# BEER

**Repository Path**: jumphone/BEER

## Basic Information

- **Project Name**: BEER
- **Description**: remove batch effect in single-cell data
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-04-29
- **Last Updated**: 2022-09-17

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

<img src="https://github.com/jumphone/BEER/raw/master/DATA/BEER_LOGO.png" width="200">

# BEER: Batch EffEct Remover for single-cell data

Environment: R

BEER's latest version: https://github.com/jumphone/BEER/releases

# News:

* Mar. 2021 ( V0.1.9 ): First version for Seurat 4.0.0

* Feb. 2021 ( V0.1.8 ): Last version for Seurat 3.0.0

* Nov. 2019 ( v0.1.7 ): In ".simple_combine(D1, D2, FILL=TRUE)", "FILL" can help users to keep genes that are expressed in only one condition (fill the matrix with “0”). Default "FILL" is FALSE

* July 2019 ( v0.1.6 ): BEER can automatically adjust "GNUM" when cell number is small in some batch 

* July 2019 ( v0.1.5 ): "ComBat" is used to replace "regression" of "ScaleData" (ComBat is much faster)

* July 2019 ( v0.1.4 ): Users can provide genes which need to be removed.

* July 2019 ( v0.1.3 ): Users can use [VISA](https://github.com/jumphone/VISA) to extract peaks of scATAC-seq.

* ...

# Content:

* [Workflow](#workflow)
* [Requirement (Installation)](#requirement)
* [Vignettes (Usage)](#vignettes)
* [Reference](#reference)
* [License](#license)

</br>
</br>
</br>

--------------------------------------------------------------------------------------------


# Workflow:

#### Latest version

<img src="https://github.com/jumphone/BEER/raw/master/DATA/BP.png" width="400">

Please see [V. Batch-effect Removal Enhancement](#v-batch-effect-removal-enhancement) for details of "Enhancement".

</br>
</br>

# Requirement:

    #R >=3.5
    install.packages('Seurat') # ==4.0.0 
    
    # Install ComBat:
    if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
    BiocManager::install("sva")
    BiocManager::install("limma")

    # Users can use "BEER" by directly importing "BEER.R" on the github webpage:
    
    source('https://raw.githubusercontent.com/jumphone/BEER/master/BEER.R')
    
    # Or, download and import it:
    
    source('BEER.R')
    
    
For batch-effect removal enhancement, please install BBKNN: https://github.com/Teichlab/bbknn

</br>
</br>


# Vignettes:

* [I. Combine Two Batches](#I-Combine-Two-Batches)
* [II. Combine Multiple Batches](#II-Combine-Multiple-Batches)
* [III. UMAP-based Clustering](#III-UMAP-based-Clustering)
* [IV. Combine scATAC-seq & scRNA-seq](#iv-combine-scatac-seq--scrna-seq)
* [V. Batch-effect Removal Enhancement](#v-batch-effect-removal-enhancement)
* [VI. Transfer Labels](#vi-transfer-labels)
* [VII. Biological Interpretation](#vii-biological-interpretation)
* [VIII. QC before using BEER](#viii-QC-before-using-beer)


</br>

# Set Python

    library(reticulate)
    use_python("/home/toolkit/local/bin/python3",required=T)
    py_config()

</br>

# I. Combine Two Batches

Download demo data: https://github.com/jumphone/BEER/raw/master/DATA/demodata.zip 

Please do basic quality control before using BEER (e.g. remove low-quality cells & genes). 

