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nf-core/scrnaseq: Output

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.

:::note 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)
  • 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

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 the raw suffix. Some aligners may have an option to give both raw&filtered and only one, like kallisto. 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 and pipeline_dag.dot/pipeline_dag.svg.
    • Reports generated by the pipeline: pipeline_report.html, pipeline_report.txt and software_versions.yml. The pipeline_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.

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.