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Although scRNA-seq technology has gained further capability to capture differential information at the cellular level compared to earlier transcriptome analysis methods including bulk RNA-seq, the cross-cellular technical errors arising from its data acquisition phase and other limitations provide challenges for researchers to maintain a balance between data pre-processing and information retention. Based on this, several relatively mature schemes including t-SNE, PCA, and multiple algorithm combinations on data dimension reduction was explored and tested in this report, and evaluated the accuracy obtained by machine-learning-based classifiers for cell classification tasks as a base metric for comprehensive comparison and evaluation.
This is the pipeline for large-scale, cell identification task from the beginning of raw data to the final classification. a. Labels + Reads Per Kilobase per Million mapped reads. b. Multiple dimension reduction methods with multiple dimensions applied. c. The specific implementation principle of the PCA + t-SNE combination algorithm. d. Visualization in both 2 & 3 dimensions and both with & without labels. e. Multiple classifiers with multiple parameters applied
The reprocessed dataset that supports the conclusion of this paper are publicly available online at https://scquery.cs.cmu.edu/processed_data/.
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- Yuetian Chen - Rensselaer Polytechnic Institute, 110 8th Street, Troy, NY, United States, 12180 (email: [email protected])
- Chenqi Xu - Southern University of Science and Technology, Shenzhen, China, 518055
- Yiyang Cao - The University of British Columbia, Vancouver, BC, Canada, V6T 1Z4
This research was undertaken as part of the CIS - Introduction to Machine Learning "Our Body" Project. Thanks to Prof. Ziv Bar-Joseph for his guidance and instruction in dataset pre-processing and paper refinement.