For QC, please see: https://satijalab.org/seurat/v3.0/pbmc3k_tutorial.html 

### Step1. Load Data

    library(Seurat)
  
    source('https://raw.githubusercontent.com/jumphone/BEER/master/BEER.R')
    #source('BEER.R')
    
    #Read 10X data: pbmc.data <- Read10X(data.dir = "../data/pbmc3k/filtered_gene_bc_matrices/hg19/")
    
    #Load Demo Data (subset of GSE70630: MGH53 & MGH54)
    #Download: https://github.com/jumphone/BEER/raw/master/DATA/demodata.zip
    
    D1 <- read.table(unz("demodata.zip","DATA1_MAT.txt"), sep='\t', row.names=1, header=T)
    D2 <- read.table(unz("demodata.zip","DATA2_MAT.txt"), sep='\t', row.names=1, header=T)

    # "D1" & "D2" are UMI matrix (or FPKM, RPKM, TPM, PKM ...; Should not be gene-centric scaled data)
    # Rownames of "D1" & "D2" are gene names
    # Colnames of "D1" & "D2" are cell names 
    
    # There shouldn't be duplicated colnames in "D1" & "D2":
    colnames(D1)=paste0('D1_', colnames(D1))
    colnames(D2)=paste0('D2_', colnames(D2))

    DATA=.simple_combine(D1,D2)$combine
    
    # Users can use "DATA=.simple_combine(D1,D2, FILL=TRUE)$combine" to keep genes that are expressed in only one condition.
    
    BATCH=rep('D2',ncol(DATA))
    BATCH[c(1:ncol(D1))]='D1'
    
    # Simple Quality Control (QC): check the number of sequenced genes
    # PosN=apply(DATA,2,.getPos)
    # USED=which(PosN>500 & PosN<4000) 
    # DATA=DATA[,USED]; BATCH=BATCH[USED] 
    

### Step2. Detect Batch Effect

    mybeer=BEER(DATA, BATCH, GNUM=30, PCNUM=50, ROUND=1, GN=2000, SEED=1, COMBAT=TRUE, RMG=NULL)   

    # DATA: Expression matrix. Rownames are genes. Colnames are cell names.
    # BATCH: A character vector. Length is equal to the "ncol(DATA)".
    # GNUM: the number of groups in each batch (default: 30)
    # PCNUM: the number of computated PCA subspaces (default: 50)
    # ROUND: batch-effect removal strength, positive integer (default: 1)
    # GN: the number of variable genes in each batch (default: 2000)
    # RMG: genes need to be removed (default: NULL)
    # COMBAT: use ComBat to adjust expression value(default: TRUE)    
    
    # Users can use "ReBEER" to adjust GNUM, PCNUM, ROUND, and RMG (it's faster than directly using BEER).
    # mybeer <- ReBEER(mybeer, GNUM=30, PCNUM=50, ROUND=1, SEED=1, RMG=NULL) 
    
    # Check selected PCs
    PCUSE=mybeer$select
    COL=rep('black',length(mybeer$cor))
    COL[PCUSE]='red'
    plot(mybeer$cor,mybeer$lcor,pch=16,col=COL,
        xlab='Rank Correlation',ylab='Linear Correlation',xlim=c(0,1),ylim=c(0,1))
    
Users can select PCA subspaces based on the distribution of "Rank Correlation" and "Linear Correlation".

    # PCUSE=.selectUSE(mybeer, CUTR=0.7, CUTL=0.7, RR=0.5, RL=0.5)
    
        
<img src="https://github.com/jumphone/BEER/raw/master/DATA/PLOT1.png" width="400">
    
### Step3. Visualization 
    
#### Keep batch effect:
   
    pbmc_batch=CreateSeuratObject(counts = DATA, min.cells = 0, min.features = 0, project = "ALL") 
    pbmc_batch@meta.data$batch=BATCH
    pbmc_batch=FindVariableFeatures(object = pbmc_batch, selection.method = "vst", nfeatures = 2000)   
    VariableFeatures(object = pbmc_batch)
    pbmc_batch <- NormalizeData(object = pbmc_batch, normalization.method = "LogNormalize", scale.factor = 10000)
    pbmc_batch <- ScaleData(object = pbmc_batch, features = VariableFeatures(object = pbmc_batch))
    pbmc_batch <- RunPCA(object = pbmc_batch, seed.use=123, npcs=50, features = VariableFeatures(object = pbmc_batch), ndims.print=1,nfeatures.print=1)
    pbmc_batch <- RunUMAP(pbmc_batch, dims = 1:50, seed.use = 123,n.components=2)
    DimPlot(pbmc_batch, reduction='umap', group.by='batch', pt.size=0.1) 
    
