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PIMMS stands for Proteomics Imputation Modeling Mass Spectrometry and is a hommage to our dear British friends who are missing as part of the EU for far too long already (Pimms is a British summer drink).

We published the work in Nature Communications as open access:

Webel, H., Niu, L., Nielsen, A.B. et al.
Imputation of label-free quantitative mass spectrometry-based proteomics data using self-supervised deep learning.
Nat Commun 15, 5405 (2024).
https://doi.org/10.1038/s41467-024-48711-5

We provide new functionality as a python package for simple use (in notebooks) and a workflow for comparsion with other methdos.

For any questions, please open an issue or contact me directly.

Getting started

The models can be used with the scikit-learn interface in the spirit of other scikit-learn imputers. You can try this using our tutorial in colab:

open in Colab

It uses the scikit-learn interface. The PIMMS models in the scikit-learn interface can be executed on the entire data or by specifying a valdiation split for checking training process. In our experiments overfitting wasn't a big issue, but it's easy to check.

Install Python package

For interactive use of the models provided in PIMMS, you can use our python package pimms-learn. The interface is similar to scikit-learn. The package is then availabe as pimmslearn for import in your Python session.

pip install pimms-learn
# import pimmslearn # in your python script

The most basic use for imputation is using a DataFrame.

import numpy as np
import pandas as pd
from pimmslearn.sklearn.ae_transformer import AETransformer
from pimmslearn.sklearn.cf_transformer import CollaborativeFilteringTransformer

fn_intensities = ('https://raw.githubusercontent.com/RasmussenLab/pimms/main/'
                  'project/data/dev_datasets/HeLa_6070/protein_groups_wide_N50.csv')
index_name = 'Sample ID'
column_name = 'protein group'
value_name = 'intensity'

df = pd.read_csv(fn_intensities, index_col=0)
df = np.log2(df + 1)

df.index.name = index_name  # already set
df.columns.name = column_name  # not set due to csv disk file format

# df # see details below to see a preview of the DataFrame

# use the Denoising or Variational Autoencoder
model = AETransformer(
    model='DAE', # or 'VAE'
    hidden_layers=[512,],
    latent_dim=50, # dimension of joint sample and item embedding
    batch_size=10,
)
model.fit(df,
          cuda=False,
          epochs_max=100,
          )
df_imputed = model.transform(df)

# or use the collaborative filtering model
series = df.stack()
series.name = value_name  # ! important
model = CollaborativeFilteringTransformer(
    target_column=value_name,
    sample_column=index_name,
    item_column=column_name,
    n_factors=30, # dimension of separate sample and item embedding
    batch_size = 4096
)
model.fit(series, cuda=False, epochs_max=20)
df_imputed = model.transform(series).unstack()
🔍 see log2 transformed DataFrame

First 10 rows and 10 columns. notice that the indices are named:

Sample ID AAAS AACS AAMDC AAMP AAR2 AARS AARS2 AASDHPPT AATF ABCB10
protein group
2019_12_18_14_35_Q-Exactive-HF-X-Orbitrap_6070 28.3493 26.1332 nan 26.7769 27.2478 32.1949 27.1526 27.8721 28.6025 26.1103
2019_12_19_19_48_Q-Exactive-HF-X-Orbitrap_6070 27.6574 25.0186 24.2362 26.2707 27.2107 31.9792 26.5302 28.1915 27.9419 25.7349
2019_12_20_14_15_Q-Exactive-HF-X-Orbitrap_6070 28.3522 23.7405 nan 27.0979 27.3774 32.8845 27.5145 28.4756 28.7709 26.7868
2019_12_27_12_29_Q-Exactive-HF-X-Orbitrap_6070 26.8255 nan nan 26.2563 nan 31.9264 26.1569 27.6349 27.8508 25.346
2019_12_29_15_06_Q-Exactive-HF-X-Orbitrap_6070 27.4037 26.9485 23.8644 26.9816 26.5198 31.8438 25.3421 27.4164 27.4741 nan
2019_12_29_18_18_Q-Exactive-HF-X-Orbitrap_6070 27.8913 26.481 26.3475 27.8494 26.917 32.2737 nan 27.4041 28.0811 nan
2020_01_02_17_38_Q-Exactive-HF-X-Orbitrap_6070 25.4983 nan nan nan nan 30.2256 nan 23.8013 25.1304 nan
2020_01_03_11_17_Q-Exactive-HF-X-Orbitrap_6070 27.3519 nan 24.4331 25.2752 24.8459 30.9793 nan 24.893 25.3238 nan
2020_01_03_16_58_Q-Exactive-HF-X-Orbitrap_6070 27.6197 25.6238 23.5204 27.1356 25.9713 31.4154 25.3596 25.1191 25.75 nan
2020_01_03_20_10_Q-Exactive-HF-X-Orbitrap_6070 27.2998 nan 25.6604 27.7328 26.8965 31.4546 25.4369 26.8135 26.2008 nan
...

