Integrating Data Clustering and Visualization for the Analysis of 3D Gene Expression Data

The recent development of methods for extracting precise measurements of spatial gene expression patterns from three-dimensional (3D) image data opens the way for new analyses of the complex gene regulatory networks controlling animal development. We present an integrated visualization and analysis framework that supports user-guided data clustering to aid exploration of these new complex datasets. The interplay of data visualization and clustering-based data classification leads to improved visualization and enables a more detailed analysis than previously possible. We discuss (i) integration of data clustering and visualization into one framework; (ii) application of data clustering to 3D gene expression data; (iii) evaluation of the number of clusters k in the context of 3D gene expression clustering; and (iv) improvement of overall analysis quality via dedicated post-processing of clustering results based on visualization. We discuss the use of this framework to objectively define spatial pattern boundaries and temporal profiles of genes and to analyze how mRNA patterns are controlled by their regulatory transcription factors

Data Science Education Repository

The Data Science Education Repository is a Community of Practice repository hosted by UC Berkeley that provides a space for data science education instructors, researchers, and scholars to share materials and showcase their work. Data science education materials can be utilized in the development of curriculum and in education research

Integrating Data Clustering and Visualization for the Analysis of 3D Gene Expression Data

The recent development of methods for extracting precise measurements of spatial gene expression patterns from three-dimensional (3D) image data opens the way for new analyses of the complex gene regulatory networks controlling animal development. We present an integrated visualization and analysis framework that supports user-guided data clustering to aid exploration of these new complex datasets. The interplay of data visualization and clustering-based data classification leads to improved visualization and enables a more detailed analysis than previously possible. We discuss (i) integration of data clustering and visualization into one framework; (ii) application of data clustering to 3D gene expression data; (iii) evaluation of the number of clusters k in the context of 3D gene expression clustering; and (iv) improvement of overall analysis quality via dedicated post-processing of clustering results based on visualization. We discuss the use of this framework to objectively define spatial pattern boundaries and temporal profiles of genes and to analyze how mRNA patterns are controlled by their regulatory transcription factors

cu-mkp/m-k-manuscript-data (All Versions)

v2.0.4 2023-07-21 Twentieth post-public release of Feb 6, 2020. This is the second preparation of a build by M&K team (rather than Performant) for production. Along with https://github.com/cu-mkp/edition-webpages/releases/tag/v3.0.3 (same as last time) and https://github.com/cu-mkp/making-knowing-edition/releases/tag/v1.2.1 (same as last time) Added Marc Smith essay, completed some typo changes, updated derivatives. Notably: #2063 #2064 #2065 #2077 #2079 #2078 #2073 Known issues: See https://github.com/cu-mkp/m-k-manuscript-data/issues for other issues and feature request