Monocle是單細(xì)胞分析的常用R包之一，可進(jìn)行細(xì)胞類型鑒定、差異表達(dá)、偽時(shí)間分析等。

1.安裝Monocle

支持環(huán)境R3.4以上版本

獲取Mnonocle：

先安裝Bioconductor?

source("http://bioconductor.org/biocLite.R")?

biocLite()?

已經(jīng)安裝了Bioconductor的可直接安裝Monocle和支持包

檢測(cè)安裝是否成功

?library(monocle)

?2.推薦分析protocol

工作流程：上傳數(shù)據(jù)到CellDataSet對(duì)象中——用已知Marker進(jìn)行細(xì)胞分類——聚類細(xì)胞——偽時(shí)間排序——差異表達(dá)分析

上傳數(shù)據(jù)到CDS中

pd <- new("AnnotatedDataFrame", data = sample_sheet)

?fd <- new("AnnotatedDataFrame", data = gene_annotation)

?cds <- newCellDataSet(expr_matrix, phenoData = pd, featureData = fd)

expr_matrix的colnames必須與pd的rownames一致

用Marker分類細(xì)胞

cth <- newCellTypeHierarchy()?

MYF5_id <- row.names(subset(fData(cds), gene_short_name == "MYF5"))?

ANPEP_id <- row.names(subset(fData(cds), gene_short_name == "ANPEP"))

?cth <- addCellType(cth, "Myoblast", classify_func = function(x) { x[MYF5_id,] >= 1 })

?cth <- addCellType(cth, "Fibroblast", classify_func = function(x) { x[MYF5_id,] < 1 & x[ANPEP_id,] > 1 } )?

cds <- classifyCells(cds, cth, 0.1)?

聚類細(xì)胞

cds <- clusterCells(cds)

偽時(shí)間分析

disp_table <- dispersionTable(cds)?

ordering_genes <- subset(disp_table, mean_expression >= 0.1)?

cds <- setOrderingFilter(cds, ordering_genes)

?cds <- reduceDimension(cds)

?cds <- orderCells(cds)?

差異表達(dá)分析

diff_test_res <- differentialGeneTest(cds, fullModelFormulaStr = "~Media")?

sig_genes <- subset(diff_test_res, qval < 0.1)