Access the running instance of the app on shinyapps.io
The app can be deployed on RStudio/Shiny server. Follow instruction here.
Alternatively, after downloading the code, the app can run within RStudio. Open server.R or ui.R file, then click "Run App" button with green triangle in the upper right corner of the window with code open.
Following packages need to be installed in order to run the app locally:
- shiny
- shinyjs
- shinyBS
- shinycssloaders
- data.table
- DT
- ggplot2
- gplots
- plotly
- scales
- grid
- dendextend
- RColorBrewer
- ggthemes
- sparcl
- dtw
- factoextra
- imputeTS
- MASS
- robust
- pracma
- Hmisc
Install packages using install.packages('name_of_the_package_from_the_list_above') command in RStudio command line.
install.packages(c("shiny", "shinyjs", "shinyBS", "shinycssloaders",
"data.table", "DT",
"ggplot2", "gplots", "plotly", "scales", "grid",
"dendextend", "RColorBrewer", "ggthemes",
"sparcl", "dtw", "factoextra",
"imputeTS", "MASS", "robust", "pracma", "Hmisc"))
Running the 2 following lines should get you started immediatly with a temporary copy of the app:
library(shiny)
runGitHub("dmattek/shiny-timecourse-inspector")
The app recognizes CSV (comma-separated values) files where data columns are separated by a comma and floating point numbers use a dot (full-stop). Compressed CSV files in zip or bz2 format can be uploaded directly without decompression. Both long and wide data formats are accepted but we highly recommend using the long format because it allows for multiple groupings and multivariate measurements.
In the long format, the first row should include column headers. The input CSV file should contain at least these three columns:
- Identifier of a time series, i.e. a track label
- Time points
- Time-varying variable
| ID | Time | Meas1 |
|---|---|---|
| 1 | 1 | 3.3 |
| 1 | 2 | 2.1 |
| 1 | 4 | 4.3 |
| ---- | ------ | ------- |
| 2 | 1 | 2.8 |
| 2 | 2 | 1.9 |
| 2 | 3 | 1.7 |
| 2 | 4 | 2.2 |
In case of multivariate time series, additional columns with variables can be added in the input. Then, the GUI allows for choosing a single or a combination of two variables to display.
Time series can be grouped by introducing a grouping column:
| Group | ID | Time | Meas1 |
|---|---|---|---|
| gr1 | 1 | 1 | 3.3 |
| gr1 | 1 | 2 | 2.1 |
| gr1 | 1 | 4 | 4.3 |
| ------- | ---- | ------ | ------- |
| gr1 | 2 | 1 | 2.8 |
| gr1 | 2 | 2 | 1.9 |
| gr1 | 2 | 3 | 1.7 |
| gr1 | 2 | 4 | 2.2 |
| ------- | ---- | ------ | ------- |
| gr2 | 1 | 1 | 5.1 |
| gr2 | 1 | 2 | 5.4 |
| gr2 | 1 | 3 | 5.3 |
Introduction of grouping allows for the analysis and displaying data per group.
In wide format, entire univariate time series are stored as rows, with columns treated as time points. The first two columns should contain a grouping and the identifier of time series.
| Group | ID | 0 | 1 | 2 | further time points |
|---|---|---|---|---|---|
| gr1 | 1 | 3.0 | 3.3 | 3.1 | ... |
| gr1 | 2 | 2.0 | 2.1 | 1.9 | ... |
| gr2 | 1 | 4.9 | 5.1 | 5.0 | ... |
| gr2 | 2 | 5.2 | 5.4 | 5.3 | ... |
| gr2 | 3 | 5.5 | 5.3 | 5.6 | ... |
We do not recommend this format because of its lack of flexibility. In wide format, only one grouping column and one measurement can be passed at a time, this means any new grouping or measurement analysis requires to create a dedicated file.
For the analysis, track labels need to be unique across the entire dataset. If the track label column is not unique in the uploaded dataset, there's an option in the UI to create a unique track ID. Check the Create unique track label box on and choose grouping columns that will be added to the existing non-unique track label.
In the example above, the ID column is not unique across the dataset (ID=1 is repeated in group gr1 and gr2), therefore the unique track label has to consist of columns Group and ID. The resulting track label will be gr1_1, gr2_1, etc.
The app opens with a default window that allows to plot population averages, individual time series, and power spectral density.
The following features of time series analysis are available in the app:
- Perform simple math calculations on an individual variable (inversion 1/X), or on two variables (division, sum, multiplication, subtraction).
- Trim the time axis of the data.
- Normalise to the average of data points in a selected interval. Time series can be normalised with respect to the entire dataset, a group, or a single time series. The latter would normalise every time course to the mean of its own selected interval.
- Remove outlier time points by removing a percentage of data from the top, bottom, or both tails of pooled data points. Gaps in time series due to outlier removal can be then linearly interpolated or tracks can be removed entirely from the set. The UI allows for selecting the size of gaps above which the track is removed.
- Highlight individual time series by selecting a unique series identifier.
- Calculate area under individual time series and visualise as a dot-, violin-, or a box-plot. The UI allows for selection of the time series range used for AUC calculation.
- Display a dot-, violin-, box-, or a line-plot for selected time points.
- Display a scatter-plot to identify correlations between two time points.
- Calculate the power spectral density (PSD) using smoothed periodogram or autoregressive fit. Both estimations rely on the R's built-in implementation \texttt{spectrum}. PSD plots can be visualized in the frequency or period domain and independently for each time-series groups. Axis can be transformed with common functions (log, inverse...) to facilitate the identification of spectral patterns.
- Perform hierarchical and sparse-hierarchical clustering of individual time series. In these modules, the dendrogram can be cut at a chosen level to help visualising clusters. Addiitonally available are plots with cluster averages, individual times series per cluster, and contribution of time series from different groupings to clusters.
- Perform cluster validation. In this module both relative and internal validations are available. Relative validation with a sweep through a range of possible cluster numbers and a report of average silhouette width and within cluster sum of squares. Internal cluster validation, for a fixed number of clusters return 3 visualizations: a dendrogram colored according to the cut, the silhouette plot and a visualization of the clusters on the first 2 principal components. This analysis relies on the implementation in the R package \texttt{factoextra}.