singleCellTK 1.6.0

The analysis modules available through the Shiny app are also available as R functions for standard R console processing of single cell RNA-Seq data using a SCtkExperiment object. At any stage, you can load the Shiny App to interactively visualize and analyze a data set, but this vignette will show a standard workflow run entirely through the R console.

The MAST package contains a convenient scRNA-Seq example data set of 96 Mucosal Associated Invariant T cells (MAITs), half of which were stimulated with cytokines to induce a response. For more details, consult the MAST package and vignette.

We will first convert the MAST example dataset to a SCtkExperiment object.

```
suppressPackageStartupMessages({
library(MAST)
library(singleCellTK)
library(xtable)
})
data(maits, package="MAST")
maits_sce <- createSCE(assayFile = t(maits$expressionmat),
annotFile = maits$cdat,
featureFile = maits$fdat,
assayName = "logtpm",
inputDataFrames = TRUE,
createLogCounts = FALSE)
rm(maits)
```

You can get summary metrics with the `summarizeTable`

function:

`knitr::kable(summarizeTable(maits_sce, useAssay = "logtpm"))`

Metric | Value |
---|---|

Number of Samples | 96 |

Number of Genes | 16302 |

Average number of reads per cell | 17867 |

Average number of genes per cell | 6833 |

Samples with <1700 detected genes | 5 |

Genes with no expression across all samples | 0 |

Typically, these summary statistics would be run on a “counts” matrix, but here we have log(tpm) values so the average number of reads per cell is calculated from the normalized values instead of raw counts.

Explore the available annotations in the data:

`colnames(colData(maits_sce))`

```
## [1] "wellKey" "condition" "nGeneOn"
## [4] "libSize" "PercentToHuman" "MedianCVCoverage"
## [7] "PCRDuplicate" "exonRate" "pastFastqc"
## [10] "ncells" "ngeneson" "cngeneson"
## [13] "TRAV1" "TRBV6" "TRBV4"
## [16] "TRBV20" "alpha" "beta"
## [19] "ac" "bc" "ourfilter"
```

`table(colData(maits_sce)$ourfilter)`

```
##
## FALSE TRUE
## 22 74
```

The data has a filtered dataset with 74 ‘pass filter’ samples, let’s subset the data to include the pass filter samples

```
maits_subset <- maits_sce[, colData(maits_sce)$ourfilter]
table(colData(maits_subset)$ourfilter)
```

```
##
## TRUE
## 74
```

`knitr::kable(summarizeTable(maits_subset, useAssay = "logtpm"))`

Metric | Value |
---|---|

Number of Samples | 74 |

Number of Genes | 16302 |

Average number of reads per cell | 16292 |

Average number of genes per cell | 7539 |

Samples with <1700 detected genes | 0 |

Genes with no expression across all samples | 157 |

Initially, there are no reduced dimensionality datasets stored in the object

`reducedDims(maits_subset)`

```
## List of length 0
## names(0):
```

PCA and t-SNE can be added to the object with the getPCA() and getTSNE() functions:

```
maits_subset <- getPCA(maits_subset, useAssay = "logtpm",
reducedDimName = "PCA_logtpm")
maits_subset <- getTSNE(maits_subset, useAssay = "logtpm",
reducedDimName = "TSNE_logtpm")
reducedDims(maits_subset)
```

```
## List of length 2
## names(2): PCA_logtpm TSNE_logtpm
```

PCA data can be visualized with the plotPCA() function:

`plotPCA(maits_subset, reducedDimName = "PCA_logtpm", colorBy = "condition")`

t-SNE data can be visualized with the plotTSNE() function:

`plotTSNE(maits_subset, reducedDimName = "TSNE_logtpm", colorBy = "condition")`

The singleCellTK has the ability to convert gene ids to various formats using the org.*.eg.db Bioconductor annotation packages. These packages are not installed by default, so these must be manually installed before this function will work.

```
suppressPackageStartupMessages({
library(org.Hs.eg.db)
})
maits_entrez <- maits_subset
maits_subset <- convertGeneIDs(maits_subset, inSymbol = "ENTREZID",
outSymbol = "SYMBOL", database = "org.Hs.eg.db")
#to remove confusion for MAST about the gene name:
rowData(maits_subset)$primerid <- NULL
```

MAST is a popular package for performing differential expression analysis on scRNA-Seq data that models the effect of dropouts using a bimodal distribution and by including the cellular detection rate into the differential expression model. Functions in the toolkit allow you to perform this analysis on a SCtkExperiment object.

First, an adaptive threshold is calculated by binning genes with similar expression levels.

```
thresholds <- thresholdGenes(maits_subset, useAssay = "logtpm")
par(mfrow = c(5, 4))
plot(thresholds)
par(mfrow = c(1, 1))
```