https://github.com/jumphone/beer

BEER: Batch EffEct Remover for single-cell data
https://github.com/jumphone/beer

batch-effects scatac-seq scrna-seq single-cell-analysis umap

Last synced: 10 months ago
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BEER: Batch EffEct Remover for single-cell data

• Host: GitHub
• URL: https://github.com/jumphone/beer
• Owner: jumphone
• License: mit
• Created: 2019-03-07T16:11:40.000Z (about 7 years ago)
• Default Branch: master
• Last Pushed: 2025-01-09T06:01:33.000Z (about 1 year ago)
• Last Synced: 2025-04-02T17:22:34.158Z (12 months ago)
• Topics: batch-effects, scatac-seq, scrna-seq, single-cell-analysis, umap
• Language: R
• Homepage:
• Size: 60.1 MB
• Stars: 24
• Watchers: 2
• Forks: 9
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          


# 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)

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

# Workflow:

#### Latest version



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

# 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

# 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)

# Set Python

    library(reticulate)

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

    py_config()

# 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)

    

        



    

### 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) 

    



     

#### 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) 

    



      

    

    

    

# 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))

    



    

### 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) 

 

    



#### 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)   

    



   

    

   

# 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)

    

    

    # Or, manually select some cells

    ppp=DimPlot(pbmc, reduction='umap', pt.size=0.5)

    used.cells <- CellSelector(plot = ppp)

    

    

    # Press "ESC"

    

    

    

    markers <- FindMarkers(pbmc, ident.1=used.cells,only.pos=T)    

    head(markers, n=20)

    

    

   

# 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))

    

   

### 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)   

    



       

#### 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)    

    



   

    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)

    



    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).

# 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)

    

    

     

### 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')

  

 

  

    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)

     

 

### 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)

     

# 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))

    

 

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

# 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 )

    

 

 

   

 

# 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) 

 

# 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





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#### More tools & studies: https://fzhang.bioinfo-lab.com/

    

# License

    

    MIT License

    

    Copyright (c) 2019 Zhang, Feng

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    in the Software without restriction, including without limitation the rights

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