nf-core/pixelator

Edit

Introduction

nf-core/pixelator is a bioinformatics best-practice analysis pipeline for analysis of data from the Molecular Pixelation (MPX) and Proximity Network (PNA) assays. It takes a samplesheet as input and will process your data using pixelator to produce a PXL file containing single-cell protein abundance and protein interactomics data.

Depending on the input data the pipeline will run different steps.

For PNA data, the pipeline will run the following steps:

1. Do quality control checks of input reads and build amplicons (pixelator single-cell-pna amplicon)
2. Create groups of amplicons based on their marker assignments (pixelator single-cell-pna demux)
3. Derive original molecules to use as edge list downstream by error correcting, and counting input amplicons (pixelator single-cell-pna collapse)
4. Compute the components of the graph from the edge list in order to create putative cells (pixelator single-cell-pna graph)
5. Analyze the spatial information in the cell graphs (pixelator single-cell-pna analysis)
6. Generate 3D graph layouts for visualization of cells (pixelator single-cell-pna layout)
7. Report generation (pixelator single-cell-pna report)

For MPX data, the pipeline will run the following steps:

1. Build an amplicons from the input reads (pixelator single-cell-mpx amplicon)
2. Read QC and filtering, correctness of the pixel binding sequence sequences (pixelator single-cell-mpx preqc | pixelator adapterqc)
3. Assign a marker (barcode) to each read (pixelator single-cell-mpx demux)
4. Error correction, duplicate removal, compute read counts (pixelator single-cell-mpx collapse)
5. Compute the components of the graph from the edge list in order to create putative cells (pixelator single-cell-mpx graph)
6. Call and annotate cells (pixelator single-cell-mpx annotate)
7. Analyze the cells for polarization and colocalization (pixelator single-cell-mpx analysis)
8. Generate 3D graph layouts for visualization of cells (pixelator single-cell-mpx layout)
9. Report generation (pixelator single-cell-mpx report)

Usage

First, prepare a samplesheet with your input data that looks as follows (the exact values you need to input depend on the design and panel you are using):

samplesheet.csv:

sample,design,panel,fastq_1,fastq_2
sample1,pna-2,proxiome-immuno-155,sample1_R1_001.fastq.gz,sample1_R2_001.fastq.gz

Each row represents a sample and gives the design, a panel file and the input fastq files.

Now, you can run the pipeline using:

nextflow run nf-core/pixelator \
   -profile <docker/singularity/.../institute> \
   --input samplesheet.csv \
   --outdir <OUTDIR>

For more details and further functionality, please refer to the usage documentation and the parameter documentation.

Pipeline output

To see the results of an example test run with a full size dataset refer to the results tab on the nf-core website pipeline page. For more details about the output files and reports, please refer to the output documentation.

Credits

nf-core/pixelator was originally written for Pixelgen Technologies AB by:

• Florian De Temmerman
• Johan Dahlberg
• Alvaro Martinez Barrio

Contributions and Support

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

For further information or help, don’t hesitate to get in touch on the Slack #pixelator channel (you can join with this invite).

Citations

If you use nf-core/pixelator for your analysis, please cite it using the following doi: 10.5281/zenodo.10015112

An extensive list of references for the tools used by the pipeline can be found in the CITATIONS.md file.

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.

You can cite the molecular pixelation technology as follows:

Molecular pixelation: spatial proteomics of single cells by sequencing.

Filip Karlsson, Tomasz Kallas, Divya Thiagarajan, Max Karlsson, Maud Schweitzer, Jose Fernandez Navarro, Louise Leijonancker, Sylvain Geny, Erik Pettersson, Jan Rhomberg-Kauert, Ludvig Larsson, Hanna van Ooijen, Stefan Petkov, Marcela González-Granillo, Jessica Bunz, Johan Dahlberg, Michele Simonetti, Prajakta Sathe, Petter Brodin, Alvaro Martinez Barrio & Simon Fredriksson

Nat Methods. 2024 May 08. doi: 10.1038/s41592-024-02268-9