Metadata-Version: 2.4
Name: fun_proteomics
Version: 0.6.0
Summary: Portable PASEF proteomics workflows, verified continuation, and protein/gene differential analysis
Author: cfarkas
License-Expression: MIT
Project-URL: Homepage, https://github.com/cfarkas/fun_proteomics
Project-URL: Repository, https://github.com/cfarkas/fun_proteomics
Project-URL: Issues, https://github.com/cfarkas/fun_proteomics/issues
Keywords: proteomics,PASEF,DIA,Nextflow,differential-abundance
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: Operating System :: POSIX :: Linux
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Requires-Python: >=3.11
Description-Content-Type: text/markdown
License-File: LICENSE
License-File: LICENSE_NOTES.md
License-File: modules/diapasef/NOTICE.md
Requires-Dist: numpy<3,>=1.26
Requires-Dist: pandas<3,>=2.2
Requires-Dist: matplotlib<4,>=3.8
Requires-Dist: pyarrow<26,>=19
Requires-Dist: openpyxl<4,>=3.1
Requires-Dist: jsonschema<5,>=4.23
Provides-Extra: build
Requires-Dist: build>=1.2; extra == "build"
Requires-Dist: twine>=6; extra == "build"
Dynamic: license-file

# fun_proteomics

Analyze Bruker DDA-PASEF and DIA-PASEF data, or start with a protein quantification table. Get protein and gene matrices, all-versus-all comparisons, quality control, and detection benchmarks against a service provider. Every matrix is delivered as **CSV and TSV**.

## Start here

| What you want to do | Guide |
| --- | --- |
| Try a small example | [Quick start](https://github.com/cfarkas/fun_proteomics/blob/main/docs/quick_start.md) |
| Watch interactive setup | [Walkthrough videos](https://cfarkas.github.io/fun_proteomics/) · [transcripts](https://github.com/cfarkas/fun_proteomics/blob/main/docs/videos.md) |
| Analyze your own data | [Set up a project](https://github.com/cfarkas/fun_proteomics/blob/main/docs/setup.md) |
| Install analysis tools | [Installation](https://github.com/cfarkas/fun_proteomics/blob/main/docs/installation.md) |
| Find matrices or understand comparisons | [Outputs](https://github.com/cfarkas/fun_proteomics/blob/main/docs/outputs.md) |
| Continue an earlier analysis | [Continue and recover](https://github.com/cfarkas/fun_proteomics/blob/main/docs/continuation.md) |
| Resolve an error | [Troubleshooting](https://github.com/cfarkas/fun_proteomics/blob/main/docs/troubleshooting.md) |

## Install

Linux and Python 3.11 or newer:

```bash
python3 -m venv proteomics-env
source proteomics-env/bin/activate
python -m pip install fun_proteomics
fun_proteomics system --path /data/my-study
fun_proteomics install --conda
fun_proteomics doctor
```

The Python package installs the launcher and reporting code. `install --conda` prepares the pinned analysis runtime and R packages in a user-writable cache; it does not use sudo. Registered DDA search archives remain separate. If you already have R and the analysis tools, see [reuse an installation](https://github.com/cfarkas/fun_proteomics/blob/main/docs/installation.md#reuse-an-installation).

## Set up your analysis

```bash
fun_proteomics setup --project /data/my-study
```

Keep the terminal open. Setup opens a local browser: choose your input, review sample groups, then click **Save configuration and check** and **Run / continue analysis**. Results and progress appear in the same page. Use the Browse buttons to select files on the computer running the analysis.

For terminal questions, including over SSH:

```bash
fun_proteomics setup --terminal --project /data/my-study --run
```

Both interfaces save `my-study/config/run.json`. To review first, omit `--run` in terminal setup. Then:

```bash
fun_proteomics check --config /data/my-study/config/run.json
fun_proteomics run --config /data/my-study/config/run.json
```

`check` validates inputs and explains the planned comparisons. `run` starts the saved analysis or continues it. Intensity comparisons assume independent biological replicates; confirm the design during setup.

## Find results and continue

Open **`my-study/START_HERE.html`** for results or **`my-study/MATRICES.html`** for CSV/TSV downloads. Detailed deliverables live in `my-study/results/final_results/`.

```bash
fun_proteomics --continue /data/my-study
```

Completed native results are verified and reused. Incomplete reports are rebuilt from verified inputs; raw workflows resume their saved Nextflow session. Existing runs from earlier releases also accept `--continue /path/to/run`. [Integrity checks and recovery details](https://github.com/cfarkas/fun_proteomics/blob/main/docs/continuation.md).

Native Python and direct Nextflow remain supported. See [advanced commands](https://github.com/cfarkas/fun_proteomics/blob/main/docs/advanced_reference.md) for raw JSON configurations, the advanced raw editor, phosphoproteomics, DDA counts, and specialist settings.

MIT licensed. Maintained by [cfarkas](https://github.com/cfarkas). The setup/check/run experience follows [OncoTracer](https://github.com/cfarkas/oncotracer).
