pimms-learn 0.0.1

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

PIMMS

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 also a british summer drink)

The pre-print is available on biorxiv.

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

We provide functionality as a python package and excutable workflows and notebooks under the project folder, inclduing an example.

The workflows folder contains snakemake workflows used for rawfile data processing, both for running MaxQuant over a large set of HeLa raw files and ThermoRawFileParser on a list of raw files to extract their meta data.

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. Using jupytext also python percentage script versions are saved.

cd project # project folder as pwd
papermill 01_0_split_data.ipynb --help-notebook
papermill 01_1_train_vae.ipynb --help-notebook

Misstyped argument names won't throw an error when using papermill

Outlook

We also plan to provide functionality and examples to interactive use of the models developed in PIMMS.

Setup

The package is not yet available as a standalone software on pypi. Currently we use conda (or mamba) and pip to setup the 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: use environment_m1.yaml where cudatoolkit is removed.

conda env create -n pimms -f environment_m1.yml # slower
mamba env create -n pimms -f environment_m1.yml # faster, less then 5mins

If on Windows: use environment_win.yaml where two R-Bioconductor R-packages (see note bolow) are removed as no binaries are available for Windows. You will need to install these manually afterwards if you want to use methods implemented in R.

Note: Turns out that installing dependencies partly by conda and partly manuaelly using BiocManager is not working.

conda env create -n pimms -f environment_win.yml # slower
mamba env create -n pimms -f environment_win.yml # faster, less then 5mins
# Then if R packages are needed, they are installed on the fly for Windows.
# Could be used as well for MacOS or Linux.

Trouble shoot your R installation by opening jupyter lab

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

Run Demo

Change to the project folder and see it's README

Currently there are only notebooks and scripts under project, but shared functionality will be added under vaep folder-package: This can then be imported using import vaep. See vaep/README.md

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
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 start 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)
grr	DreamAI	-	Fails to install	Rigde regression
GMS	GMSimpute	tar file	Fails on Windows	Lasso model