In msraredon/Connectome: Single Cell Connectomics

knitr::opts_chunk$set(
  collapse = TRUE,
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This vignette details how to use the Centrality function of Connectome.

Load dependencies

library(Seurat)
library(SeuratData)
library(Connectome)
library(ggplot2)
library(cowplot)

Load Data

Again, we will use the cross-platform Pancreas data distributed by SeuratData:

InstallData('panc8')
data('panc8')
table(Idents(panc8))
Idents(panc8) <- panc8[['celltype']]
table(Idents(panc8))

Normalize, Scale and make connectome

``` {r message=F, warning=F, results = 'hide'} panc8 <- NormalizeData(panc8) connectome.genes <- union(Connectome::ncomms8866_human$Ligand.ApprovedSymbol,Connectome::ncomms8866_human$Receptor.ApprovedSymbol) genes <- connectome.genes[connectome.genes %in% rownames(panc8)] panc8 <- ScaleData(panc8,features = genes) panc8.con <- CreateConnectome(panc8,species = 'human',min.cells.per.ident = 75,p.values = F,calculate.DOR = F)

## Full system overviews
`Centrality` performs a series of analyses within a single integrated function. First, it takes the entire input network and filters the data based on parameters passed to `FilterConnectome`. Then, it subdivides the remaining network based on the `group.by` argument. Within each sub-network, it then performs a graph-theory-based centrality analysis, ranking each producer and receiver within the sub-network based on their cumulative outgoing and incoming edgeweight and their Kleinberg centrality scores. By default, the resulting plots are normalized over total network edgeweight so that the x-axis is fraction contribution or reception of signal within the sub-network.

We can use `Centrality` to immediately look at an unfiltered edgelist.  Here, we use edgeweights from the normalized data slot, which are always positive:
``` {r  message=F, warning=F, fig.width = 20,fig.height = 10}
Centrality(panc8.con,
           modes.include = NULL,
           min.z = NULL,
           weight.attribute = 'weight_norm',
           group.by = 'mode')

However, it is generally more informative to limit the analysis to those edges which meet a user-defined statistical threshold: ``` {r message=F, warning=F,fig.width = 20,fig.height=10} Centrality(panc8.con, modes.include = NULL, weight.attribute = 'weight_norm', min.z = 0.25, min.pct = 0.1)

## Focusing on specific signaling families
`Centrality` has built-in functionality to focus on specific signaling families of interest, which can help to gain a sense of which cells may be dominantly communicating via which pathways:
``` {r  message=F, warning=F,fig.align = "center",fig.width = 10,fig.height = 6}
Centrality(panc8.con,
           modes.include = c('VEGF','NOTCH','TGFB','WNT','ANGPT','EGF'),
           weight.attribute = 'weight_sc',
           min.z = 0.25,
           min.pct = 0.1)

Breaking down a signaling family

It is important to note that a single signaling family is generally made up of multiple mechanisms, and that a ligand can hit multiple receptors and vice-versa. Therefore, it can be useful to perform centrality analysis for every single mechanism (ligand-receptor pair) present in a sub-network. Here, we do this for every mechanism in the 'NOTCH' family: ``` {r message=F, warning=F,fig.align = "center",fig.width = 10,fig.height = 8} Centrality(panc8.con, modes.include = c('NOTCH'), weight.attribute = 'weight_norm', min.z = NULL, min.pct = 0.1, group.by = 'mechanism')

If only specific mechanisms are of interest, `Centrality` can be used to directly inquire about networks based on select genes:
``` {r  message=F, warning=F,fig.align = "center",fig.width = 10,fig.height = 5}
temp <- subset(panc8.con,pair %in% c('TGFB1 - TGFBR1','DLL4 - NOTCH3'))
Centrality(temp,
           weight.attribute = 'weight_norm',
           min.pct = 0.1,
           group.by = 'mechanism')

Which suggests that the TGFB1-TGFBR1 network is associated with activated_stellate -> endothelial communication, while the DLL4 - NOTCH3 network is associated with endothelial -> quiescent_stellate commmunication.

msraredon/Connectome documentation built on April 11, 2022, 9:16 a.m.