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Organisation of what we want to do during the Hackathon

Teaser

Please help us modify and implement this file, with your ideas, expectation, questions, ...

What plot types do we want to show?

  • Volcano (Max)
  • Violin (Kelly)
  • GGplot
  • Karyotype
  • Pedigree
  • Timeline
  • Gene-specific expression across tSNE clusters
  • Single-cell mtDNA/mitochondrial transcriptome coverage (KELLY)
  • RNAseq
  • Survival (Yaseswini Neelamraju)
  • Box (Suzy)
  • Heatmap (Max)
  • Scatterplots (general) and try adding plotly (Lawryn)
  • Plotly
  • DNA methylation region plots/browsers (Yaseswini Neelamraju; Christy LaFlamme)
  • Manhattan plot (Yaseswini Neelamraju)
  • sample-to-sample distance plot (Max)
  • Histogram (Max)
  • PCA plot (Max)
  • 3D interactive PCA plot (Lawryn)

What modules do we have so far? List under each one what cusotmization options we want

  • Heatmap
    • color selection
    • changing font sizes
    • bars at top and side for meta data
    • zoom in
    • output table of selection in window
  • Plotly Scatterplot
    • add linear regression lin
    • selection of points specific color (by user input, by pathway etc)
    • adjusting x and y axis
  • plotDist
  • plot Volcano
    • set p value cutoff interactively
    • set fold change interactively
    • up one color
    • down one color
    • color palettes
    • label top points
  • Histogram
    • Add color picker
    • Different bars different color or gradient
  • plot PCA
    • Add color picker
    • Shapes
    • selection of points

What general options do we want for each plot type? (Customization options, overlay options, export options)

What file/object types do we want to be able to use?

There are two test data can be used, located in example_data. One is MS_2.rda contains three data.frame:

  • df, a data.frame with feature on row and sample on column. It doesn't matter what is feature, it could be gene if the data is RNA-Seq count/normalized count, or it could be peptide or metabolites if the data is Mass Spec peak intensity data.

  • sample_meta, a data.frame containing sample metadata, such as sample names, sample group/label/class, sex, time point, etc.

  • feature_meta, a data.frame containing feature metadata, such as gene name/symbol/emsembleID for gene (if gene is feature), accession number for protein/peptide (if peptide is feature)

The second data L29_vitro_Control_vs_knockdown_diff is a statistic result table, containing p-value and log2FC, among other variables. Could be the output from DESeq2 or other stat package.

What general stucture should we go for ?

  • Add tooltips to ggplot for plotly informations

What role does everyone want to have?

  • Project manager
  • Clean up person
  • Tester
  • Documenter