<img src="https://github.com/jumphone/BEER/raw/master/DATA/PLOT2.png" width="400">
     

#### Remove batch effect:

    pbmc <- mybeer$seurat
    PCUSE <- mybeer$select
    pbmc <- RunUMAP(object = pbmc, reduction='pca',dims = PCUSE, check_duplicates=FALSE)
    
    DimPlot(pbmc, reduction='umap', group.by='batch', pt.size=0.1) 
    
<img src="https://github.com/jumphone/BEER/raw/master/DATA/PLOT3.png" width="400">

      
    
    
</br>
</br>
    
# II. Combine Multiple Batches

Download demo data: https://sourceforge.net/projects/beergithub/files/
   
### Step1. Load Data
    
    source('https://raw.githubusercontent.com/jumphone/BEER/master/BEER.R')
    
    #Load Demo Data (Oligodendroglioma, GSE70630)
    #Download: https://sourceforge.net/projects/beergithub/files/
    
    D1=readRDS('MGH36.RDS')
    D2=readRDS('MGH53.RDS')
    D3=readRDS('MGH54.RDS')
    D4=readRDS('MGH60.RDS')
    D5=readRDS('MGH93.RDS')
    D6=readRDS('MGH97.RDS')
    
    BATCH=c(rep('D1',ncol(D1)),
            rep('D2',ncol(D2)),
            rep('D3',ncol(D3)),
            rep('D4',ncol(D4)),
            rep('D5',ncol(D5)),
            rep('D6',ncol(D6)) )
            
    D12=.simple_combine(D1,D2)$combine
    D34=.simple_combine(D3,D4)$combine
    D56=.simple_combine(D5,D6)$combine
    D1234=.simple_combine(D12,D34)$combine
    D123456=.simple_combine(D1234,D56)$combine
    
    DATA=D123456   
   
    rm(D1);rm(D2);rm(D3);rm(D4);rm(D5);rm(D6)
    rm(D12);rm(D34);rm(D56);rm(D1234);rm(D123456)
    
    # Simple Quality Control (QC): check the number of sequenced genes
    # PosN=apply(DATA,2,.getPos)
    # USED=which(PosN>500 & PosN<4000) 
    # DATA=DATA[,USED]; BATCH=BATCH[USED] 
    
### Step2. Use BEER to Detect Batch Effect

    mybeer=BEER(DATA, BATCH, GNUM=30, PCNUM=50, ROUND=1, GN=2000, SEED=1, COMBAT=TRUE )

    # Check selected PCs
    PCUSE=mybeer$select
    COL=rep('black',length(mybeer$cor))
    COL[PCUSE]='red'
    plot(mybeer$cor,mybeer$lcor,pch=16,col=COL,
        xlab='Rank Correlation',ylab='Linear Correlation',xlim=c(0,1),ylim=c(0,1))
    

<img src="https://github.com/jumphone/BEER/raw/master/DATA/PLOT4.png" width="400">

    
### Step3. Visualization 
        
#### Keep batch effect:
  

    pbmc_batch=CreateSeuratObject(counts = DATA, min.cells = 0, min.features = 0, project = "ALL") 
    pbmc_batch@meta.data$batch=BATCH
    pbmc_batch=FindVariableFeatures(object = pbmc_batch, selection.method = "vst", nfeatures = 2000)   
    VariableFeatures(object = pbmc_batch)
    pbmc_batch <- NormalizeData(object = pbmc_batch, normalization.method = "LogNormalize", scale.factor = 10000)
    pbmc_batch <- ScaleData(object = pbmc_batch, features = VariableFeatures(object = pbmc_batch))
    pbmc_batch <- RunPCA(object = pbmc_batch, seed.use=123, npcs=50, features = VariableFeatures(object = pbmc_batch), ndims.print=1,nfeatures.print=1)
    pbmc_batch <- RunUMAP(pbmc_batch, dims = 1:50, seed.use = 123,n.components=2)
    DimPlot(pbmc_batch, reduction='umap', group.by='batch', pt.size=0.1) 
 
