Open AccessProceedings Article
SLICER: Inferring Branched, Nonlinear Cellular Trajectories from Single Cell RNA-seq Data.
Joshua D. Welch,Ziqing Liu,Li Wang,Junjie Lu,Paul H. Lerou,Jeremy E. Purvis,Li Qian,Alexander J. Hartemink,Jan F. Prins +8 more
- pp 239-240
TLDR
SLICER (Selective Locally Linear Inference of Cellular Expression Relationships) as mentioned in this paper can infer highly nonlinear trajectories, select genes without prior knowledge of the process, and automatically determine the location and number of branches and loops.Abstract:
Single cell experiments provide an unprecedented opportunity to reconstruct a sequence of changes in a biological process from individual “snapshots” of cells. However, nonlinear gene expression changes, genes unrelated to the process, and the possibility of branching trajectories make this a challenging problem. We develop SLICER (Selective Locally Linear Inference of Cellular Expression Relationships) to address these challenges. SLICER can infer highly nonlinear trajectories, select genes without prior knowledge of the process, and automatically determine the location and number of branches and loops. SLICER recovers the ordering of points along simulated trajectories more accurately than existing methods. We demonstrate the effectiveness of SLICER on previously published data from mouse lung cells and neural stem cells.read more
Citations
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SPAdes, a new genome assembly algorithm and its applications to single-cell sequencing ( 7th Annual SFAF Meeting, 2012)
TL;DR: SPAdes as mentioned in this paper is a new assembler for both single-cell and standard (multicell) assembly, and demonstrate that it improves on the recently released E+V-SC assembler and on popular assemblers Velvet and SoapDeNovo (for multicell data).
Dissertation
Learning and inference with single cell data
TL;DR: Learning and inference with single cell data is studied in the context of drug discovery and its applications in medicine, where single cell discovery and inference are concerned.
Posted ContentDOI
Scalable preprocessing for sparse scRNA-seq data exploiting prior knowledge
TL;DR: UNCURL, a preprocessing framework based on non-negative matrix factorization for scRNA-seq data, that is able to handle varying sampling distributions, scales to very large cell numbers and can incorporate prior knowledge is presented.
Dissertation
Boolean Modeling of Developmental Gene Networks in T Cell Progenitor Differentiation
TL;DR: In this paper, computational Boolean network (BN) is applied to simulate systems-level dynamics of T cell lineage differentiation of hematopoietic progenitors via gene regulatory networks.
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