An Introduction to BioLayout Express3D

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Download the datasets used in the tutorial videos.

1.Introduction to data analysis – course overview (lecture)

Content:

  • biological analysis
  • interest in gene expression data
  • available data (there’s lots of it)
  • big data, big challenges

2.Introduction to networks (lecture)

Content:

  • network graphs of biological relationships
  • plotting the statistically improbable: principles of correlation (co-expression) networks

3. BioLayout Express3D – background and benefits (lecture)

Content:

  • BioLayout Express3D: one tool many applications
  • benefits
  • available data (there’s lots of it)
  • analysis pipeline for high dimensional data

4. BioLayout Express3D – getting started 1 (Practical Session)

Content:

  • simple input formats
  • BioLayout requirements
  • basic navigation in 3D (rotate, zoom, translate, select nodes)
  • edge weight display

5. BioLayout Express3D – getting started 2 (Practical Session)

Content:

  • simple input formats (cont.)
  • BioLayout interface – 2D (translate, zoom, move selected nodes)
  • graph layout

6. Introduction to data analysis – Expression data 1 (lecture)

Content:

  • high dimensional data analysis: Stage 1
  • getting the basics right: Sample annotation and ordering
  • Quality Control
  • the box plot
  • normalisation
  • sample similarity analysis – does the biology cluster
  • sample-to-sample correlation matrix

7. Introduction to data analysis – Expression data 2 (lecture)

Content:

  • patterns in data, the more samples the more possibility for differences
  • the Pearson correlation matrix
  • the basic principle of correlation matrix analysis

8. File preparation expression data analysis in BioLayout

Content:

  • microarray/RNA-seq gene expression data file format (.expression)
  • unique ID
  • sample ID
  • sample classes
  • gene classes
  • numerical data format

9. Network analysis of Expression data – sample-sample correlation graph 1 (Practical Session)

Example dataset 2 – GNF mouse tissue atlas dataset
Content:

  • minimum correlation setting
  • preprocessing; transpose data
  • graph topology

10. Network analysis of Expression data – sample-sample correlation graph 2 (Practical Session)

Example dataset 2 – GNF mouse tissue atlas dataset
Content:

  • MCL (Markov Cluster Algorithm), inflation value, smallest cluster allowed, class viewer
  • open the last graph

11. Network analysis of Expression data – (Practical Session)

Example dataset 2 – time-course experiment – Part 1
Content:

  • NHDF data set
  • graph layout, component size
  • layout options
  • clustering
  • Class viewer, view class sets

12. Network analysis of Expression data – (Practical Session)

Example dataset 2 – time-course experiment – Part 2
Content:

  • Class viewer, cluster
  • time points and replicas
  • multiple classes
  • exploit and understand the graph structure
  • animation control, node size

13. Network analysis of Expression data – (Practical Session)

Example dataset 3 – pig tissue atlas data (part 1)
Content:

  • GNF data set(pig-GI tract data)
  • MCL graph clustering
  • understanding graph structure and signatures in genomic data

14. Network analysis of Expression data – (Practical Session)

Example dataset 3 – pig tissue atlas data – part 2
Content:

  • Export options, render graph imgae to file, rendering options
  • Save as a Layout file – the structrure of a Layout file
  • Save as an Expression file
  • Save visible graph
  • Export table
  • class statistic options

15. Fun stuff (Practical Session)

Example dataset 3 – pig tissue atlas data – part 2
Content:

  • MCL graph clustering
  • rotate, trippy background
  • node shape editor, customize shape, load external shape, rendering options
  • node surface image texture

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