    
<img src="https://github.com/jumphone/BEER/raw/master/DATA/PLOT5.png" width="400">


#### Remove batch effect:


    pbmc <- mybeer$seurat
    PCUSE <- mybeer$select
    pbmc <- RunUMAP(object = pbmc, reduction='pca',dims = PCUSE, check_duplicates=FALSE)
    
    DimPlot(pbmc, reduction='umap', group.by='batch', pt.size=0.1)   
    
<img src="https://github.com/jumphone/BEER/raw/master/DATA/PLOT6.png" width="400">
   
    
</br>   
</br>


# III. UMAP-based Clustering
   

    VEC=pbmc@reductions$umap@cell.embeddings

    # Here, we use K-means to do the clustering
    N=20
    set.seed(123)
    K=kmeans(VEC,centers=N)

    CLUST=K$cluster
    pbmc@meta.data$clust=as.character(CLUST)
    DimPlot(pbmc, reduction='umap', group.by='clust', pt.size=0.5,label=TRUE)
    

<img src="https://github.com/jumphone/BEER/raw/master/DATA/CLUST1.png" width="400">    


    # Or, manually select some cells

    ppp=DimPlot(pbmc, reduction='umap', pt.size=0.5)
    used.cells <- CellSelector(plot = ppp)
    

<img src="https://github.com/jumphone/BEER/raw/master/DATA/PLOT6.png" width="400">    

    # Press "ESC"
    
<img src="https://github.com/jumphone/BEER/raw/master/DATA/CLUST3.png" width="400">    
    
    markers <- FindMarkers(pbmc, ident.1=used.cells,only.pos=T)    
    head(markers, n=20)
    
    
</br>   
</br>

# IV. Combine scATAC-seq & scRNA-seq

Please install "Signac": https://satijalab.org/signac/

Download DEMO data: https://sourceforge.net/projects/beer-file/files/ATAC/ & https://satijalab.org/signac/articles/pbmc_vignette.html

### Step1. Load Data

    source('https://raw.githubusercontent.com/jumphone/BEER/master/BEER.R')
    #source('BEER.R')
    
    library(Seurat)
    library(Signac)
    library(EnsDb.Hsapiens.v75)
    
    counts <- Read10X_h5(filename = "./data/atac_v1_pbmc_10k_filtered_peak_bc_matrix.h5")
    
    metadata <- read.csv(
      file = "./data/atac_v1_pbmc_10k_singlecell.csv",
      header = TRUE,
      row.names = 1
        )

    chrom_assay <- CreateChromatinAssay(
        counts = counts,
        sep = c(":", "-"),
        genome = 'hg19',
        fragments = './data/atac_v1_pbmc_10k_fragments.tsv.gz',
        min.cells = 10,
        min.features = 200
       )

    pbmc.atac <- CreateSeuratObject(
        counts = chrom_assay,
        assay = "peaks",
        meta.data = metadata
        )
    
    annotations <- GetGRangesFromEnsDb(ensdb = EnsDb.Hsapiens.v75)
    seqlevelsStyle(annotations) <- "UCSC"
    genome(annotations) <- "hg19"
    Annotation(pbmc.atac) <- annotations


    gene.activities <- GeneActivity(pbmc.atac)
         
    pbmc.rna <- readRDS("./data/pbmc_10k_v3.rds")
    
