R for High-Dimensional Molecular Data
Add PCA Loadings
Flow
Interactive Heatmap
PC Plot
Calculate Sample Mahalanobis Distances
Center T* Omic
Check Design
Check Tidy Omic
Check T*Omic
Check Triple Omic
Convert Wide to Tidy Omic
Create Tidy Omic
Create Triple Omic
Downsample Heatmap
Export T*Omic in Tidy Format
Export T*Omic as Triple
Export T*Omic as Wide Data
Filter T* Omics
Filter Input
Filter Server
Format Names for Plotting
Get Design Table
Get Tomic Table
ggBivariate Output
ggBivariate Server
ggplot Output
ggplot Server
ggUnivariate Output
ggUnivariate Server
Hierarchical clustering order
Impute Missing Values
Infer Tomic Table Type
Lasso Input
Lasso Server
Organize Input
Organize Servers
Bivariate Plot
Plot Heatmap
Plot Missing Values
Univariate Plot
Plot Saver Input
Plot Saver Server
Prepare Example Datasets
Reconcile Triple Omic
Reform Tidy Omic
Remove Missing Values
romic: R for High-Dimensional Molecular Data
Shiny Filter Test
Shiny ggBivariate Test
Shiny ggplot Test
Shiny ggUnivariate Test
Shiny Lasso Test w/ Reactive Values
Shiny Lasso Test
Shiny Organize Test
Shiny Plot Saver Test
Shiny Sort Test
Sort Triple Omic
Sort Triple Arrange
Sort Triple Hclust
Sort Input
Sort Server
Tidy omic to triple omic
T* Omic Sort Status
Tomic To Matrix
T* Omic To
Triple Omic to Tidy Omic
Try brushedPoints
Update Sample Factors
Update Tidy Omic
Update T* Omic
Var Partial Match
Represents high-dimensional data as tables of features, samples and measurements, and a design list for tracking the meaning of individual variables. Using this format, filtering, normalization, and other transformations of a dataset can be carried out in a flexible manner. 'romic' takes advantage of these transformations to create interactive 'shiny' apps for exploratory data analysis such as an interactive heatmap.
Useful links