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A small toy dataset is included in the package. The toy dataset includes:
dat.expression
: a toy scRNA-seq dataset with genes
(rows) and cells (columns)
dat.tsne
: 2D coordinates of the cells in a t-SNE
splot
First, let’s apply haystack
(the main function of the
package) on the toy dataset. This should take just several seconds on a
typical desktop computer.
library(singleCellHaystack)
set.seed(1234)
# run the main 'haystack' analysis
# inputs are:
# 1) the coordinates of the cells in the input space (here: dat.tsne)
# 2) the expression data (dat.expression)
res <- haystack(dat.tsne, dat.expression)
#> ### calling haystack_continuous_highD()...
#> ### Package sparseMatrixStats not found. Install for speed improvements.
#> ### Calculating row-wise mean and SD...
#> ### Filtered 0 genes with zero variance...
#> ### Using 100 randomizations...
#> ### Using 100 genes to randomize...
#> Warning in haystack_continuous_highD(x, expression = expression,
#> weights.advanced.Q = weights.advanced.Q, : The value of 'grid.points' appears
#> to be very high (> No. of cells / 10). You can set the number of grid points
#> using the 'grid.points' parameter.
#> ### scaling input data...
#> ### deciding grid points...
#> ### calculating Kullback-Leibler divergences...
#> ### performing randomizations...
#> ### estimating p-values...
#> ### picking model for mean D_KL...
#> ### using natural splines
#> ### best RMSD : 0.09
#> ### best df : 3
#> ### picking model for stdev D_KL...
#> ### using natural splines
#> ### best RMSD : 0.019
#> ### best df : 5
#> ### returning result...
# the returned results 'res' is of class 'haystack'
class(res)
#> [1] "haystack"
Let’s have a look at the most significant differentially expressed genes (DEGs).
# show top 10 DEGs
show_result_haystack(res.haystack = res, n=10)
#> D_KL log.p.vals log.p.adj
#> gene_79 2.447641 -39.95618 -37.25721
#> gene_497 2.271242 -39.67883 -36.97986
#> gene_62 2.174074 -35.65688 -32.95791
#> gene_275 1.819669 -35.31421 -32.61524
#> gene_242 1.742783 -35.29556 -32.59659
#> gene_71 2.546493 -34.79988 -32.10091
#> gene_381 2.733446 -34.36011 -31.66114
#> gene_351 1.844673 -33.08759 -30.38862
#> gene_479 2.343509 -31.95521 -29.25624
#> gene_300 2.097546 -30.39698 -27.69801
# alternatively: use a p-value threshold
#show_result_haystack(res.haystack = res, p.value.threshold = 1e-10)
One of the most significant DEGs is “gene_497”. Here we visualize its expression in the t-SNE plot. As you can see, this DEG is expressed only in cells in the upper-left corner of the plot.
d <- cbind(dat.tsne, t(dat.expression))
d[1:4, 1:4]
#> tSNE1 tSNE2 gene_1 gene_2
#> cell_1 -21.69304 11.599176 0 0
#> cell_2 -20.28140 10.808351 0 0
#> cell_3 -22.69715 8.643215 0 2
#> cell_4 -20.13836 12.485293 0 0
library(ggplot2)
ggplot(d, aes(tSNE1, tSNE2, color=gene_497)) +
geom_point() +
scale_color_distiller(palette="Spectral")
Yes, the coordinates of the cells in this toy example t-SNE space roughly resemble a haystack; see the Haystack paintings by Monet.
You are not limited to single genes. Here, we pick up a set of DEGs, and group them by their expression pattern in the plot into 5 clusters.
# get the top most significant genes, and cluster them by their distribution pattern in the 2D plot
sorted.table <- show_result_haystack(res.haystack = res, p.value.threshold = 1e-10)
gene.subset <- row.names(sorted.table)
# k-means clustering
#km <- kmeans_haystack(dat.tsne, dat.expression[gene.subset, ], grid.coordinates=res$info$grid.coordinates, k=5)
#km.clusters <- km$cluster
# alternatively: hierarchical clustering
hm <- hclust_haystack(dat.tsne, dat.expression[gene.subset, ], grid.coordinates=res$info$grid.coordinates)
#> ### collecting density data...
… and visualize the pattern of the selected genes.
ComplexHeatmap::Heatmap(dat.expression[gene.subset, ], show_column_names=FALSE, cluster_rows=hm, name="expression")
We divide the genes into clusters with cutree.
Then calculate the average expression of the genes in each cluster.
for (cluster in unique(hm.clusters)) {
d[[paste0("cluster_", cluster)]] <- colMeans(dat.expression[names(which(hm.clusters == cluster)), ])
}
lapply(c("cluster_1", "cluster_2", "cluster_3", "cluster_4"), function(cluster) {
ggplot(d, aes(tSNE1, tSNE2, color=.data[[cluster]])) +
geom_point() +
scale_color_distiller(palette="Spectral")
}) |> patchwork::wrap_plots()
From this plot we can see that genes in each cluster are expressed in different subsets of cells.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.