    D1=as.matrix(gene.activities)
    D2=as.matrix(pbmc.rna@assays$RNA@counts)
    colnames(D1)=paste0('ATAC_', colnames(D1))
    colnames(D2)=paste0('RNA_', colnames(D2))
    
    D1=.check_rep(D1)
    D2=.check_rep(D2)
    
    DATA=.simple_combine(D1,D2)$combine
    BATCH=rep('RNA',ncol(DATA))
    BATCH[c(1:ncol(D1))]='ATAC'
    
 
 
### Step2. Use BEER to Detect Batch Effect

    mybeer <- BEER(DATA, BATCH, GNUM=30, PCNUM=50, ROUND=1, GN=5000, SEED=1, COMBAT=TRUE)
    saveRDS(mybeer, file='mybeer')
    
    # Users can use "ReBEER" to adjust parameters
    mybeer <- ReBEER(mybeer, GNUM=100, PCNUM=100, ROUND=3, SEED=1)
    
    PCUSE=mybeer$select
    #PCUSE=.selectUSE(mybeer, CUTR=0.8, CUTL=0.8, RR=0.5, RL=0.5)
    
    COL=rep('black',length(mybeer$cor))
    COL[PCUSE]='red'
    plot(mybeer$cor,mybeer$lcor,pch=16,col=COL,
        xlab='Rank Correlation',ylab='Linear Correlation',xlim=c(0,1),ylim=c(0,1))
    

<img src="https://github.com/jumphone/BEER/raw/master/DATA/PLOT7.png" width="400">   


### Step3. Visualization 
    
#### Keep batch effect:
  
    pbmc_batch=CreateSeuratObject(counts = DATA, min.cells = 0, min.features = 0, project = "ALL") 
    pbmc_batch@meta.data$batch=BATCH
    pbmc_batch=FindVariableFeatures(object = pbmc_batch, selection.method = "vst", nfeatures = 2000)   
    VariableFeatures(object = pbmc_batch)
    pbmc_batch <- NormalizeData(object = pbmc_batch, normalization.method = "LogNormalize", scale.factor = 10000)
    pbmc_batch <- ScaleData(object = pbmc_batch, features = VariableFeatures(object = pbmc_batch))
    pbmc_batch <- RunPCA(object = pbmc_batch, seed.use=123, npcs=50, features = VariableFeatures(object = pbmc_batch), ndims.print=1,nfeatures.print=1)
    pbmc_batch <- RunUMAP(pbmc_batch, dims = 1:50, seed.use = 123,n.components=2)
    DimPlot(pbmc_batch, reduction='umap', group.by='batch', pt.size=0.1)   
    
<img src="https://github.com/jumphone/BEER/raw/master/DATA/PLOT8.png" width="400">
       

#### Remove batch effect:

    pbmc <- mybeer$seurat  
    PCUSE=mybeer$select
    pbmc <- RunUMAP(object = pbmc, reduction='pca',dims = PCUSE, check_duplicates=FALSE)
    
    DimPlot(pbmc, reduction='umap', group.by='batch', pt.size=0.1)    
    
<img src="https://github.com/jumphone/BEER/raw/master/DATA/PLOT9.png" width="400">
   
    pbmc@meta.data$celltype=rep(NA,length(pbmc@meta.data$batch))
    pbmc@meta.data$celltype[which(pbmc@meta.data$batch=='RNA')]=pbmc.rna@meta.data$celltype
    
    DimPlot(pbmc, reduction='umap', group.by='celltype', pt.size=0.1,label=T)
    
<img src="https://github.com/jumphone/BEER/raw/master/DATA/PLOT10.png" width="400">

    saveRDS(mybeer, file='mybeer.final.RDS')
    

