analysis
bustools_counts
- Contains two subdirectories
eqcount
: Containing the Transcript Compatibility Count (TCC) Matrix (tcc.mtx
)genecount
: Containing the Gene Count Matrix (gene.mtx
)
- Contains two subdirectories
bustools_metrics
* Contains the JSON metrics generated by BUStools
For details on how to load these into R and perform further downstream analysis, please refer to the BusTools HowTo.
Output directory: results/reference_genome
kallisto_index
- Contains the index of the supplied (genome/transcriptome) fasta file
STARsolo
Output directory: results/star
- Files will be organized in one directory per sample
- Contains the mapped BAM files and output metrics created by STARsolo
Output directory: results/reference_genome
star_index
- Contains the index of the supplied genome fasta file
Salmon Alevin & AlevinQC
Output directory: results/alevin
alevin
- Contains the created Salmon Alevin pseudo-aligned output
alevinqc
- Contains the QC report for the aforementioned Salmon Alevin output data
Output directory: results/reference_genome
salmon_index
- Contains the indexed reference transcriptome for Salmon Alevin
alevin/txp2gene.tsv
- The transcriptome to gene mapping TSV file utilized by Salmon Alevin
Cellranger
Cell Ranger is a set of analysis scripts that processes 10x Chromium single cell data to align reads, generate feature-barcode matrices, perform clustering and other secondary analysis. See Cellranger for more information on Cellranger.
Output directory: results/cellranger
- Contains the mapped BAM files, filtered and unfiltered HDF5 matrices and output metrics created by Cellranger
Cellranger ARC
Cell Ranger ARC is a set of analysis pipelines that process Chromium Single Cell Multiome ATAC + Gene Expression sequencing data to generate a variety of analyses pertaining to gene expression (GEX), chromatin accessibility, and their linkage. Furthermore, since the ATAC and GEX measurements are on the very same cell, we are able to perform analyses that link chromatin accessibility and GEX. See Cellranger ARC for more information on Cellranger.
Output directory: results/cellrangerarc
- Contains the mapped BAM files, filtered and unfiltered HDF5 matrices and output metrics created by Cellranger ARC
Cellranger multi
Cell Ranger Multi is the 10x analysis pipeline for multiomics and multiplexed experiments. See Cell Ranger Multi for the corresponding documentation.
Output directory: results/cellrangermulti
- Overall same output structure as cellranger. In case of multiplexed samples there will be one ouput folder for each demultiplexed sample, and one containing all (non-demultiplexed) cells.
UniverSC
UniverSC is a wrapper that calls an open-source implementation of Cell Ranger v3.0.2 and adjusts run parameters for compatibility with a wide ranger of technologies. Custom inputs and at least 40 preset technologies are supported. UniverSC is developed independently from 10X Genomics and all software are not subject to the 10X Genomics End User License Agreement which restricts usage on other platforms. Therefore in principle UniverSC can be run on any scRNA-Seq technology without restrictions to align reads, generate feature-barcode matrices, perform clustering and other secondary analysis. See UniverSC for more information on UniverSC.
UniverSC has been published in Nature Communications.
Battenberg, K., Kelly, S.T., Ras, R.A., Hetherington, N.A., Hayashi, K., and Minoda, A. (2022) A flexible cross-platform single-cell data processing pipeline. Nat Commun 13(1): 1-7. https://doi.org/10.1038/s41467-022-34681-z
Output directory: results/universc
- Contains the mapped BAM files, filtered and unfiltered HDF5 matrices and output metrics created by the open-source implementation of Cell Ranger run via UniverSC
Custom emptydrops filter
The pipeline also possess a module to perform empty-drops calling and filtering with a custom-made script that uses a library called bioconductor-dropletutils
that is available in bioconda
. The process is simple, it takes a raw/unfiltered matrix file, and performs the empty-drops calling and filtering on it, generating another matrix file.
Users can turn it of with
--skip_emptydrops
.
Output directory: results/${params.aligner}/emptydrops_filtered
- Contains the empty-drops filtered matrices results generated by the
bioconductor-dropletutils
custom script
Other output data
Output directory: results/reference_genome
barcodes
- Contains the utilized cell barcode whitelists (if applicable)
extract_transcriptome
- When supplied with a
--fasta
genome fasta, this contains the extracted transcriptome - The GTF file supplied with
--gtf
is used to extract the transcriptome positions appropriately
- When supplied with a
Output directory: results/${params.aligner}/mtx_conversions
*_matrix.h5ad
.mtx
files converted to AnnData in.h5ad
format, using scanpy package.- One per sample and a single one with all samples concatenated together
combined_matrix.h5ad
*_matrix.rds
.mtx
files converted to R native data format, rds, using the Seurat package- One per sample
Because the pipeline has both the data directly from the aligners, and from the custom empty-drops filtering module the conversion modules were modified to understand the difference between raw/filtered from the aligners itself and filtered from the custom empty-drops module. So, to try to avoid confusion by the user, we added “suffixes” to the generated converted files so that we have provenance from what input it came from.
So, the conversion modules generate data with the following syntax: *_{raw,filtered,custom_emptydrops_filter}_matrix.{h5ad,rds}
. With the following meanings:
suffix | meaning |
---|---|
raw | Conversion of the raw/unprocessed matrix generated by the tool. It is also used for tools that generate only one matrix, such as alevin. |
filtered | Conversion of the filtered/processed matrix generated by the tool |
custom_emptydrops_filter | Conversion of the matrix that was generated by the new custom empty drops filter module |
Some aligners, like
alevin
do not produce both raw&filtered matrices. When aligners give only one output, they are treated with theraw
suffix. Some aligners may have an option to give both raw&filtered and only one, likekallisto
. Be aware when using the tools.
MultiQC
Output files
multiqc/
multiqc_report.html
: a standalone HTML file that can be viewed in your web browser.multiqc_data/
: directory containing parsed statistics from the different tools used in the pipeline.multiqc_plots/
: directory containing static images from the report in various formats.
MultiQC is a visualization tool that generates a single HTML report summarising all samples in your project. Most of the pipeline QC results are visualised in the report and further statistics are available in the report data directory.
Results generated by MultiQC collate pipeline QC from supported tools e.g. FastQC. The pipeline has special steps which also allow the software versions to be reported in the MultiQC output for future traceability. For more information about how to use MultiQC reports, see http://multiqc.info.
Pipeline information
Output files
pipeline_info/
- Reports generated by Nextflow:
execution_report.html
,execution_timeline.html
,execution_trace.txt
andpipeline_dag.dot
/pipeline_dag.svg
. - Reports generated by the pipeline:
pipeline_report.html
,pipeline_report.txt
andsoftware_versions.yml
. Thepipeline_report*
files will only be present if the--email
/--email_on_fail
parameter’s are used when running the pipeline. - Reformatted samplesheet files used as input to the pipeline:
samplesheet.valid.csv
. - Parameters used by the pipeline run:
params.json
.
- Reports generated by Nextflow:
Nextflow provides excellent functionality for generating various reports relevant to the running and execution of the pipeline. This will allow you to troubleshoot errors with the running of the pipeline, and also provide you with other information such as launch commands, run times and resource usage.