paul15数据集scanpy和Seurat可视化python
import scanpy as sc
adata=sc.datats.paul15()
alize_per_cell(adata,counts_per_cell_after=1e4)# 这⼀部也是需要加⼊进去的。
西红柿英语
sc.pp.filter_genes(adata,min_cells=1)
sc.pp.filter_genes_dispersion(adata,n_top_genes =2000)#top 1000 gene
#log1p data
sc.pp.log1p(adata)
sc.pp.scale(adata)
ighbors(adata)
sc.tl.umap(adata)
sc.pl.umap(adata,color=["paul15_clusters"])
最终的结果如图
这⾥我发现⼀个问题，如果不是看保存成png的图，这个图的图例是少了了，其实并不是少了，是因
为右边没有显⽰出来，图⽚显⽰太⼩真正的结果如下
R
suppressPackageStartupMessages({
library(SingleCellExperiment)
library(Seurat)
})
paul=readRDS("./paul.rds")
不知疲倦的意思# 这个数据是sc.datats.pual15()是⼀样的，只不过是数据的信息
print("======paul信息=========")
古剑奇谭主题曲
print(paul)
## create Seurat object
paul <- CreateSeuratObject(assay(paul,"X"),meta.data = as.data.frame(colData(paul)),row.names = rownames(paul))
老顾客
# meta.data不使⽤as.data.frame就会发⽣下⾯的错误
竭蹶#Error in h(simpleError(msg, call)): 在为'['函数选择⽅法时评估'i'参数出了错: 在为'duplicated'函数选择⽅法时评估'x'参数出了错: DataFrame object with NULL colnames, plea fix it with colnames(x) <- value
推荐材料#Traceback:
## Normalize the count data prent in a given assay.
paul <- NormalizeData(object = paul)
## Identifies features that are outliers on a 'mean variability plot'.
paul <- FindVariableFeatures(object = paul)
## Scales and centers features in the datat. If variables are provided ress, they are individually regresd against each feautre, and the res ulting residuals are then scaled and centered.
paul <- ScaleData(
object = paul
)
## Run a PCA dimensionality reduction. For details about stored PCA calculation parameters, e PrintPCAParams.
paul <- RunPCA(
object = paul,马简笔画图片大全
初一寒假日记
#runPCA和RunUMAP是同时等价地位的。
paul <- RunUMAP(paul, reduction ="pca", dims =1:50,verbo = F)
DimPlot(paul, reduction ="umap", group.by ="paul15_clusters",label.size =10)
