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Labs

Lab instructions

Clone the course repository

Retrieve the entire repository including all datasets and notebooks.

cd ~/Desktop/course
git clone git@github.com:NBISweden/workshop_omics_integration.git .

Inside you will find the following folders:

  • environments/ - all conda environments necessary for running notebooks
  • session_preprocessing/ - data pre-processing, prior to course start
  • session_ml/ - the machine learning sessions, days 1 and 2
  • session_topology/ - the network topology analysis, day 3 morning
  • session_meta/ - network meta-analysis, day 3 afternoon
  • session_nmf/ - session on matrix factorization, SNF and recommender systems, day 4 morning
  • session_gems/ - the genome-scale modeling session, day 4 afternoon
  • session_gsa/ - gene set and reporter feature, day 5 morning
  • .../ - remaining folders not necessary for running any of the contents

You will need to create specific conda environments as indicated below.

Environments See the pre-course installation, specifically 3. Create and activate the environment.
Environments - we merged the environments for many notebooks down to 4 environments.

  • Data pre-processing (linux: env-preprocessing_linux.yaml, macOS: env-preprocessing.yaml)
  • Supervised Integration & Feature selection& (linux: env-ml_linux.yaml | macOS: env-ml.yaml)
  • Meta analysis (linux | macOS /session_meta/renv.lock)
  • All remaining notebooks (linux env-ml_nets_linux.yaml | MacOS env-ml_nets.yaml)

Alternatively, you can find smaller environments below.

Before the course: data pre-processing
  • Data pre-processing:
    • environment macOS: /environments/env-preprocessing.yaml
    • environment linux: /environments/env-preprocessing_linux.yaml
    • notebook (html): /session_preprocessing/preprocessing.ipynb
Day 1: Supervised and Unsupervised integration

In MacOSX you need to install XQuartz if you get errors related with rgl:

install.packages('session_ml/rgl_0.100.54.tgz', repos = NULL, type="source", dependencies = TRUE)

  • Supervised integration:

    • environment macOS: /environments/env-ml.yaml
    • environment linux: /environments/env-ml_linux.yaml
    • notebook (html): /session_ml/SupervisedOMICsIntegration/supervised_omics_integr_CLL/supervised_omics_integr_CLL.Rmd

  • Feature selection:

    • environment macOS: /environments/env-ml.yaml
    • environment linux: /environments/env-ml_linux.yaml
    • notebook (html): session_ml/FeatureSelectionIntegrOMICs/OmicsIntegration_FeatureSelection.Rmd

  • Unsupervised integration:

    • environment macOS: /environments/env-ml_day2.yaml
    • environment linux: /environments/env-ml_day2_linux.yaml
    • notebook (html): session_ml/UnsupervisedOMICsIntegration/UnsupervisedOMICsIntegration.Rmd
Day 2: Dimensionality reduction, deep learning, single-cell omics integration

After activating the environment as above, open the .Rmd below by launching Rstudio with rstudio & or the jupyter notebooks with jupyter-notebook.

  • Dimensionality reduction:

    • environment macOS: /environments/env-ml_day2.yaml
    • environment linux: /environments/env-ml_day2_linux.yaml
    • notebook (html): session_ml/DimReductSingleCell/OmicsIntegration_DimensionReduction.Rmd

  • UMAP for data integration:

    • Note that the data needs to be decompressed before running the notebook gunzip scRNAseq.csv.gz; gunzip scProteomics.csv.gz
    • environment macOS: /environments/env-ml_day2.yaml
    • environment linux: /environments/env-ml_day2_linux.yaml
    • notebook (html): session_ml/UMAP_DataIntegration/UMAP_DataIntegration.ipynb

  • Deep learning:

    • environment macOS: /environments/env-ml_day2.yaml
    • environment linux: /environments/env-ml_day2_linux.yaml
    • notebook (html): session_ml/DeepLearningDataIntegration/DeepLearningDataIntegration.ipynb

  • Single cell:

    • environment macOS: /environments/env-ml_day2.yaml
    • environment linux: /environments/env-ml_day2_linux.yaml
    • notebook (html): session_ml/SingleCell/SingleCell_OmicsIntegration.Rmd
Day 3: Network topology

  • Network topology:

    • environment macOS: /environments/env-merged_nets.yaml
    • environment linux: /environments/env-merged_nets_linux.yaml
    • notebook (html): /session_topology/lab.ipynb

  • Meta analyses:

    • environment macOS:/session_meta/renv.lock
    • environment linux:/session_meta/renv.lock
    • notebook (html): /session_meta/lab_meta-analayses-v2.Rmd
Day 4: GEM structure and simulation with Cobrapy
  • Genome-scale metabolic modeling:
    • environment macOS: /environments/env-merged_nets.yaml
    • environment linux: /environments/env-merged_nets_linux.yaml
    • notebook (html): /session_gems/lab.ipynb
Day 5: Gene set and reporter analysis
  • Gene set analysis and reporter features:
    • environment macOS: /environments/env-merged_nets.yaml
    • environment linux: /environments/env-merged_nets_linux.yaml
    • notebook: (html): /session_gsa/GEM_GSA.Rmd