README.md

---
license: cc-by-4.0
tags:
- biology
- PPIs
pretty_name: >-
  hu.MAP3.0: Atlas of human protein complexes by integration of > 25,000
  proteomic experiments. 
repo: https://github.com/KDrewLab/huMAP3.0_analysis
---
# hu.MAP3.0: Atlas of human protein complexes by integration of > 25,000 proteomic experiments. 
Proteins interact with each other and organize themselves into macromolecular machines (ie. complexes) 
to carry out essential functions of the cell. We have a good understanding of a few complexes such as 
the proteasome and the ribosome but currently we have an incomplete view of all protein complexes as 
well as their functions. The hu.MAP attempts to address this lack of understanding by integrating several
large scale protein interaction datasets to obtain the most comprehensive view of protein complexes. 
In hu.MAP 3.0 we integrated large scale affinity purification mass spectrometry (AP/MS) datasets from Bioplex, 
Bioplex2.0, Bioplex3.0, Boldt et al. and Hein et al., large scale biochemical fractionation data (Wan et al.), 
proximity labeling data (Gupta et al., Youn et al.), and RNA hairpin pulldown data (Treiber et al.) to produce 
a complex map with over 15k complexes. 


## Funding

  NIH R00, NSF/BBSRC
  
## Citation

    Samantha N. Fischer, Erin R Claussen, Savvas Kourtis, Sara Sdelci, Sandra Orchard, Henning Hermjakob, Georg Kustatscher, Kevin Drew hu.MAP3.0: Atlas of human protein complexes by integration of > 25,000 proteomic experiments BioRxiv https://doi.org/10.1101/2024.10.11.617930

## References

    Kevin Drew, John B. Wallingford, Edward M. Marcotte hu.MAP 2.0: integration of over 15,000 proteomic experiments builds a global compendium of human multiprotein assemblies Mol Syst Biol (2021)17:e10016. https://doi.org/10.15252/msb.202010016
    Kevin Drew, Chanjae Lee, Ryan L Huizar, Fan Tu, Blake Borgeson, Claire D McWhite, Yun Ma, John B Wallingford, Edward M Marcotte Integration of over 9,000 mass spectrometry experiments builds a global map of human protein complexes. Molecular Systems Biology (2017) 13, 932. DOI 10.15252/msb.20167490
    Huttlin et al. Dual proteome-scale networks reveal cell-specific remodeling of the human interactome Cell. 2021 May 27;184(11):3022-3040.e28. doi: 10.1016/j.cell.2021.04.011.
    Huttlin et al. Architecture of the human interactome defines protein communities and disease networks. Nature. 2017 May 25;545(7655):505-509. DOI: 10.1038/nature22366.
    Treiber et al. A Compendium of RNA-Binding Proteins that Regulate MicroRNA Biogenesis.. Mol Cell. 2017 Apr 20;66(2):270-284.e13. doi: 10.1016/j.molcel.2017.03.014.
    Boldt et al. An organelle-specific protein landscape identifies novel diseases and molecular mechanisms. Nat Commun. 2016 May 13;7:11491. doi: 10.1038/ncomms11491.
    Youn et al. High-Density Proximity Mapping Reveals the Subcellular Organization of mRNA-Associated Granules and Bodies. Mol Cell. 2018 Feb 1;69(3):517-532.e11. doi: 10.1016/j.molcel.2017.12.020.
    Gupta et al. A Dynamic Protein Interaction Landscape of the Human Centrosome-Cilium Interface. Cell. 2015 Dec 3;163(6):1484-99. doi: 10.1016/j.cell.2015.10.065.
    Wan, Borgeson et al. Panorama of ancient metazoan macromolecular complexes. Nature. 2015 Sep 17;525(7569):339-44. doi: 10.1038/nature14877. Epub 2015 Sep 7.
    Hein et al. A human interactome in three quantitative dimensions organized by stoichiometries and abundances. Cell. 2015 Oct 22;163(3):712-23. doi: 10.1016/j.cell.2015.09.053. Epub 2015 Oct 22.
    Huttlin et al. The BioPlex Network: A Systematic Exploration of the Human Interactome. Cell. 2015 Jul 16;162(2):425-40. doi: 10.1016/j.cell.2015.06.043.
    Reimand et al. g:Profiler-a web server for functional interpretation of gene lists (2016 update). Nucleic Acids Res. 2016 Jul 8;44(W1):W83-9. doi: 10.1093/nar/gkw199.

## Associated code

  Code examples using the [hu.MAP 3.0 model](https://huggingface.co/sfisch/hu.MAP3.0_AutoGluon) and downstream analysis can be found on our
  [GitHub](https://github.com/KDrewLab/huMAP3.0_analysis)

# Usage

## Accessing the model
hu.MAP 3.0 was built using the auto-ML tool [AutoGluon](https://auto.gluon.ai/stable/index.html) and the [TabularPredictor](https://auto.gluon.ai/stable/api/autogluon.tabular.TabularPredictor.html)
module is used to train, test, and make predictions with the model. 

This can be downloaded using the following:

      $ pip install autogluon==0.4.0

Then it can be imported as:

    >>> from autogluon.tabular import TabularPredictor
Note that to perform operations with our model the **0.4.0 version** must be used

Our [trained model](https://huggingface.co/sfisch/hu.MAP3.0_AutoGluon) can be downloaded through Huggingface using [huggingface_hub](https://huggingface.co/docs/hub/index)

    >>> from huggingface_hub import snapshot_download

    >>> model_dir = snapshot_download(repo_id="sfisch/hu.MAP3.0_AutoGluon")

    >>> predictor = TabularPredictor.load(f"{model_dir}/huMAP3_20230503_complexportal_subset10kNEG_notScaled_accuracy")

## Using the training and test data
Both the train and test feature matrices can be loaded using the Huggingface [datasets](https://huggingface.co/docs/datasets/index) library.

This can be done from the command-line using:
    
      $ pip install datasets

When loading into Python use the following:

    >>> from datasets import load_dataset
    >>> dataset = load_dataset('sfisch/hu.MAP3.0')

Training and test feature matrices can then be accessed as separate objects:

    >>> train = dataset["train"].to_pandas()
    >>> test = dataset["test"].to_pandas()

Jupyter notebooks containing more in-depth examples of model training, testing, and generating predictions can be found on our [GitHub](https://github.com/KDrewLab/huMAP3.0_analysis/huMAP3.0_model_devel)

## Accessing full feature matrix and all test/train interaction/complex files
All other files, such as the full feature matrix, can be accessed via Huggingface_hub.

    >>> from huggingface_hub import hf_hub_download
    >>> full_file = hf_hub_download(repo_id="sfisch/hu.MAP3.0", filename='full/humap3_full_feature_matrix_20220625.csv.gz', repo_type='dataset')

This just provides the file for download. Depending on your workflow, if you wish to use as a pandas dataframe for example:

    >>> import pandas as pd
    >>> full_featmat = pd.read_csv(full_file, compression="gzip")

The other complex/interaction files can be downloaded in the same manner. The files within the 'reference_interactions' directory
contain the complexes split from [Complex Portal](https://www.ebi.ac.uk/complexportal) into test and training sets. Within that directory you
will also find the pairwise protein interactions that were used as positive and negative interactions for both the test and training sets.

## Dataset card authors
Samantha Fischer (sfisch6@uic.edu)