pimms-learn 0.4.2

Imputing (MS-based prote-) omics data using self supervised deep learning models.

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 functionality as a python package, an excutable workflow or simply in notebooks.

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:

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.

pip install pimms-learn

Then you can use the models on a pandas DataFrame with missing values. You can try this in the tutorial on Colab by uploading your data:

PIMMS was called vaep during development.
Before entire refactoring has been completed the imported package will be vaep.

Notebooks as scripts using papermill

If you want to run a model on your prepared data, you can run notebooks prefixed with 01_, i.e. project/01_*.ipynb after cloning the repository. Using jupytext also python percentage script versions are saved.

# 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

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

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 Snakefile_v2.smk (v2 of imputation workflow), specified in on configuration
• the Snakefile_ald_comparision workflow for differential analysis

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:

• config/alzheimer_study/config.yaml
• config/alzheimer_study/comparsion.yaml

To execute that workflow, follow the Setup instructions below and run the following command 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

Setup workflow and development environment

Setup comparison workflow

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

:warning: 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

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

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

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 = vaep.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

Available imputation methods

Packages either are based on this repository, or were referenced by NAGuideR (Table S1). From the brief description in the table the exact procedure is not always clear.

Method	Package	source	status	name
CF	pimms	pip		Collaborative Filtering
DAE	pimms	pip		Denoising Autoencoder
VAE	pimms	pip		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		k nearest neighbor imputation
SEQKNN	SeqKnn	tar file		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		Bayesian PCA missing value imputation
SVDMETHOD	pcaMethods	BIOCONDUCTOR		replace NA initially with zero, use k most significant eigenvalues using Singular Value Decomposition for imputation until convergence
LLS	pcaMethods	BIOCONDUCTOR		Local least squares imputation of a feature based on k most correlated features
MLE	norm	CRAN		Maximum likelihood estimation
QRILC	imputeLCMD	CRAN		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		replace NA with q-quantile minimum in a sample
MINPROB	imputeLCMD	CRAN		replace NA by random draws from q-quantile minimum centered distribution
IRM	VIM	CRAN		iterativ robust model-based imputation (one feature at at time)
IMPSEQ	rrcovNA	CRAN		Sequential imputation of missing values by minimizing the determinant of the covariance matrix with imputed values
IMPSEQROB	rrcovNA	CRAN		Sequential imputation of missing values using robust estimators
MICE-NORM	mice	CRAN		Multivariate Imputation by Chained Equations (MICE) using Bayesian linear regression
MICE-CART	mice	CRAN		Multivariate Imputation by Chained Equations (MICE) using regression trees
TRKNN	-	script		truncation k-nearest neighbor imputation
RF	missForest	CRAN		Random Forest imputation (one feature at a time)
PI	-	-		Downshifted normal distribution (per sample)
GSIMP	-	script		QRILC initialization and iterative Gibbs sampling with generalized linear models (glmnet)
MSIMPUTE	msImpute	BIOCONDUCTOR		Missing at random algorithm using low rank approximation
MSIMPUTE_MNAR	msImpute	BIOCONDUCTOR		Missing not at random algorithm using low rank approximation
grr	DreamAI	-	Fails to install	Rigde regression
GMS	GMSimpute	tar file	Fails on Windows	Lasso model