Downstream analysis based on Anndata object

After running desc, you get an AnnData object. Then, you may want to do some downstream analysis in R.

Extract some information in Anndata based on desc analysis.

#`.obs` saved the metadata of each cell, such as nUMI, nGene, clusterID, et al.
df=adata.obs

Convert AnnData object in python to Seurat object in R

list.of.packages <- c("reticulate", "anndata",,"Seurat")
new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
if(length(new.packages)) install.packages(new.packages)

raw.data.matrix <- tryCatch(
expr = t(py_to_r(from$raw$X)),
    error = function(e) {
    stop("No adata.raw.X in provided adata. please make sure adata has adata.raw.X when you tyr to turn it to `seurat object`")
})

Some useful functions

suppressMessages(library(Seurat))# the version of Seurat <3.0
suppressMessages(library(reticulate))
#turn category obs in adata into string
change_obs=function(adata,exclude=c("n_genes","n_counts")){
#@exclude: the columns names
  n_col=as.numeric(as.character(adata$obs$columns$shape[0]))
  tmp=adata$obs$columns
  for (i in seq_len(n_col)){
    cur_colname=as.character(tmp[i-1])
    if (!cur_colname %in% exclude){
      adata$obs[[cur_colname]]=adata$obs[[cur_colname]]$astype("str")
    }
  }
  return(adata)
}


Convert_from_anndata_to_seurat=function(from=adata,X.slot="scale.data",raw.X.slot="logcount.data"){
        if(!py_module_available("anndata")) {
                stop("Please install the anndata python module")
         }
        stopifnot(X.slot%in%c("scale.data","normlizecount.data"))
        stopifnot(raw.X.slot%in%c("count.data","logcount.data"))
        data.matrix=tryCatch(
                expr=t(py_to_r(from$X)),
                        error=function(e){
                                stop("No adata.X in provided adata. If Both adata.X and adata.raw.X are None")
                        }
       )
        rownames(data.matrix)<-rownames(py_to_r(from$var))
        colnames(data.matrix)<-rownames(py_to_r(from$obs))
        if (X.slot=="normalizecount.data") X.slot="data"

        raw.data.matrix <- tryCatch(
                expr = t(py_to_r(from$raw$X)),
                error = function(e) {
                        stop("No adata.raw.X in provided adata. please make sure adata have adata.raw.X when you tyr to turn it to `seurat object`")
                }
        )
        if(raw.X.slot=="logcount.data"){
                raw.data.matrix=expm1(raw.data.matrix)
        }

        rownames(x = raw.data.matrix) <- rownames(x = py_to_r(from$raw$var))
        colnames(x = raw.data.matrix) <- rownames(x =py_to_r(from$obs))
        #get meta.data
        meta.data=py_to_r(from$obs)
        if ("nUMI" %in% colnames(x = meta.data)) {
                colnames(x = meta.data) <- gsub(
                pattern = "nUMI",
                replacement = "nUMI_ori",
                x = colnames(x = meta.data)
                )
        }
         if ("nGene" %in% colnames(x = meta.data)) {
                colnames(x = meta.data) <- gsub(
                pattern = "nGene",
                replacement = "nGene_ori",
                x = colnames(x = meta.data)
                )
        }
        seurat.object <- CreateSeuratObject(raw.data = raw.data.matrix,meta.data = meta.data)
        seurat.object <- SetAssayData(
                object = seurat.object,
                assay.type = "RNA",
                slot = X.slot,
                new.data = data.matrix
        )
        #deal with obsm fields that are not dimensional reductions, or have different name structures
        x1=py_to_r(from$obsm$keys())
        drs<-unlist(strsplit(gsub(".{1,50}:|\\s|)",replacement = "",x = x1),split = ","))
        for (dr in drs) {
                dr.embed <- py_to_r(from$obsm[[eval(dr)]])
                dr.name <- sub(pattern="X_",replacement="",x=dr)
                if (is.na(dr.name)|dr.name=="") {
                        dr.name <- dr
                }
                dr.dict <- list(tSNE_ = "tsne", PC = "pca")
                if (dr.name %in% dr.dict) {
                        dr.key <- names(x = which(x = dr.dict == dr.name))
                } else {
                        dr.key <- toupper(x = dr.name)
                }
                colnames(x = dr.embed) <- paste0(dr.key, 1:ncol(x = dr.embed))
                rownames(x = dr.embed) <- seurat.object@cell.names
                seurat.object <- SetDimReduction(
                        object = seurat.object,
                        reduction.type = dr.name,
                        slot = "cell.embeddings",
                        new.data = dr.embed
                )
                seurat.object <- SetDimReduction(
                        object = seurat.object,
                        reduction.type = dr.name,
                        slot = "key",
                        new.data = dr.key
                )
         }
        return(seurat.object)
}

# example
adata=change_obs(adata,exclude=c("n_genes","n_counts"))# columns to exclude
obj0=Convert_from_anndata_to_seurat(adata,raw.X.slot = "count.data")
obj0=NormalizeData(obj0,display.progress = F)

Updating…..