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Application and Development of Novel Methods for Pathway Analysis and Visualization of the LINCS L1000 Dataset

White, Shana
http://orcid.org/0000-0001-9497-0849
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1623241379918016

Abstract Details

2021, PhD, University of Cincinnati, Medicine: Biostatistics (Environmental Health).
The LINCS L1000 dataset is a large-scale compendium that contains records of the cell line specific transcriptional effects of cellular perturbation that was established to provide mechanistic and circuit-level insights with regard to cancer biology. This undertaking is a scaled-up version of the Connectivity Map (CMap) project whose goal was to connect transcriptional signatures of the downstream effects of genetic and small-molecule perturbations in a high-throughput yet cost-effective manner. This was accomplished by profiling a reduced representation of the human transcriptome – nearly 1,000 landmark transcripts whose expression is predictive of roughly 80% of non-measured genes. Whereas the choice to measure a subset of the transcriptome was primarily cost-based, reducing the representation of transcriptional data is a common method for amplifying the signal amidst the noisy background of large datasets. It can also be a valuable tool for making data amenable to a variety of bioinformatics-based analyses, for example, when lists of genes and their direction of regulation is considered based on continuously valued measurements subjected to a significance-based threshold. In the work presented in this document, we subject the records contained in the L1000 dataset to a thresholding procedure and explore how connections between over 2,000 common genetic perturbations differ between a core set of seven cancer cell lines. Specifically, we frame the connections in the context of edges between nodes in a novel adaptation of pathway-level analysis. We begin by conducting a simulation study in order to interrogate the data-generating mechanism best suited to reproduce our data of interest with the least amount of bias. This will be followed by a power analysis to assess the appropriate threshold for edge-based measurements for our dataset. Then, we will demonstrate how these measurements can be incorporated into the topology of cellular signaling pathways and introduce an R Bioconductor package that easily integrates this type of data into pathways from the Kyoto Encyclopedia of Genes and Genomes or KEGG – one of the most widely known online repositories for biological pathways. Finally, we will conduct an edge set enrichment analysis of our data that applies the well-known methodology of gene set enrichment analysis to this novel edge-data type.
Mario Medvedovic, Ph.D. (Committee Chair)
Marepalli Rao, Ph.D. (Committee Member)
John Reichard, PharmD Ph.D. (Committee Member)
Heidi Sucharew, Ph.D. (Committee Member)
200 p.
Biostatistics
Pathway Analysis; Cell Signaling; Cancer Cell Lines

Recommended Citations

Citations

  • White, S. (2021). Application and Development of Novel Methods for Pathway Analysis and Visualization of the LINCS L1000 Dataset [Doctoral dissertation, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1623241379918016

    APA Style (7th edition)

  • White, Shana. Application and Development of Novel Methods for Pathway Analysis and Visualization of the LINCS L1000 Dataset. 2021. University of Cincinnati, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1623241379918016.

    MLA Style (8th edition)

  • White, Shana. "Application and Development of Novel Methods for Pathway Analysis and Visualization of the LINCS L1000 Dataset." Doctoral dissertation, University of Cincinnati, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1623241379918016

    Chicago Manual of Style (17th edition)

ucin1623241379918016
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This open access ETD is published by University of Cincinnati and OhioLINK.