For hints on how to add validation (and potentially test data) to use early stopping, see the tutorial: open in Colab

PIMMS comparison workflow and differential analysis workflow

The PIMMS comparison workflow is a snakemake workflow that runs the all selected PIMMS models and R-models on a user-provided dataset and compares the results. An example for a publickly available Alzheimer dataset on the protein groups level is re-built regularly and available at: rasmussenlab.org/pimms

It is built on top of

The associated notebooks are index with 01_* for the comparsion workflow and 10_* for the differential analysis workflow. The project folder can be copied separately to any location if the package is installed. It's standalone folder. It's main folders are:

# project folder:
project
│   README.md # see description of notebooks and hints on execution in project folder
|---config # configuration files for experiments ("workflows")
|---data # data for experiments
|---runs # results of experiments
|---src # source code or binaries for some R packges
|---tutorials # some tutorials for libraries used in the project
|---workflow # snakemake workflows

To re-execute the entire workflow locally, have a look at the configuration files for the published Alzheimer workflow:

To execute that workflow, follow the Setup instructions below and run the following commands in the project folder:

# being in the project folder
snakemake -s workflow/Snakefile_v2.smk --configfile config/alzheimer_study/config.yaml -p -c1 -n # one core/process, dry-run
snakemake -s workflow/Snakefile_v2.smk --configfile config/alzheimer_study/config.yaml -p -c2 # two cores/process, execute
# after imputation workflow, execute the comparison workflow
snakemake -s workflow/Snakefile_ald_comparison.smk --configfile config/alzheimer_study/comparison.yaml -p -c1
# If you want to build the website locally: https://www.rasmussenlab.org/pimms/
pip install .[docs]
pimms-setup-imputation-comparison -f project/runs/alzheimer_study/
pimms-add-diff-comp -f project/runs/alzheimer_study/ -sf_cp project/runs/alzheimer_study/diff_analysis/AD
cd project/runs/alzheimer_study/
sphinx-build -n --keep-going -b html ./ ./_build/
# open ./_build/index.html

Notebooks as scripts using papermill

The above workflow is based on notebooks as scripts, which can then be rendered as html files.'Using jupytext also python percentage script versions are saved.

If you want to run a specific model on your data, you can run notebooks prefixed with 01_, i.e. project/01_*.ipynb after creating hte appropriate data split. Start by cloning the repository.

# navigat to your desired folder
git clone https://github.com/RasmussenLab/pimms.git # get all notebooks
cd project # project folder as pwd
# pip install pimms-learn papermill # if not already installed
papermill 01_0_split_data.ipynb --help-notebook
papermill 01_1_train_vae.ipynb --help-notebook

⚠️ Mistyped argument names won't throw an error when using papermill, but a warning is printed on the console thanks to my contributions:)

Setup workflow and development environment

Either (1) install one big conda environment based on an environment file, or (2) install packages using a mix of conda and pip, or (3) use snakemake separately with rule specific conda environments.

Setup comparison workflow (1)

The core funtionality is available as a standalone software on PyPI under the name pimms-learn. However, running the entire snakemake workflow in enabled using conda (or mamba) and pip to setup an analysis environment. For a detailed description of setting up conda (or mamba), see instructions on setting up a virtual environment.

Download the repository:

git clone https://github.com/RasmussenLab/pimms.git
cd pimms

Using conda (or mamba), install the dependencies and the package in editable mode

# from main folder of repository (containing environment.yml)
conda env create -n pimms -f environment.yml # slower
mamba env create -n pimms -f environment.yml # faster, less then 5mins

If on Mac M1, M2 or having otherwise issue using your accelerator (e.g. GPUs): Install the pytorch dependencies first, then the rest of the environment:

Install pytorch first (2)

⚠️ We currently see issues with some installations on M1 chips. A dependency for one workflow is polars, which causes the issue. This should be fixed now for general use by delayed import of mrmr-selection in njab. If you encounter issues, please open an issue.

Check how to install pytorch for your system here.

  • select the version compatible with your cuda version if you have an nvidia gpu or a Mac M-chip.
conda create -n pimms python=3.9 pip
conda activate pimms
# Follow instructions on https://pytorch.org/get-started: 
# CUDA is not available on MacOS, please use default package
# pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
conda install pytorch::pytorch torchvision torchaudio fastai -c pytorch -c fastai -y
pip install pimms-learn
pip install jupyterlab papermill # use run notebook interactively or as a script

cd project
# choose one of the following to test the code
jupyter lab # open 04_1_train_pimms_models.ipynb
papermill 04_1_train_pimms_models.ipynb 04_1_train_pimms_models_test.ipynb # second notebook is output
python 04_1_train_pimms_models.py # just execute the code

Let Snakemake handle installation (3)

If you only want to execute the workflow, you can use snakemake to build the environments for you:

Install snakemake e.g. using the provided snakemake_env.yml file as used in this workflow.

