DREAM Single Cell Transcriptomics Challenge

• https://www.synapse.org/#!Synapse:syn15665609/wiki/582909

Team ThinNguyen's ranking: (https://www.life-science-alliance.org/content/lsa/3/11/e202000867.full.pdf, Figure 1)

• Sub challenge 1: 1^st
• Sub challenge 2: 2^nd
• Sub challenge 3: 2^nd

Leaderboards:

• https://dream-sctc.uni.lu/ranking/team_rankings.html
• https://dream-sctc.uni.lu/ranking/post_team_rankings.html
• https://www.synapse.org/#!Synapse:syn15665609/wiki/583247

Data:

• Data is freely available to Synapse users: ALL_files.zip at https://www.synapse.org/#!Synapse:syn16782362.
• Related data: https://github.com/thinng/exp2loc/blob/master/data.zip.

Python libraries needed:

• sklearn:
  • for popular machine learning methods, including those for feature selection, such as variance-based algorithms.
  • install: pip install scikit-learn
• skfeature:
  • for Multi-Cluster Feature Selection (MCFS) (Cai et al., 2010) and Nonnegative Discriminative Feature Selection (NDFS) (Li et al., 2012).
  • Cai, D., Zhang, C., and He, X. (2010). Unsupervised feature selection for multi-cluster data. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pages 333–342.
  • Li, Z., Yang, Y., Liu, J., Zhou, X., and Lu, H. (2012). Unsupervised feature selection using nonnegative spectral analysis. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 1026–1032.
  • install: pip install skfeature-chappers

Source is written in Python. It can be run in Jupyter Notebook.

• By sub challenge
  • sub_challenge_1.ipynb: Source code and step by step instructions for Sub challenge 1.
  • sub_challenge_2.ipynb: Source code and step by step instructions for Sub challenge 2.
  • sub_challenge_3.ipynb: Source code and step by step instructions for Sub challenge 3.
• You may also want to choose your own combination of feature selection and cell prediction methods through using all_sub_challenges.ipynb.
• You may also want to run all the combination of feature selection and cell prediction methods through using all_approaches_all_feature_selection.ipynb. Then you could score the performance of these combinations using evaluation.ipynb.