README.md

In decusInLabore/biologicSC: What the Package Does (One Line, Title Case)

biologicSC

Step 1

Clone app from github

git clone [e-mail]:decusInLabore/testbiologicSC.git

mv testbiologicSC yourApp

Run development version with default dataset (from Crick DB) locally:
# Set options here
options(golem.app.prod = FALSE) # TRUE = production mode, FALSE = development mode

# Detach all loaded packages and clean your environment
golem::detach_all_attached()
# rm(list=ls(all.names = TRUE))

# Document and reload your package
golem::document_and_reload()

# Run the application
run_app()

Step 2 Option A Create SQLite database with your data

Structure of the data directory that needs to be added to the above app. data ├── connect │   └── db.txt └── pbmc_small_app2_DB

Go to the dir

devtools::install_github("decusinlabore/biologicSC")
library(biologicSC)
library(Seurat)

testObj <- pbmc_small
all.genes <- rownames(testObj)
testObj <- ScaleData(testObj, features = all.genes)
testObj <- RunPCA(testObj, npcs = 3)
testObj <- RunUMAP(testObj, dims = 1:3)

testObj <- FindNeighbors(testObj, dims = 1:3)
testObj <- FindClusters(testObj, resolution = 0.5)

[e-mail][["all"]] <- "all"

[e-mail][["sampleName"]] <- "SampleA"
[e-mail][["sampleColor"]] <- "#009900"

[e-mail][["clusterName"]] <- paste0("Cluster_", as.vector([e-mail]$seurat_clusters))

clusterVec <- unique([e-mail]$clusterName)
clusterCols <- scales::hue_pal()(length(clusterVec))
dfCol <- data.frame(clusterVec, clusterCols)

dfCell <- data.frame(cellID = row.names([e-mail]), clusterName = [e-mail]$clusterName) 
dfCell <- merge(dfCell, dfCol, by.x = "clusterName", by.y = "clusterVec")

addVec <- dfCell$clusterCols
names(addVec) <- row.names(dfCell)

testObj <- Seurat::AddMetaData(
                object = testObj, 
                metadata = addVec, 
                "clusterColor"
)

[e-mail][["meta_Region"]] <- "Tumor"
[e-mail][1:20,"meta_Region"] <- "Normal"



params <- scanObjParams(testObj)

seurat2viewer(
    obj = testObj,
    assay = "RNA",
    #slot = "data",
    geneSel = NULL,
    params = params,
    projectName = "pbmc_small_app"
) 

Step 2 Option B your data to remote db

Step 3

Deploy

devtools::install_github("decusinlabore/biologicSC")
library(biologicSC)
library(Seurat)

testObj <- pbmc_small
all.genes <- rownames(testObj)
testObj <- ScaleData(testObj, features = all.genes)
testObj <- RunPCA(testObj, npcs = 3)
testObj <- RunUMAP(testObj, dims = 1:3)

testObj <- FindNeighbors(testObj, dims = 1:3)
testObj <- FindClusters(testObj, resolution = 0.5)

[e-mail][["all"]] <- "all"

[e-mail][["sampleName"]] <- "SampleA"
[e-mail][["sampleColor"]] <- "#009900"

[e-mail][["clusterName"]] <- paste0("Cluster_", as.vector([e-mail]$seurat_clusters))

clusterVec <- unique([e-mail]$clusterName)
clusterCols <- scales::hue_pal()(length(clusterVec))
dfCol <- data.frame(clusterVec, clusterCols)

dfCell <- data.frame(cellID = row.names([e-mail]), clusterName = [e-mail]$clusterName) 
dfCell <- merge(dfCell, dfCol, by.x = "clusterName", by.y = "clusterVec")

addVec <- dfCell$clusterCols
names(addVec) <- row.names(dfCell)

testObj <- Seurat::AddMetaData(
                object = testObj, 
                metadata = addVec, 
                "clusterColor"
)

[e-mail][["meta_Region"]] <- "Tumor"
[e-mail][1:20,"meta_Region"] <- "Normal"



params <- scanObjParams(testObj)

seurat2viewer(
    obj = testObj,
    assay = "RNA",
    #slot = "data",
    geneSel = NULL,
    params = params,
    projectName = "pbmc_small_app"
) 

setwd("..")
library(shiny)
runApp("pbmc_small_app")

decusInLabore/biologicSC documentation built on May 24, 2021, 4:11 p.m.