This data set is taken from the publication "Extreme heterogeneity of influenza virus infection in single cells" by Russell et al., eLIFE (2018) (DOI, GEO submission). It contains ~13,000 cells from 4 samples before and 6/8/10h after infection with the influenza virus.
To test Cerebro, download the .crb file from either Seurat v3 or scanpy and load it into Cerebro.
The workflows of all three frameworks are conceptually the same, containing the following steps:
- Load the transcript counts.
- Filter cells based on the number of transcripts and expressed genes.
- Normalize the transcript counts and scaled each cell to contain 10,000 transcripts.
- Identify variable genes.
- Scale the expression matrix and regressing out the number of transcripts.
- Perform cell cycle analysis.
- Perform principal component analysis.
- Identify clusters and build a cluster tree.
- Perform dimensional reduction.
Then, using the functions of cerebroApp, we add some more data:
- Calculate the percent of mitochondrial and ribosomal gene expression (
addPercentMtRibo()). - Get the most expressed genes in each sample and cluster (
getMostExpressedGenes()). - Get marker genes for each sample and cluster (
getMarkerGenes()). - Perform pathway enrichment analysis using the marker genes of each sample and cluster (
getEnrichedPathways()). - Perform gene set enrichment analysis for each sample and cluster (
performGeneSetEnrichmentAnalysis()).
Next, we calculate trajectories of (1) all cells and (2) a subset of cells (those in G1 phase) using Monocle v2 and the variable features identified by Seurat.
We extract these trajectories from the respective Monocle objects and add them to our Seurat object through the extractMonocleTrajectory() function.
Lastly, from the Seurat object we export a Cerebro file (.crb extension) that can be loaded into Cerebro (exportFromSeurat()).
The example data sets were generated using the official Cerebro Docker image which was built in Docker (Docker Hub) and imported into Singularity (here I used Singularity 2.6.0). Details and descriptions for all workflows can be found in the respective directories Seurat v3, and scanpy.