adata.write_h5ad('output.h5ad') 方案2:数据读取时就指定索引名称# 读取barcodes文件时指定索引名称 obs = pd.read_csv('barcodes.tsv', index_col=0, header=None) obs.index.name = 'cell_barcode' 读取features文件时指定索引名称 var = pd.read_csv('features.tsv', sep='\t', header=None) va...
adata = diopy.input.read_h5(file = 'scdata.h5') anndata for R 官方提供的演示案例 Single-cell data from Python to R # in Pythondiopy.output.write_h5(data_py,file='scdata.h5')# in Radata=dior::read_h5(file='scdata.h5',target.object='seurat') Seurat object 官方提供的演示案例 空...
adata = sc.read_loom("/Users/yuanzan/Desktop/tmp/sdata.loom", sparse=True, cleanup=False, X_name='spliced', obs_names='CellID', var_names='Gene', dtype='float32')
library(anndataR) adata <- read_h5ad("test.h5ad", to = "InMemoryAnnData") SeuratObject <- adata$to_Seurat() 报错信息: Warning message: In value[[3L]](cond): Error reading element'var'oftype'dataframe': HDF5-API Errors: error#000: H5A.c in H5Aread(): line 1043: can't synchro...
adata.write("./write/my_results.h5ad") adata.write_csvs('./write/my_results_csvs', ) 6、读取数据 代码语言:javascript 代码运行次数:0 运行 AI代码解释 import scanpy as sc import pandas as pd # 初始化数据 adata = sc.read(filename) # 加入数据 anno = pd.read_csv(filename_sample_annot...
library(anndataR) h5ad_path <- system.file("extdata", "example.h5ad", package = "anndataR") Read an h5ad file: adata <- read_h5ad(h5ad_path, to = "InMemoryAnnData") View structure: adata #> class: InMemoryAnnData #> dim: 50 obs x 100 var #> X: dgRMatrix #> layers: coun...
idea: move read_zarr in together with .h5ad as “native file format” So onereadfunction? We then need a dependency onzarrthen probably experimental.sparse_dataset needs to come to the same place as CS{RC}Dataset or be removed. Exported then, it is fairly stable. ...
⑤ 将矩阵读入储存为h5ad文件(在sh脚本中运行python代码) 该步骤会在sh脚本中运行python代码,将表达矩阵读为anndata对象并储存为h5ad文件将有利于后续读入数据,可以较快读取速度,但是会占用磁盘空间。 所导出的h5ad文件命名为:SP00_[SAMPLE_NAME].h5ad
new_anndata_name = scanpy.read('path/anndata_name.h5ad') 数据载入后,要把此数据赋值给一个新的变量名,以方便后续调用 >>>import scanpy as sc>>>adata=sc.read('path/adata.h5ad')>>>adata#adata是一个anndata类的对象>>>AnnDataobject with n_obs × n_vars=11097×12319var:'gene_ids','featu...
data = sc.read_10x_mtx("GSE163558_RAW/"+dir[i], var_names="gene_symbols", cache=True) data.var_names_make_unique() adata[dir[i]] = data print(dir[i]) # 使用 concat 函数将多个adata连接在一起: adata = sc.concat(adata,label='sampleid') ...