⚠️ Snakefile workflow for imputation v1 only support that atm.

snakemake -p -c1 --configfile config/single_dev_dataset/example/config.yaml --use-conda -n # dry-run
snakemake -p -c1 --configfile config/single_dev_dataset/example/config.yaml --use-conda # execute with one core

Troubleshooting

Trouble shoot your R installation by opening jupyter lab

# in projects folder
jupyter lab # open 01_1_train_NAGuideR.ipynb

Run example on HeLa data

Change to the project folder and see it's README You can subselect models by editing the config file: config.yaml file.

conda activate pimms # activate virtual environment
cd project # go to project folder
pwd # so be in ./pimms/project
snakemake -c1 -p -n # dryrun demo workflow, potentiall add --use-conda
snakemake -c1 -p

The demo will run an example on a small data set of 50 HeLa samples (protein groups):

  1. it describes the data and does create the splits based on the example data
    • see 01_0_split_data.ipynb
  2. it runs the three semi-supervised models next to some default heuristic methods
    • see 01_1_train_collab.ipynb, 01_1_train_dae.ipynb, 01_1_train_vae.ipynb
  3. it creates an comparison
    • see 01_2_performance_plots.ipynb

The results are written to ./pimms/project/runs/example, including html versions of the notebooks for inspection, having the following structure:

│   01_0_split_data.html
│   01_0_split_data.ipynb
│   01_1_train_collab.html
│   01_1_train_collab.ipynb
│   01_1_train_dae.html
│   01_1_train_dae.ipynb
│   01_1_train_vae.html
│   01_1_train_vae.ipynb
│   01_2_performance_plots.html
│   01_2_performance_plots.ipynb
│   data_config.yaml
│   tree_folder.txt
|---data
|---figures
|---metrics
|---models
|---preds

The predictions of the three semi-supervised models can be found under ./pimms/project/runs/example/preds. To combine them with the observed data you can run

# ipython or python session
# be in ./pimms/project
folder_data = 'runs/example/data'
data = pimmslearn.io.datasplits.DataSplits.from_folder(
    folder_data, file_format='pkl')
observed = pd.concat([data.train_X, data.val_y, data.test_y])
# load predictions for missing values of a certain model
model = 'vae'
fpath_pred = f'runs/example/preds/pred_real_na_{model}.csv '
pred = pd.read_csv(fpath_pred, index_col=[0, 1]).squeeze()
df_imputed = pd.concat([observed, pred]).unstack()
# assert no missing values for retained features
assert df_imputed.isna().sum().sum() == 0
df_imputed

⚠️ The imputation is simpler if you use the provide scikit-learn Transformer interface (see Tutorial).

Available imputation methods

Packages either are based on this repository, were referenced by NAGuideR or released recently. From the brief description in this table the exact procedure is not always clear.

Method Package source links name
CF pimms pip paper Collaborative Filtering
DAE pimms pip paper Denoising Autoencoder
VAE pimms pip paper Variational Autoencoder
ZERO - - - replace NA with 0
MINIMUM - - - replace NA with global minimum
COLMEDIAN e1071 CRAN - replace NA with column median
ROWMEDIAN e1071 CRAN - replace NA with row median
KNN_IMPUTE impute BIOCONDUCTOR docs k nearest neighbor imputation
SEQKNN SeqKnn tar file paper Sequential k- nearest neighbor imputation
starts with feature with least missing values and re-use imputed values for not yet imputed features
BPCA pcaMethods BIOCONDUCTOR paper Bayesian PCA missing value imputation
SVDMETHOD pcaMethods BIOCONDUCTOR paper replace NA initially with zero, use k most significant eigenvalues using Singular Value Decomposition for imputation until convergence
LLS pcaMethods BIOCONDUCTOR paper Local least squares imputation of a feature based on k most correlated features
MLE norm CRAN Maximum likelihood estimation
QRILC imputeLCMD CRAN paper quantile regression imputation of left-censored data, i.e. by random draws from a truncated distribution which parameters were estimated by quantile regression
MINDET imputeLCMD CRAN paper replace NA with q-quantile minimum in a sample
MINPROB imputeLCMD CRAN paper replace NA by random draws from q-quantile minimum centered distribution
IRM VIM CRAN paper iterativ robust model-based imputation (one feature at at time)
IMPSEQ rrcovNA CRAN paper Sequential imputation of missing values by minimizing the determinant of the covariance matrix with imputed values
IMPSEQROB rrcovNA CRAN paper Sequential imputation of missing values using robust estimators
MICE-NORM mice CRAN paper Multivariate Imputation by Chained Equations (MICE) using Bayesian linear regression
MICE-CART mice CRAN paper Multivariate Imputation by Chained Equations (MICE) using regression trees
TRKNN - script paper truncation k-nearest neighbor imputation
RF missForest CRAN paper Random Forest imputation (one feature at a time)
PI - - Downshifted normal distribution (per sample)
GSIMP - script paper QRILC initialization and iterative Gibbs sampling with generalized linear models (glmnet) - slow
MSIMPUTE msImpute BIOCONDUCTOR paper Missing at random algorithm using low rank approximation
MSIMPUTE_MNAR msImpute BIOCONDUCTOR paper Missing not at random algorithm using low rank approximation

DreamAI and GMSimpute are not available for installation on Windows or failed to install.