17-SeuratVignetteMapping.Rmd

---
title: "Seurat Vignette : Mapping and annotating query datasets"
output: github_document
---

# Load libraries

```{r, message = FALSE, warning = FALSE, load-libraries}
library(Seurat)
library(SeuratData)
library(ggplot2)
```

# Load Datasets

```{r}
InstallData("panc8")
panc8 <- LoadData("panc8")
panc8 <- UpdateSeuratObject(panc8)
```

# Start workflow

```{r}
pancreas.ref <- subset(panc8, tech %in% c("celseq2", "smartseq2"))
pancreas.ref[["RNA"]] <- split(pancreas.ref[["RNA"]], f = pancreas.ref$tech)

# pre-process dataset (without integration)
pancreas.ref <- NormalizeData(pancreas.ref)
pancreas.ref <- FindVariableFeatures(pancreas.ref)
pancreas.ref <- ScaleData(pancreas.ref)
pancreas.ref <- RunPCA(pancreas.ref)
pancreas.ref <- FindNeighbors(pancreas.ref, dims = 1:30)
pancreas.ref <- FindClusters(pancreas.ref)
```


```{r}
pancreas.ref <- RunUMAP(pancreas.ref, dims = 1:30)
DimPlot(pancreas.ref, group.by = c("celltytpe", "tech"))
```

```{r}
pancreas.ref <- IntegrateLayers(object = pancreas.ref, method = CCAIntegration, orig.reduction = "pca",
    new.reduction = "integrated.cca", verbose = FALSE)
pancreas.ref <- FindNeighbors(pancreas.ref, reduction = "integrated.cca", dims = 1:30)
pancreas.ref <- FindClusters(pancreas.ref)
```


```{r}
pancreas.ref <- RunUMAP(pancreas.ref, reduction = "integrated.cca", dims = 1:30)
DimPlot(pancreas.ref, group.by = c("tech", "celltype"))
```
# Cell Type classification using integrated reference

Two distinctions between data transfer and integration

- In data transfer, Seurat does not correct or modify the query expression data.

- In daa transfer, Seurat has an option to project the PCA structure of the reference onto the query.

After finding anchors, we transfer data to classify the query cells based on reference data.

```{r}
# select two technologies for the query datasets
pancreas.query <- subset(panc8, tech %in% c("fluidigmc1", "celseq"))
pancreas.query <- NormalizeData(pancreas.query)
pancreas.anchors <- FindTransferAnchors(reference = pancreas.ref, query = pancreas.query, dims = 1:30,
    reference.reduction = "pca")
predictions <- TransferData(anchorset = pancreas.anchors, refdata = pancreas.ref$celltype, dims = 1:30)
pancreas.query <- AddMetaData(pancreas.query, metadata = predictions)
```

```{r}
pancreas.query$prediction.match <- pancreas.query$predicted.id == pancreas.query$celltype
table(pancreas.query$prediction.match)
```

```{r, fig.width = 12, fig.height = 9}
VlnPlot(pancreas.query, c("REG1A", "PPY", "SST", "GHRL", "VWF", "SOX10"), group.by = "predicted.id")
```

# Umimodal UMAP Projection

Map query is a combination of TransferData, IntegrateEmbeddings, ProjectUMAP all at once.

```{r}
pancreas.ref <- RunUMAP(pancreas.ref, dims = 1:30, reduction = "integrated.cca", return.model = TRUE)
pancreas.query <- MapQuery(anchorset = pancreas.anchors, reference = pancreas.ref, query = pancreas.query,
    refdata = list(celltype = "celltype"), reference.reduction = "pca", reduction.model = "umap")
```

```{r}
p1 <- DimPlot(pancreas.ref, reduction = "umap", group.by = "celltype", label = TRUE, label.size = 3,
    repel = TRUE) + NoLegend() + ggtitle("Reference annotations")
p2 <- DimPlot(pancreas.query, reduction = "ref.umap", group.by = "predicted.celltype", label = TRUE,
    label.size = 3, repel = TRUE) + NoLegend() + ggtitle("Query transferred labels")
p1 + p2
```