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Comparison of Seurat and scanpy

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1. General Seurat data input

2. General scanpy data input

a. Seurat pipeline

b. Scanpy pipeline

1. General Seurat data input

⑴ Overview: Seurat is R-based (recently, R has been becoming less prominent compared to Python)

⑵ Case 1. When there is an .h5 file

library(dplyr)
library(Seurat)
pbmc.data <- Read10X(data.dir = "filtered_gene_bc_matrices/hg19/")

⑶ Case 2. When there are barcodes.tsv, genes.tsv, and matrix.mtx files

library(dplyr)
library(Seurat)
pbmc.data <- Read10X(data.dir = "filtered_gene_bc_matrices/hg19/")

① If a problem occurs because there is features.tsv instead of genes.tsv, rename that file to genes.tsv

⑷ Case 3. When the input file is not an .mtx file but an .rds file

library(dplyr)
library(Seurat)
data <- readRDS("C:/Users/sun/Desktop/GSM4557327_555_1_cell.counts.matrices.rds", refhook = NULL)

2. General scanpy data input

⑴ Overview: scanpy is Python-based. Some functions, such as read_visium, tend to be outsourced to packages such as squidpy

⑵ Main functions

① scanpy.read

② scanpy.read_10x_h5

③ scanpy.read_10x_mtx

④ scanpy.read_visium

⑤ scanpy.read_h5ad

⑥ scanpy.read_csv

⑦ scanpy.read_excel

⑧ scanpy.read_hdf

⑨ scanpy.read_loom

⑩ scanpy.read_mtx

⑪ scanpy.read_text

⑫ scanpy.read_umi_tools

⑶ Inputting open scRNA-seq datasets

⑷ Inputting open ST datasets

Input: 2023.11.10 17:23

Modified: 2026.05.08 00:02