public v1.1.0

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: FastIntegration
 Type: Package
 Title: Fast Integration of Single-cell Data
-Version: 0.1.0
+Version: 1.1.0
 Author: c(
   person(given = "Mengwei", family = "Li", email = "[email protected]", role = "aut"),
   person(given = "Xiaomeng", family = "Zhang", email = "[email protected]", role = "aut"),

R/GetDiscoData.R

@@ -0,0 +1,116 @@
+GetDiscoSample = function(
+    verbose = T
+) {
+  if(!require("jsonlite")) {
+    stop("Please install jsonlite")
+  }
+
+  tryCatch(
+    {
+      if (verbose) {
+        message("Starting to download metadata from DISCO database")
+      }
+      meta = fromJSON("https://www.immunesinglecell.org/api/sample/all")
+      return(meta)
+    },
+    error=function(cond) {
+      stop("Network error. Please try again")
+    }
+  )
+}
+
+#' @import jsonlite
+#' @export
+FindSampleByMetadata = function(
+  tissue = c(),
+  disease = c(),
+  platform = c(),
+  project.id = c(),
+  sample.id = c(),
+  sample.type = c()
+) {
+  meta.all =  GetDiscoSample()
+  if (length(tissue) > 0) {
+    meta.all = meta.all[which(meta.all$tissue %in% tissue),]
+  }
+  if (length(disease) > 0) {
+    meta.all = meta.all[which(meta.all$disease %in% disease),]
+  }
+  if (length(platform) > 0) {
+    meta.all = meta.all[which(meta.all$platform %in% platform),]
+  }
+  if (length(project.id) > 0) {
+    meta.all = meta.all[which(meta.all$projectId %in% project.id),]
+  }
+  if (length(sample.type) > 0) {
+    meta.all = meta.all[which(meta.all$sampleType %in% sample.type),]
+  }
+
+  meta.all = meta.all[which(meta.all$processStatus == "QC pass"),]
+  if (nrow(meta.all) == 0) {
+    stop("Sorry, no sample is found. Please try to use other filters.")
+  } else {
+    return(meta.all)
+  }
+}
+
+#' @export
+DownloadDiscoData = function(
+    metadata,
+    expressed.gene = c(),
+    unexpressed.gene = c(),
+    dir = "./disco_data"
+) {
+
+  meta = metadata
+  dir.create(dir)
+  tryCatch(
+    {
+      for (i in 1:nrow(meta)) {
+        message(paste0("Downloading the ", i, "st sample"))
+        rna = readRDS(url(paste0(
+          "http://dc.vishuo.com:8887/api/vishuo/download/getExp?project=",meta$projectId[i],"&sample=",meta$sampleId[i]
+        )))
+
+        if (length(expressed.gene) > 0) {
+          for (j in 1:length(expressed.gene)) {
+            if (length(which(rna@assays$RNA@data[expressed.gene[j],] > 0)) > 0) {
+              rna = subset(rna, cells = names(which(rna@assays$RNA@data[expressed.gene[j],] > 0)))
+            } else {
+              rna = NULL
+              break
+            }
+          }
+        }
+
+        if (length(unexpressed.gene) > 0) {
+          for (j in 1:length(unexpressed.gene)) {
+            if (length(which(rna@assays$RNA@data[unexpressed.gene[j],] == 0)) > 0) {
+              rna = subset(rna, cells = names(which(rna@assays$RNA@data[unexpressed.gene[j],] == 0)))
+            } else {
+              rna = NULL
+              break
+            }
+          }
+        }
+
+        if (is.null(rna) == F) {
+          rna@assays$RNA@counts = expm1(rna@assays$RNA@data)
+          saveRDS(rna, paste0(dir, "/", meta$sampleId[i], ".rds"), compress = F)
+        } else {
+          message(paste0("Sking the ", i, "st sample. No cells are found after filtering."))
+        }
+
+      }
+    },
+    error=function(cond) {
+      stop("Network error. Please try again")
+    }
+  )
+  message("Job finished")
+}
+
+
+
+
+

README.md

@@ -1,43 +1,45 @@
-# FastIntegration v1.0.0
+# FastIntegration v1.1.0
+
 FastIntegration is a fast and high-capacity version of Seurat Integration. FastIntegrate can integrate thousands of scRNA-seq datasets and outputs batch-corrected values for downstream analysis.
 
+**Recent update: New functions which allow users to filter and download data in DISCO (<https://www.immunesinglecell.org/repository>), comprising 5200+ single-cell samples!**
 
 ## 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)
+-   [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)
 
 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 yum install -y openblas openblas-threads openblas-openmp \# for centos
+
+sudo apt-get install libopenblas-dev \# for debian
 
-sudo apt-get install libopenblas-dev # for debian
+./configure --enable-R-shlib --enable-byte-compiled-packages --enable-BLAS-shlib --enable-memory-profiling
 
-./configure --enable-R-shlib --enable-byte-compiled-packages \
+--with-blas="-lopenblas"
 
-  --enable-BLAS-shlib --enable-memory-profiling \
-
-  --with-blas="-lopenblas"
-
 ## Installation
 
-```R
+``` r
 devtools::install_github("[email protected]:JinmiaoChenLab/FastIntegrate.git")
 ```
 
 ## Usage
 
 ### Preprocess
-```R
+
+``` r
 library(Seurat)
 library(pbmcapply)
 rna.list = readRDS("rna_list.rds") # read list of Seurat object, each element in list is a sample
@@ -50,27 +52,25 @@ for (i in 1:length(rna.list)) {
   rna.list[[i]] = FindVariableFeatures(rna.list[[i]])
   rna.list[[i]] = RenameCells(rna.list[[i]], new.names = paste0(Cells(rna.list[[i]]), "--", i))
 }
-
 ```
 
-
-
 ### Onestop function
 
-For large sample size (> 200 samples), we recommend to use step by step integration.
-```R
+For large sample size (\> 200 samples), we recommend to use step by step integration.
+
+``` r
 library(FastIntegration)
 # rna.list is the list of seurat object
 data = OneStopIntegration(
   rna.list = rna.list, 
   tmp.dir = "./test/", 
   max.cores = 30
 )
-
 ```
 
 ### Step by step integration
-```R
+
+``` r
 library(Seurat)
 library(pbmcapply)
 library(FastIntegration)
@@ -94,14 +94,13 @@ pbmclapply(
     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
+  }, mc.cores = 20 
 )
-
 ```
 
-
 ### After integration
-```R
+
+``` r
 ##### create Seurat obj with the variable features of integration (For very big dataset) ##### 
 features = readRDS("FastIntegrationTmp/others/features.rds")
 rna.data = pbmclapply(
@@ -137,13 +136,27 @@ 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 ##### 
+# You can further filter cells by specifying expressed or unexpressed genes.
+# dir is the location where the files are saved
+DownloadDiscoData(meta, expressed.gene = c("CD3E"), unexpressed.gene = c("CD8A"), dir = "./disco") # mostly CD4 T cells (CD3E+CD8A-)
+
+```
 
 ## 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.
 
+All other code in this repository is licensed under a [GPL-3](https://www.r-project.org/Licenses/GPL-3) license.