# It's not good enough !

### For further enhancement, please see [V. Batch-effect Removal Enhancement](#v-batch-effect-removal-enhancement).

</br>
</br>

# V. Batch-effect Removal Enhancement

Please install BBKNN: https://github.com/Teichlab/bbknn
    
This DEMO follows [IV. Combine scATAC-seq & scRNA-seq](#iv-combine-scatac-seq--scrna-seq)
    
    source('https://raw.githubusercontent.com/jumphone/BEER/master/BEER.R')
    #source('BEER.R')
    mybeer=readRDS('mybeer.final.RDS')
    pbmc.rna <- readRDS("./data/pbmc_10k_v3.rds")
    
    
### Use ComBat & BBKNN without BEER:

    pbmc <- mybeer$seurat
    PCUSE=c(1:ncol(pbmc@reductions$pca@cell.embeddings))
    pbmc=BEER.combat(pbmc) #Adjust PCs using ComBat
    umap=BEER.bbknn(pbmc, PCUSE, NB=3, NT=10)
    pbmc@reductions$umap@cell.embeddings=umap
    DimPlot(pbmc, reduction='umap', group.by='batch', pt.size=0.1,label=F)
    
    
<img src="https://github.com/jumphone/BEER/raw/master/DATA/PLOT12.png" width="400">     

### Use ComBat & BBKNN with BEER:

    pbmc <- mybeer$seurat
    PCUSE=mybeer$select   
    pbmc=BEER.combat(pbmc) #Adjust PCs using ComBat
    umap=BEER.bbknn(pbmc, PCUSE, NB=3, NT=10)
    pbmc@reductions$umap@cell.embeddings=umap
    DimPlot(pbmc, reduction='umap', group.by='batch', pt.size=0.1,label=F)
     
    saveRDS(pbmc, file='seurat.enh.RDS')
  
<img src="https://github.com/jumphone/BEER/raw/master/DATA/PLOT13.png" width="400"> 
  
    pbmc@meta.data$celltype=rep(NA,length(pbmc@meta.data$batch))
    pbmc@meta.data$celltype[which(pbmc@meta.data$batch=='RNA')]=pbmc.rna@meta.data$celltype
    DimPlot(pbmc, reduction='umap', group.by='celltype', pt.size=0.1,label=T)
     
<img src="https://github.com/jumphone/BEER/raw/master/DATA/PLOT14.png" width="400"> 

### Use BBKNN in Python:

Please download [beer_bbknn.py](https://raw.githubusercontent.com/jumphone/BEER/master/beer_bbknn.py).

    source('https://raw.githubusercontent.com/jumphone/BEER/master/BEER.R')
    #source('BEER.R')
    pbmc <- mybeer$seurat
    pbmc=BEER.combat(pbmc) #Adjust PCs using ComBat
    PCUSE = mybeer$select
    used.pca = pbmc@reductions$pca@cell.embeddings[,PCUSE]
    .writeTable(DATA=used.pca, PATH='used.pca.txt',SEP=',')
    .writeTable(DATA=pbmc@meta.data$batch, PATH='batch.txt',SEP=',')
    
Then, use "beer_bbknn.py" in your command line (please modify parameters in [beer_bbknn.py](https://raw.githubusercontent.com/jumphone/BEER/master/beer_bbknn.py)):

    python beer_bbknn.py

Finally, load the output of beer_bbknn.py and draw UMAP:

    umap=read.table('bbknn_umap.txt',sep='\t',header=FALSE)
    umap=as.matrix(umap)
    rownames(umap)=rownames(pbmc@reductions$umap@cell.embeddings)
    colnames(umap)=colnames(pbmc@reductions$umap@cell.embeddings)
    pbmc@reductions$umap@cell.embeddings=umap
    DimPlot(pbmc, reduction='umap', group.by='batch', pt.size=0.1,label=F)
     

