In veltenlab/rnamagnet: Infers physical and signaling interactions from single-cell RNA-seq data

This vignette demonstrates the use of RNAMagnet for infering spatial co-localization in the context of data from the bone marrow niche. A second vignette describes the use of RNAMagnet for signaling analyses.

Let's assume that our organ of interest has a number of scaffold structures. In the context of bone marrow, those would be arteriolar and sinusoidal blood vessels, as well as the 'endosteal' surface of the bone. These scaffolds can be defined by anchor cell types, i.e. cell types we know to define our scaffolds. In the context on bone marrow, those would be sinusoidal endothelial cells (ECs), arteriolar ECs, and osteoblasts; Smooth muscle cells, which exclusively line arterioles, are a possible fourth anchor.

The idea behind of RNAMagnet is to identify, for each single cell from a dataset, which of these anchors it is most likely to bind to. Potential physical interactions between cells and anchors are scored based on the mutual expression level of receptors that bind to surface molecules expressed on a second cell (e.g. Selectin P ligand-Selectin P, or homophilic interactions of cadherins), or based on receptors binding to structural extracellular matrix components (e.g. Integrin a1b1-Collagen). We can retrieve the underlying receptor-ligand lists using getLigandsReceptors.

require(RNAMagnet)
require(ggplot2)

ligrec <- getLigandsReceptors("1.0.0",cellularCompartment = c("ECM","Surface","Both"),manualAnnotation = "Correct")
head(ligrec)

The data at hand

Let's familiarize ourselves with the dataset at hand: Our dataset contains 7497 cells from 32 populations and is stored as an object of class seurat.

NicheDataColors <-
c(Erythroblasts = "#bc7c7c", Chondrocytes = "#a6c7f7", Osteoblasts = "#0061ff", 
`Fibro/Chondro p.` = "#70a5f9", `pro-B` = "#7b9696", `Arteriolar ECs` = "#b5a800", 
`B cell` = "#000000", `large pre-B.` = "#495959", `Sinusoidal ECs` = "#ffee00", 
Fibroblasts = "#70a5f9", `Endosteal fibro.` = "#264570", `Arteriolar fibro.` = "#567fba", 
`Stromal fibro.` = "#465f82", `small pre-B.` = "#323d3d", `Adipo-CAR` = "#ffb556", 
`Ng2+ MSCs` = "#ab51ff", Neutrophils = "#1f7700", `T cells` = "#915400", 
`NK cells` = "#846232", `Schwann cells` = "#ff00fa", `Osteo-CAR` = "#ff0000", 
`Dendritic cells` = "#44593c", Myofibroblasts = "#dddddd", Monocytes = "#8fff68", 
`Smooth muscle` = "#ff2068", `Ery prog.` = "#f9a7a7", `Mk prog.` = "#f9e0a7", 
`Ery/Mk prog.` = "#f9cda7", `Gran/Mono prog.` = "#e0f9a7", `Neutro prog.` = "#c6f9a7", 
`Mono prog.` = "#f4f9a7", LMPPs = "#a7f9e9", `Eo/Baso prog.` = "#a7b7f9", 
HSPCs = "#c6f9a7")

qplot(x = NicheData10x@dr$[e-mail][,1], y = NicheData10x@dr$[e-mail][,2], color =NicheData10x@ident) + scale_color_manual(values = NicheDataColors) + theme_bw() + theme(panel.grid = element_blank(), axis.text = element_blank(), axis.title = element_blank())

Running RNAMagnet for physical interactions

Simply run RNAMagnetAnchors:

  result <- RNAMagnetAnchors(NicheData10x, anchors = c("Sinusoidal ECs","Arteriolar ECs","Smooth muscle","Osteoblasts"), .version = "1.0.0")

The result is a dataframe that contains

• The anchor population that a cell is most specifically interacting with ('direction')
• The overall strength of the interaction ('adhesiveness')
• Specificity scores for interaction with each of the anchor populations.

head(result)

The result can easily be highlighted on a t-SNE...

qplot(x =NicheData10x@dr$[e-mail][,1], y=NicheData10x@dr$[e-mail][,2], color = direction,size=I(0.75),alpha= adhesiveness,data=result) + scale_color_brewer(name = "RNAMagnet\nLocation",palette= "Set1") + scale_alpha_continuous(name = "RNAMagnet\nAdhesiveness") + theme_bw() + theme(panel.grid = element_blank(), axis.text = element_blank(), axis.title = element_blank())

...or we can compute population-level summaries, maybe after subsetting for popualtions that have a certain level of adhesiveness:

require(plyr)
require(reshape2)
require(pheatmap)

result$id <- NicheData10x@ident

summarised <- ddply(result, c("id"), summarise,  RNAmagnet.score = table(direction) / length(direction), n = rep(length(direction),4), RNAmagnet.adhesiveness = rep(mean(adhesiveness),4), experiment = names(table(direction)))

castMagnet <- dcast(subset(summarised, RNAmagnet.adhesiveness > 35), id ~ experiment, value.var = "RNAmagnet.score")
rownames(castMagnet) <- castMagnet[,1]
castMagnet <- castMagnet[,-1]
castMagnet <- t(apply(castMagnet,1,function(x) (x-min(x)) / (max(x)-min(x))))

pheatmap(castMagnet, cluster_cols = F, annotation_legend = F, annotation_names_col = F, color = colorRampPalette(c("white","white","blue","red"))(100), fontsize=8, treeheight_row = 20)

As we detail in our manuscript and in the CIBERSORT vignette, these assignments are confirmed by the analysis of LCM-seq data, and micropscopy.

veltenlab/rnamagnet documentation built on June 24, 2021, 6:19 p.m.