CellBench: single cell RNA-seq benchmarking
CellBench uses three human lung adenocarcinoma cell lines HCC827, H1975 and H2228, which were cultured separately, and then processed in three different ways. Firstly, single cells from each cell line were mixed in equal proportions, with libraries generated using three different protocols: CEL-seq2, Drop-seq (with Dolomite equipment) and 10X Chromium. Secondly, the single cells were sorted from the three cell lines into 384-well plates, with an equal number of cells per well in different combinations (generally 9-cells, but with some 90-cell population
controls). Thirdly, RNA was extracted in bulk for each cell line and the RNA was mixed in 7 different proportions and diluted to single cell equivalent amounts ranging from 3.75pg to 30pg and preocessed using CEL-seq 2 and SORT-seq. ERCC spike-in controls were present in samples processed using the 2 plate-based technologies (CEL-seq2 and SORT-seq).
Raw data from this series of experiments is available under GEO accession number GSE118767. The processed count data obtained from scPipe is stored in R objects that use the SingleCellExperiment class. Below are instructions for getting the count data and metadata (including annotations) for each dataset. All data is post sample quality control, without gene filtering.
Summary of all datasets
Load files into R
You can find R object files in the data folder
load("data/sincell_with_class.RData")
This will create three variables: sce10x_qc
, sce4_qc
, and scedrop_qc_qc
. sce10x_qc
contains the read counts after quality control processing from the 10x platform. sce4_qc
contains the read counts after quality control processing from the CEL-seq2 platform. scedrop_qc_qc
contains the read counts after quality control proessing from the Drop-seq platform.
Counts
To access count data from a SingleCellExperiment object, use the counts(sce)
function:
counts(sce10x_qc)[1:5, 1:5]
Metadata
To access sample information from a SingleCellExperiment object, use the colData(sce)
function:
head(colData(sce10x_qc))
Examples of using these datasets
You can find an Rnotebook in the script/data_QC_visualization folder named data_explore_mixture.Rmd
which includes code for analysing the cell mixture and RNA mixture datasets.
Scripts for reproducing a broader methods comparison
The [script] folder contains scripts that can reproduce the analysis and figures from our paper: Benchmarking single cell RNA-sequencing analysis pipelines using mixture control experiments.
Note: The ggtern
package, which has been used to generate the ternary plots, has known issues with recent versions of ggplot
and the relevant code may be broken if you have updated the ggplot
package.