</br>

# VI. Transfer labels

This DEMO follows [V. Batch-effect Removal Enhancement](#v-batch-effect-removal-enhancement)
   
    pbmc@meta.data$celltype=rep(NA,length(pbmc@meta.data$batch))
    pbmc@meta.data$celltype[which(pbmc@meta.data$batch=='RNA')]=pbmc.rna@meta.data$celltype
    #DimPlot(pbmc, reduction='umap', group.by='celltype', pt.size=0.1,label=T)
    
    #######
    VEC=pbmc@reductions$umap@cell.embeddings
    set.seed(123)
    N=150
    K=kmeans(VEC,centers=N)
    pbmc@meta.data$kclust=K$cluster   
    #DimPlot(pbmc, reduction='umap', group.by='kclust', pt.size=0.1,label=T)

    pbmc@meta.data$transfer=rep(NA, length(pbmc@meta.data$celltype))
    TMP=cbind(pbmc@meta.data$celltype, pbmc@meta.data$kclust)
    
    KC=unique(pbmc@meta.data$kclust)
    i=1
    while(i<=length(KC)){
        this_kc=KC[i]
        this_index=which(pbmc@meta.data$kclust==this_kc)
        this_tb=table(pbmc@meta.data$celltype[this_index])
        if(length(this_tb)!=0){
            this_ct=names(this_tb)[which(this_tb==max(this_tb))[1]]
            pbmc@meta.data$transfer[this_index]=this_ct}
        i=i+1}
        
    pbmc@meta.data$tf.ct=pbmc@meta.data$celltype
    NA.index=which(is.na(pbmc@meta.data$celltype))
    pbmc@meta.data$tf.ct[NA.index]=pbmc@meta.data$transfer[NA.index]
    
    ######
    RNA.cells=colnames(pbmc)[which(pbmc@meta.data$batch=='RNA')]
    ATAC.cells=colnames(pbmc)[which(pbmc@meta.data$batch=='ATAC')]
    
    library(ggplot2)
    
    plot.all <- DimPlot(pbmc, reduction='umap', group.by='batch', 
        pt.size=0.1,label=F) + labs(title = "Batches")
    
    plot.ct <- DimPlot(pbmc,reduction='umap', group.by='tf.ct', 
        pt.size=0.1,label=T) + labs(title = "CellType")
    
    plot.rna <- DimPlot(pbmc, cells=RNA.cells,reduction='umap', 
        group.by='tf.ct', pt.size=0.1,label=T,plot.title='RNA.transfer') + labs(title = "RNA")
    
    plot.atac <- DimPlot(pbmc, cells=ATAC.cells,reduction='umap', 
        group.by='tf.ct', pt.size=0.1,label=T,plot.title='ATAC.transfer') + labs(title = "ATAC")
    
    CombinePlots(list(all=plot.all, ct=plot.ct, rna=plot.rna, atac=plot.atac))
    

<img src="https://github.com/jumphone/BEER/raw/master/DATA/PLOT15.png" width="900"> 

If you want to visualize peak signals of any given cluster, please go to https://github.com/jumphone/VISA.

</br>
</br>

# VII. Biological Interpretation

Please install "RITANdata" and "RITAN".

RITAN: https://bioconductor.org/packages/devel/bioc/vignettes/RITAN/inst/doc/enrichment.html

This DEMO follows [IV. Combine scATAC-seq & scRNA-seq](#iv-combine-scatac-seq--scrna-seq)

    library(RITANdata)
    library(RITAN)
    
    PCUSE <- mybeer$select
    PCALL <- c(1:length(mybeer$cor))
    PCnotUSE <- PCALL[which(!PCALL %in% PCUSE)]
    
    LD=mybeer$seurat@reductions$pca@feature.loadings
    GNAME=rownames(LD)
    
    N=100
    getPosAndNegTop <- function(x){
        O=c(order(x)[1:N],order(x)[(length(x)-(N-1)):length(x)])
        G=GNAME[O]
        return(G)
        }
    
    GMAT=apply(LD,2,getPosAndNegTop)
    colnames(GMAT)=paste0(colnames(GMAT),'_R_',round(mybeer$cor,1),"_L_",round(mybeer$lcor,1))
    GMAT=toupper(GMAT)
    
