knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.align = "center" )
The goal of this vignette is to highlight the power of R and Plotly regarding the visualisation of time series.
library(colorRamps) library(dplyr) library(plotly) library(purrr) library(mgx2r) library(cellviz3d)
To install {cellviz3d}:
# devtools::install_github("marionlouveaux/cellviz3d") library(cellviz3d)
Some .ply demonstration data coming from my PhD thesis are attached to this package and used here in the vignette. This dataset is a timelapse recording of the development of a WT shoot apical meristem expressing a membrane marker. I took one 3D stack every 12h and have 5 timepoints in total. Here I load the .ply and cell graph .ply for all the timepoints of this timelapse recording.
ply.dir <- system.file("extdata", "full/normalMesh/", package = "mgx2r") mesh.all <- map(list.files(ply.dir, recursive = TRUE, full.names = TRUE), ~ read_mgxPly(file = .x, ShowSpecimen = FALSE)) graph.dir <- system.file("extdata", "full/cellGraph/", package = "mgx2r") cellGraph.all <- map(list.files(graph.dir, recursive = TRUE, full.names = TRUE), ~read_mgxCellGraph(fileCellGraph = .x, header_max = 30))
saveRDS(mesh.all, file = "mesh_meristem_full_all.rds") saveRDS(cellGraph.all, file = "cellGraph_meristem_full_all.rds")
In plotly, the slider option allows to visualise several graphs linked by a time variable.
meshColors.all <- list(NULL, NULL, NULL, NULL, NULL) plotlyMesh_all(meshExample = mesh.all, graphExample = cellGraph.all, meshColors = meshColors.all, display = 'heatmap')
silent <- file.copy( system.file("img", "full/timeserie800ms.gif", package = "mgx2r"), "timeserie800ms.gif") knitr::include_graphics("timeserie800ms.gif")
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