Single Cell Visualizations¶
If you would like to make your single-cell RNA-seq data publicly available on a website, for example as a supplement for a publication, we can help you with that!
We use cellxgene to visualize single cell RNA-seq data. cellxgene is an interactive data explorer which is very scalable and flexible.
To be able for us to create a cellxgene website for your data we need to have your data in the h5ad (AnnData) format.
AnnData format usually contains the following slots:
- X contains the expression matrix.
- obsm contains the embeddings data.
- obs contains the cell metadata.
- var contains the gene metadata.
When you work with cellxgene you only need to modify two of the slots above: obsm and obs.
cellxgene works faster when the expression matrix is stored in
CSC (compressed sparse column) format instead of
CSR (compressed sparse row) format or dense Numpy array (which sometimes can create a smaller
h5ad file depending on the sparsity of your data).
To convert your expression matrix into the
CSC format please use:
adata.X = scipy.sparse.csc_matrix(adata.X)
To convert your expression matrix into the Numpy array please use:
adata.X = scipy.sparse.csc_matrix.toarray(adata.X)
To visualize your cells in 2D cellxgene uses obsm slot. If there are multiple embeddings stored in this slot they will all be available on the web interface.
cellxgene requires that all of the embeddings’ names are prefixed with
X_. For example,
To highlight and colour your cells cellxgene uses obs slot. The colouring will depend on the type of you cell metadata contained in the obs slot.
When the metadata is categorical, i.e. there is one colour per category, the visualization will look like this:
To make your cell metadata categorical please use the following code:
import pandas as pd adata.obs['metadata_name'] = pd.Categorical(adata.obs['metadata_name'])
When the metadata is continuous, the visualization will look like this:
Note there is a continuous scale on the right side of the plot.
To make your cell metadata continuous please use the following code:
import numpy as np adata.obs['metadata_name'] = np.float32(adata.obs['metadata_name'])
We have released the
sceasy package on GitHub (https://github.com/cellgeni/sceasy) to easily convert other single-cell file types to AnnData format for visualization with cellxgene. Currently it supports converting Seurat, SingleCellExperiment and Loom objects to AnnData. By default it transfers expression matrices, cell and gene metadata table, and, if available, cell embeddings in reduced dimensions to AnnData.
Before installing the conda packages below please first create a new conda environment
EnvironmentName and activate it. Everything else can be installed in
sceasy is installable either as a
conda install -c bioconda r-sceasy
or as an
sceasy ensure the
anndata package (version has to be < 0.6.20) is installed:
conda install anndata == 0.6.19 -c bioconda
In addition, please also ensure the
loompy package (loompy version < 3.0.0) is installed:
conda install loompy == 2.0.17 -c bioconda
You will also need to install
Finally, before converting your data please load the following libraries in your
library(sceasy) library(reticulate) use_condaenv('EnvironmentName') loompy <- reticulate::import('loompy')
Seurat to AnnData¶
sceasy:::convertFormat(seurat_object, from="seurat", to="anndata", outFile='filename.h5ad')
Seurat to SingleCellExperiment¶
sceasy:::convertFormat(seurat_object, from="seurat", to="sce", outFile='filename.rds')
SingleCellExperiment to AnnData¶
sceasy:::convertFormat(sce_object, from="sce", to="anndata", outFile='filename.h5ad')
SingleCellExperiment to Loom¶
sceasy:::convertFormat(sce_object, from="sce", to="loom", outFile='filename.loom')
Loom to AnnData¶
sceasy:::convertFormat('filename.loom', from="loom", to="anndata", outFile='filename.h5ad')
Loom to SingleCellExperiment¶
sceasy:::convertFormat('filename.loom', from="loom", to="sce", outFile='filename.rds')
We have already created a couple of websites for some of our programme members. You can have a look at them at the following links: