sce_full_SimKumar: SimKumar data sets

In csoneson/DuoClustering2018: Data, Clustering Results and Visualization Functions From Duò et al (2018)

SimKumar data sets

Description

Gene counts for scRNA-seq data sets simulated with the splatter package.

Usage

sce_full_SimKumar4easy(metadata = FALSE)
sce_filteredExpr10_SimKumar4easy(metadata = FALSE)
sce_filteredHVG10_SimKumar4easy(metadata = FALSE)
sce_filteredM3Drop10_SimKumar4easy(metadata = FALSE)
sce_full_SimKumar4hard(metadata = FALSE)
sce_filteredExpr10_SimKumar4hard(metadata = FALSE)
sce_filteredHVG10_SimKumar4hard(metadata = FALSE)
sce_filteredM3Drop10_SimKumar4hard(metadata = FALSE)
sce_full_SimKumar8hard(metadata = FALSE)
sce_filteredExpr10_SimKumar8hard(metadata = FALSE)
sce_filteredHVG10_SimKumar8hard(metadata = FALSE)
sce_filteredM3Drop10_SimKumar8hard(metadata = FALSE)

Arguments

metadata

Logical, whether only metadata should be returned

Format

SingleCellExperiment

Details

Using one subpopulation of the sce_full_Kumar data set as input, scRNA-seq data with known group structure was simulated with the splatter package from Zappia et al. (2017). The simulated data have been used to evaluate the performance of clustering algorithms in Duò et al. (2018).

Three data sets have been generated, each consisting of 500 cells and approximately 43,000 features, with varying degree of cluster separability. The sce_full_SimKumar4easy data set consists of 4 subpopulations with relative abundances 0.1, 0.15, 0.5 and 0.25, and probabilities of differential expression set to 0.05, 0.1, 0.2 and 0.4 for the four subpopulations, respectively. The sce_full_SimKumar4hard data set consists of 4 subpopulations with relative abundances 0.2, 0.15, 0.4 and 0.25, and probabilities of differential expression 0.01, 0.05, 0.05 and 0.08. Finally, the sce_full_SimKumar8hard data set consists of 8 subpopulations with relative abundances 0.13, 0.07, 0.1, 0.05, 0.4, 0.1, 0.1 and 0.05, and probabilities of differential expression equal to 0.03, 0.03, 0.03, 0.05, 0.05, 0.07, 0.08 and 0.1, respectively.

The scater package was used to perform quality control of the data sets (McCarthy et al. (2017)). Features with zero counts across all cells, as well as cells with total count or total number of detected features more than 3 median absolute deviations (MADs) below the median across all cells (on the log scale), were excluded. The filteredExpr, filteredHVG and filteredM3Drop10 are further reduced data sets. For each of the filtering method, we retained 10 percent of the original number of genes (with a non-zero count in at least one cell) in the original data sets.

For the filteredExpr data sets, only the genes with the highest average expression (log-normalized count) value across all cells were retained. Using the Seurat package, the filteredHVG data sets were filtered on the variability of the features and only the most highly variable ones were retained (Satija et al. (2015)). Finally, the M3Drop package was used to model the dropout rate of the genes as a function of the mean expression level using the Michaelis-Menten equation and select variables to retain for the filteredM3Drop10 data sets (Andrews and Hemberg (2018)).

The scater package was used to normalize the count values, based on normalization factors calculated by the deconvolution method from the scran package (Lun et al. (2016)). This data set is provided as a SingleCellExperiment object (Lun and Risso (2017)). For further information on the SingleCellExperiment class, see the corresponding manual.

Value

Returns a SingleCellExperiment object.

References

Andrews, T.S., and Hemberg, M. (2018). Dropout-based feature selection for scRNASeq. bioRxiv doi:https://doi.org/10.1101/065094.

Duò, A., Robinson, M.D., and Soneson, C. (2018). A systematic performance evaluation of clustering methods for single-cell RNA-seq data. F1000Res. 7:1141.

Lun, A.T.L., Bach, K., and Marioni, J.C. (2016) Pooling across cells to normalize single-cell RNA sequencing data with many zero counts. Genome Biol. 17(1): 75.

Lun, A.T.L., and Risso, D. (2017). SingleCellExperiment: S4 Classes for Single Cell Data. R package version 1.0.0.

McCarthy, D.J., Campbell, K.R., Lun, A.T.L., and Wills, Q.F. (2017): Scater: pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R. Bioinformatics 33(8): 1179-1186.

Satija, R., Farrell, J.A., Gennert, D., Schier, A.F., and Regev, A. (2015). Spatial reconstruction of single-cell gene expression data. Nat. Biotechnol. 33(5): 495–502.

Zappia, L., Phipson, B., and Oshlack, A. (2017). Splatter: simulation of single-cell RNA sequencing data. Genome Biol. 18(1): 174.

Examples

sce_filteredExpr10_SimKumar4easy()

csoneson/DuoClustering2018 documentation built on July 4, 2023, 2:23 p.m.