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Activity Number: 123 - Unraveling Tissue Heterogeneity for Analyzing Omics Data in Cancer Research
Type: Topic Contributed
Date/Time: Monday, August 3, 2020 : 1:00 PM to 2:50 PM
Sponsor: WNAR
Abstract #311158
Title: Adjusting for Handling Effects in Transcriptomics Data for Tumor Subtyping
Author(s): Li-Xuan Qin*
Companies: Memorial Sloan Kettering Cancer Center
Keywords: transcriptomics ; preprocessing ; normalization ; subtyping

Data normalization is an important preprocessing step for transcriptomics data containing unwanted data variation due to experimental handling. There has been a critical yet over-looked disconnection between the use of data normalization and the goals of subsequent analysis: on one hand, methods for data normalization that have been developed for group comparison frequently encounter ‘off-label’ use for other analysis goals such as sample subtyping; on the other hand, analysis are often performed on normalized data neglecting potential normalization ‘side-effects’ such as over-compressed data variability. A bridge between these two is made possible by a unique pair of microRNA array datasets on the same set of tumor tissue samples that were collected at Memorial Sloan Kettering Cancer Center. In this talk, I will illustrate the use of this dataset pair to study the impact of data normalization on the development of molecular signature for tumor subtyping.

Authors who are presenting talks have a * after their name.

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