Machine Learning Thesis Defense

  • Remote Access Enabled
  • Virtual Presentation
  • CHIEH LIN
  • Ph.D. Student
  • Machine Learning Department
  • Carnegie Mellon University
Thesis Orals

Probabilistic Single Cell Lineage Tracing

Cell lineage tracing is a long-standing open problem in biology. To solve this problem, new technologies that can profile single-cells have been introduced in the last decade. Currently, studies attempt to construct lineage relationships using time-series single-cell RNA sequencing (scRNA-Seq) data or by utilizing artificial mutations for marking cells. The former studies rely on pseudo-time ordering which suffers from shortcomings that can impact their accuracy. The latter often apply phylogeny-based methods which often lead to hundreds of candidate trees. There is no current method to combine single-cell lineage trees from different individuals of the same organism to reconstruct a single invariant lineage for the same species. In this thesis, we present a set of machine learning models that focus on reconstructing single-cell lineages. We developed a probabilistic model based on Continuous-State Hidden Markov Model (CSHMM) to reconstruct trajectories and branchings from time series scRNA-Seq data. The model is then extended by learning the dynamics of regulatory interactions that take place during the process being studied (CSHMM-TF). We next present a method that integrates sequence and expression data, In addition, we developed LinTIMaT, a statistical model for reconstructing single-cell lineage trees using both artificial mutations and scRNA-Seq data and for constructing a general invariant lineage tree from multiple cell lineage trees of the same species. Finally, we apply CSHMM to a new dataset and show that it is capable of reconstructing lineage relationships and provides important novel insights for studying lung development.

Thesis Committee members:
Ziv Bar-Joseph (Chair)
Jian Ma
Roni Rosenfeld
Darrell Kotton (Boston University)
Killian Hurley (Boston University) 

Additional Thesis  Document

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