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Simple Shiny app

The simple Shiny app is intended for the visualization of single cell RNA-seq data generated in the study "Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq" by Tirosh, and collaborators 2016.

The deployed version of the app can be accessed at https://mtello.shinyapps.io/tsneviz/

The data is freely available at GSE72056.

The tSNE coordinates were calculated using the following code:

library(data.table)
library(Rtsne)
scexp <- fread("GSE72056_melanoma_single_cell_revised_v2.tsv", header = TRUE)
scexp <- scexp[4:nrow(scexp),]
setnames(scexp, "Cell", "Gene")
scexp <- as.matrix(scexp[, .SD, .SDcols = !"Gene"])
scexp <- t(scexp)
tsne_coord <- Rtsne(scexp, partial_pca  = TRUE)

The current shiny app has the following graphic elements:

  • Tumor_composition: Provides the proportion of cells from all tumors classified into cell types or malignant status depending on the "var" selected. This feature is aimed to provide a by-tumor reference to guide the selection of tumors

  • tSNEref: Provides the tSNE colored by the selected "var" of interest: cell types or malignancy status. This feature is intended as a reference of the different cell clusters to compare cells from individual tumors to other cells in the similar tSNE coordinates.

  • tSNEtumor: Provides a visualization of a given tumor of interest, colors the corresponding cells by the "var" of interest and sets the color of other cells to a gray background. This feature provides a way to localize cells from the same tumor samples in the context of other cell clusters.

Reactive version

The previous Shiny app was also updated to provide a platform to explore the dataset and filter it based on user-defined requirements. The document to run the reactive version can be found at

"./tSNEviz/reactive_doc.Rmd"

Once the document is loaded in an Rstudio session, its possible to runt it by clicking the "Run document" button.

The deployed version can be found at https://mtello.shinyapps.io/reactive_doc/

In this version, I only included non-malignant cells from two donors due to computational and storage limitations. by GitHub and ShinyApps.io. However, the code is able to run for the full dataset in a local computer with the following specs:

Processor: 11th Gen Intel(R) Core(TM) i5-11400H @ 2.70GHz, 2688 Mhz, 6 Core(s), 12 Logical Processor(s)
Installed Physical Memory (RAM): 16.0 GB

Therefore it is a good template to develop a local platform to analyze and filter single cell RNA-seq data.