A python package for managing single-cell RNA-seq datasets.
SingleCell is a python class available in the singlecelldata package for managing single-cell RNA-seq data. It contains three pandas dataframes;
data for holding gene expression values (counts/normalized counts),
genedata for holding more information about the genes e.g., gene names, and
celldata which contains more information about cells such as cell types, labels etc.
The singlecelldata package can be easily installed using the following command:
pip install singlecelldata
The SingleCell class reference manual can be found here
Using the SingleCell class
The SingleCell class can be used to create an object which stores single-cell gene expression data and additional data about genes and cells in their respective dataframes. To create a SingleCell object sc, the following python code can be used:
import pandas as pd from singlecelldata import SingleCell dataset = 'biase' data_path = "data/" + dataset + '/' + dataset + "_data.csv" celldata_path = "data/" + dataset + '/' + dataset + "_celldata.csv" genedata_path = "data/" + dataset + '/' + dataset + "_genedata.csv" # Create pandas dataframes by reading data from files data = pd.read_csv(data_path, index_col=0) celldata = pd.read_csv(celldata_path, index_col=0) genedata = pd.read_csv(genedata_path, index_col = 0) # Create a single cell object sc = SingleCell(dataset, data, celldata, genedata)
In the above example, a SingleCell object, sc, was ceated by passing the dataset name and the main data, the cell data and gene data as Pandas dataframes. Pandas is a powerpul python library for creating data structures from a variety of sources. Pandas can open and read data from numerous differernt file types such as csv files and creating dataframes from it. This enables the user to create SingleCell objects from a variety of different data file types.
More detailed example can be found here
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