== Making diffusion maps with Slingshot ==

You can either assigned cells to clusters based on [https://bioconductor.org/packages/release/bioc/vignettes/slingshot/inst/doc/vignette.html | Slingshot protocol ] or make a single cell object, i.e. from the Seurat object. 

Example commands for convert to single cell object from Seurat. 
 
  * **Making a single cell object from a Seurat object** 
{{{
library(Seurat) 
library(SingleCellExperiment)
sce <- as.SingleCellExperiment(seuratObject)
#this has the cell classification
table(sce$ident)
}}}

== Calculate pseudotime and lineages with Slingshot ==

  You can use either PCA/UMAP/TSNE for reduction. Since Slingshot is designed for cases in which cells fall along a continuous trajectory, it's better to use PCA if clearly discrete clusters presented in UMAP, as mentioned in [https://github.com/kstreet13/slingshot/issues/43 | Slingshot github page]

{{{
sce <- slingshot(sce, clusterLabels = ident, reducedDim = "PCA",
                      allow.breaks = FALSE)
# get the lineages:
lnes <- getLineages(reducedDim(sce,"PCA"), sce$ident)
lnes@lineages

}}}

If you know the which cluster is the origin ( last cluster), you can assign starting cluster/last cluster:

{{{
sce <- slingshot(sce, clusterLabels = ident, reducedDim = "PCA",
                      allow.breaks = FALSE, start.clus="2")
# get the lineages:
lnes <- getLineages(reducedDim(sce2,"PCA"),
                    sce2$ident, start.clus = "2")
lnes@lineages
}}}

== Visualize the pseudotime or lineages ==

You can draw plot with first pseudotime as x-axis, and y-axis is the cell type. If you have multiple lineages, cells that were identified as being specific to Lineage 2 will have NA values for slingPseudotime_1 ( [https://github.com/kstreet13/slingshot/issues/42 | slingshot github page] )

{{{

library(Polychrome)
library(ggbeeswarm)
library(ggthemes)

# this define the cluster color. You can change it with different color scheme.
my_color <- createPalette(length(levels(sce$ident)), c("#010101", "#ff0000"), M=1000)
names(my_color) <- unique(as.character(sce$ident))

slingshot_df <- data.frame(colData(sce))

# re-order y-axis for better figure: This should be tailored with your own cluster names
# slingshot_df$ident = factor(slingshot_df$ident, levels=c(4,2,1,0,3,5,6))

ggplot(slingshot_df, aes(x = slingPseudotime_1, y = ident, 
                              colour = ident)) +
    geom_quasirandom(groupOnX = FALSE) + theme_classic() +
    xlab("First Slingshot pseudotime") + ylab("cell type") +
    ggtitle("Cells ordered by Slingshot pseudotime")+scale_colour_manual(values = my_color)


}}}

To plot lineages along the cells in PCAs:

{{{

plot(reducedDims(sce)$PCA, col = my_color[as.character(sce$ident)], 
     pch=16, 
     asp = 1)
legend("bottomleft",legend = names(my_color[levels(sce$ident)]),  
       fill = my_color[levels(sce$ident)])
lines(SlingshotDataSet(lnes), lwd=2, type = 'lineages', col = c("black"))

}}}