# 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](https://www.nextflow.io/) and processes data using the following steps:

- [nf-core/scrnaseq: Output](#nf-corescrnaseq-output)
  - [:warning: Please read this documentation on the nf-core website: https://nf-co.re/scrnaseq/output](#warning-please-read-this-documentation-on-the-nf-core-website-httpsnf-corescrnaseqoutput)
  - [Introduction](#introduction)
  - [Pipeline overview](#pipeline-overview)
  - [FastQC](#fastqc)
  - [Kallisto & Bustools Results](#kallisto--bustools-results)
  - [STARsolo](#starsolo)
  - [Salmon Alevin & AlevinQC](#salmon-alevin--alevinqc)
  - [Cellranger](#cellranger)
  - [Cellranger ARC](#cellranger-arc)
  - [UniverSC](#universc)
  - [Other output data](#other-output-data)
  - [MultiQC](#multiqc)
  - [Pipeline information](#pipeline-information)

## FastQC

See [FastQC](https://www.bioinformatics.babraham.ac.uk/projects/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](https://pachterlab.github.io/kallisto/about) for details about Kallisto and [Bustools](https://bustools.github.io/) 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](https://www.kallistobus.tools/getting_started.html) 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](https://github.com/BUStools/getting_started/blob/master/getting_started.ipynb).

**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](https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/latest/what-is-cell-ranger) 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](https://support.10xgenomics.com/single-cell-multiome-atac-gex/software/pipelines/latest/what-is-cell-ranger-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

## 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](https://github.com/minoda-lab/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

## 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](https://anndata.readthedocs.io/en/latest/) in `.h5ad` format, using [scanpy package](https://scanpy.readthedocs.io/en/stable/).
  - One per sample and a single one with all samples concatenated together `combined_matrix.h5ad`

## MultiQC

<details markdown="1">
<summary>Output files</summary>

- `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.

</details>

[MultiQC](http://multiqc.info) 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

<details markdown="1">
<summary>Output files</summary>

- `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`.

</details>

[Nextflow](https://www.nextflow.io/docs/latest/tracing.html) 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.
