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DESCRIPTION (891, 2023-01-27)
FastIntegration.Rproj (356, 2023-01-27)
NAMESPACE (523, 2023-01-27)
R/ (0, 2023-01-27)
R/CELLiD.R (1606, 2023-01-27)
R/FastFindAnchors.R (9231, 2023-01-27)
R/FastIntegration.R (7915, 2023-01-27)
R/GetDiscoData.R (2610, 2023-01-27)
R/OneStop.R (1808, 2023-01-27)
R/RcppExports.R (329, 2023-01-27)
R/Utility.R (132, 2023-01-27)
src/ (0, 2023-01-27)
src/FastIntegration.so (1759784, 2023-01-27)
src/Makevars (16, 2023-01-27)
src/RcppExports.cpp (1491, 2023-01-27)
src/RcppExports.o (2246784, 2023-01-27)
src/inetgration.cpp (364, 2023-01-27)
src/inetgration.o (1447592, 2023-01-27)

# FastIntegration v1.1.0 FastIntegration provides two main functions: - A fast and high-capacity version of Seurat Integration - Access data from [DISCO](https://www.immunesinglecell.org/) database **Recent update: Thanks to [Nathan Siemers](https://github.com/NathanSiemers) for reporting [bugs](https://github.com/JinmiaoChenLab/FastIntegration/issues/6) and providing [suggestions](https://github.com/JinmiaoChenLab/FastIntegration/issues/6) on DownloadDiscoData function. We have added resume download functions in the new version. More vignettes can be found at https://immunesinglecell.org/vignette/docs/DISCO/FastIntegration/cell-type-atlas ## Requirement FastIntegration requires the following packages: - [R](https://www.r-project.org/) (\>= 4.0.0) - [Seurat](https://cran.r-project.org/web/packages/Seurat/index.html) (\>= 4.0.0) - [SeuratObject](https://cran.r-project.org/web/packages/SeuratObject/index.html) (\>= 4.0.0) - [data.table](https://cran.r-project.org/web/packages/data.table/vignettes/datatable-intro.html) - [Matrix](https://cran.r-project.org/web/packages/Matrix/index.html) - [tictoc](https://cran.r-project.org/web/packages/tictoc/index.html) - [dplyr](https://cran.r-project.org/web/packages/dplyr/index.html) - [pbmcapply](https://cran.r-project.org/web/packages/pbmcapply/index.html) - [stringr](https://cran.r-project.org/web/packages/stringr/vignettes/stringr.html) - [tools](https://www.rdocumentation.org/packages/tools/versions/3.6.2) We highly recommend you to build R with openblas which will accelerate integration 2-3x times. Here is the common way to do it: sudo yum install -y openblas openblas-threads openblas-openmp \# for centos sudo apt-get install libopenblas-dev \# for debian ./configure --enable-R-shlib --enable-byte-compiled-packages --enable-BLAS-shlib --enable-memory-profiling --with-blas="-lopenblas" ## Installation ``` r devtools::install_github("git@github.com:JinmiaoChenLab/FastIntegrate.git") ``` ## Usage ### Preprocess ``` r library(Seurat) library(pbmcapply) library(stringr) rna.list = readRDS("rna_list.rds") # read list of Seurat object, each element in list is a sample # make all samples have same genes overlapped.gene = Reduce(intersect, lapply(rna.list, rownames)) for (i in 1:length(rna.list)) { rna.list[[i]] = subset(rna.list[[i]], features = overlapped.gene) rna.list[[i]] = NormalizeData(rna.list[[i]]) rna.list[[i]] = FindVariableFeatures(rna.list[[i]]) rna.list[[i]] = RenameCells(rna.list[[i]], new.names = paste0(Cells(rna.list[[i]]), "--", i)) } ``` ### Step by step integration ``` r library(Seurat) library(pbmcapply) library(FastIntegration) # rna.list is the list of seurat object BuildIntegrationFile(rna.list = rna.list, tmp.dir = "./", nCores = 50) FastFindAnchors(tmp.dir = "./", nCores = 50) # close current R session and open a new one to clean the memory (This is important for large data integration) # In the new session, please just set work directory and do not load any data. Then run the following codes: library(Seurat) library(pbmcapply) library(FastIntegration) genes = readRDS("FastIntegrationTmp/raw/1.rds") genes = rownames(genes) idx = split(1:length(genes), cut(1:length(genes), 20, labels = FALSE)) pbmclapply( 1:20, function(i) { rna.integrated = FastIntegration(tmp.dir = "./", npcs = 1:30, slot = "data", features.to.integrate = genes[idx[[i]]]) saveRDS(rna.integrated, paste0("FastIntegrationTmp/inte/inte_", i, ".rds"), compress = F) }, mc.cores = 20 ) ``` ### After integration ``` r ##### create Seurat obj with the variable features of integration (For very big dataset) ##### features = readRDS("FastIntegrationTmp/others/features.rds") rna.data = pbmclapply( 1:20, function(i) { rna = readRDS(paste0("./FastIntegrationTmp/inte/inte_", i, ".rds")) rna = rna[intersect(rownames(rna), features),] return(rna) }, mc.cores = 20 ) rna.data = do.call(rbind, rna.data) rna.data = CreateSeuratObject(rna.data) rna.data = ScaleData(rna.data, features = features) rna.data = RunPCA(rna.data, features = features, npcs = 50) rna.data = FindNeighbors(rna.data, dims = 1:50) rna.data = FindClusters(rna.data, graph.name = "RNA_snn", algorithm = 2) rna.data = RunUMAP(rna.data, dims = 1:50) ##### select varibale gene based on integrated data (For dataset with less than 100 samples) ##### rna.data = pbmclapply( 1:20, function(i) { rna = readRDS(paste0("FastIntegrationTmp/inte/inte_", i, ".rds")) return(rna) }, mc.cores = 20 ) rna.data = do.call(rbind, rna.data) rna.data = CreateSeuratObject(rna.data) rna.data = FindVariableFeatures(rna.data, nfeatures = 2000) features = VariableFeatures(rna.data) rna.data = ScaleData(rna.data, features = features) rna.data = RunPCA(rna.data, features = features) rna.data = FindNeighbors(rna.data, dims = 1:50) rna.data = FindClusters(rna.data, resolution = 0.5, algorithm = 2) rna.data = RunUMAP(rna.data, dims = 1:50) ``` ### Download data from DISCO ``` r ##### Filter samples and get metadata ##### # You can filter samples by their different headers: tissue, disease, platform, project.id. sample.id, sample.type # For each header, you can select multiple items as follows: meta = FindSampleByMetadata(tissue = c("blood", "kidney")) ##### Download sample ##### # dir is the location where the files are saved DownloadDiscoData(meta, dir = "./disco") # mostly CD4 T cells (CD3E+CD8A-) ##### Recover counts slot ##### # To reduce file size, we removed counts slot from data. You can recover it as follow: rna = readRDS("/test/AML0024_3p.rds") rna = AddCountsSlot(rna) ``` ## Usage Scenario We have apply FastIntegration to [DISCO](http://www.immunesinglecell.org/) database for integrating thousands of samples. ## License All other code in this repository is licensed under a [GPL-3](https://www.r-project.org/Licenses/GPL-3) license.

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