    GMAT=GMAT[,PCnotUSE]
    #GMAT=GMAT[,PCUSE]
  
    study_set=list()
    TAG=colnames(GMAT)
    i=1
    while(i<=ncol(GMAT)){
         study_set=c(study_set,list(GMAT[,i]))
         i=i+1
         }  
         
    names(study_set)=TAG
    #names(geneset_list)
    resources=c('KEGG_filtered_canonical_pathways','MSigDB_Hallmarks')
    
    e <- term_enrichment_by_subset( study_set, q_value_threshold = 1e-5, 
                                resources = resources,
                                all_symbols = cached_coding_genes )
    
    plot( e, show_values = FALSE, label_size_y = 7, label_size_x = 7, cap=10 )
    
</br> 

<img src="https://github.com/jumphone/BEER/raw/master/DATA/PLOTEB.png" width="600"> 

</br>   
</br> 


# VIII. QC before using BEER

Download demo data: https://sourceforge.net/projects/beergithub/files/

### Step1. Load Data

    source('https://raw.githubusercontent.com/jumphone/BEER/master/BEER.R')

    #Load Demo Data (Oligodendroglioma, GSE70630)
    #Download: https://sourceforge.net/projects/beergithub/files/

    D1=readRDS('MGH36.RDS')
    D2=readRDS('MGH53.RDS')
    D3=readRDS('MGH54.RDS')
    D4=readRDS('MGH60.RDS')
    D5=readRDS('MGH93.RDS')
    D6=readRDS('MGH97.RDS')

    BATCH=c(rep('D1',ncol(D1)),
            rep('D2',ncol(D2)),
            rep('D3',ncol(D3)),
            rep('D4',ncol(D4)),
            rep('D5',ncol(D5)),
            rep('D6',ncol(D6)) )
        
    D12=.simple_combine(D1,D2)$combine
    D34=.simple_combine(D3,D4)$combine
    D56=.simple_combine(D5,D6)$combine
    D1234=.simple_combine(D12,D34)$combine
    D123456=.simple_combine(D1234,D56)$combine

    DATA=D123456   

    rm(D1);rm(D2);rm(D3);rm(D4);rm(D5);rm(D6)
    rm(D12);rm(D34);rm(D56);rm(D1234);rm(D123456)
    


### Step2. QC
    
    pbmc <- CreateSeuratObject(counts = DATA, project = "pbmc3k", min.cells = 0, min.features = 0)
    Idents(pbmc)=BATCH
    pbmc@meta.data$batch=BATCH
    
    pbmc <- subset(pbmc, subset = nFeature_RNA > 200 & nFeature_RNA < 2500 & percent.mt < 5)
    
Please fllow https://satijalab.org/seurat/v3.1/pbmc3k_tutorial.html to do Quality Control.

    
    BATCH=pbmc@meta.data$batch
    
    DATA=as.matrix(pbmc@assays$RNA@counts[,which(colnames(pbmc@assays$RNA@counts) %in% colnames(pbmc@assays$RNA@data))])
    
 

### Step3. BEER

Refer to [II. Combine Multiple Batches](#II-Combine-Multiple-Batches) 

</br> 




# Reference:

Feng Zhang, Yu Wu, Weidong Tian*; A novel approach to remove the batch effect of single-cell data, Cell Discovery, 2019, https://doi.org/10.1038/s41421-019-0114-x


### Differences between the latest version and the manuscript version

Latest version: https://github.com/jumphone/BEER/releases

Manuscript version: https://github.com/jumphone/BEER/archive/0.0.2.zip

<img src="https://github.com/jumphone/BEER/raw/master/DATA/DIFF.png" width="600">

</br>
</br>

    
# License
    
    MIT License
    
    Copyright (c) 2019 Zhang, Feng

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