cell_signaling: Cell Signaling

In SCA-IRCM/SingleCellSignalR: Cell Signalling Using Single Cell RNAseq Data Analysis

Cell Signaling

Description

Computes "autocrine" or "paracrine" interactions between cell clusters.

Usage

cell_signaling(
  data,
  genes,
  cluster,
  int.type = c("paracrine", "autocrine"),
  c.names = NULL,
  s.score = 0.5,
  logFC = log2(1.5),
  species = c("homo sapiens", "mus musculus"),
  tol = 0,
  write = TRUE,
  verbose = TRUE
)

Arguments

data	a data frame of n rows (genes) and m columns (cells) of read or UMI counts (note : rownames(data)=genes)
genes	a character vector of HUGO official gene symbols of length n
cluster	a numeric vector of length m
int.type	"autocrine" or "paracrine"
c.names	(optional) cluster names
s.score	LRscore threshold
logFC	a number, the log fold-change threshold for differentially expressed genes
species	"homo sapiens" or "mus musculus"
tol	a tolerance parameter for balancing "autocrine|paracrine" interactions to the "autocrine" or "paracrine" group
write	a logical
verbose	a logical

Details

'int.type' must be equal to "paracrine" or "autocrine" exclusively. The "paracrine" option looks for ligands expressed in cluster A and their associated receptors according to LR*db* that are expressed in any other cluster but A. These interactions are labelled "paracrine". The interactions that involve a ligand and a receptor, both differentially expressed in their respective cell clusters according to the **edgeR** analysis performed by the **cluster_analysis()** function, are labelled "specific". The "autocrine" option searches for ligands expressed in cell cluster A and their associated receptors also expressed in A. These interactions are labelled "autocrine". Additionally, it searches for those associated receptors in the other cell clusters (not A) to cover the part of the signaling that is "autocrine" and "paracrine" simultaneously. These interactions are labelled "autocrine/paracrine".

The 'tol' argument allows the user to tolerate a fraction of the cells in cluster A to express the receptors in case 'int.type="paracrine"', that is to call interactions that are dominantly paracrine though not exclusively. Conversely, it allows the user to reject interactions involving receptors that would be expressed by a small fraction of cluster A cells in case 'int.type="autocrine"'. By construction theassociation of these two options covers all the possible interactions and increasing the 'tol' argument allows the user to move interactions from "autocrine" to "paracrine".

If the user does not set 'c.names', the clusters will be named from 1 to the maximum number of clusters (cluster 1, cluster 2, ...). The user can exploit the 'c.names' vector in the list returned by the **cell_classifier()** function for this purpose. The user can also provide her own cluster names.

's.score' is the threshold on the LRscore. The value must lie in the [0;1] interval, default is 0.5 to ensure confident ligand-receptor pair identifications (see our publication). Lower values increase the number of putative interactions while increasing the false positives. Higher values do the opposite.

'logFC' is a threshold applied to the log fold-change (logFC) computed for each gene during the differential gene expression analysis. Its default value is log~2~(1.5) It further selects the differentially expressed genes (>logFC) after the p-value threshold imposed in the function **cluster_analysis()** below.

'species' must be equal to "homo sapiens" or "mus musculus", default is "homo sapiens". In the case of mouse data, the function converts mouse genes in human orthologs (according to Ensembl) such that LR*db* can be exploited, and finally output genes are converted back to mouse.

If 'write' is TRUE, then the function writes a text file that reports the interactions in the *cell-signaling* folder. This file is a 4-column table: ligands, receptors, interaction types ("paracrine", "autocrine", "autocrine/paracrine" and "specific"), and the associated LRscore.

Remarks:

- This function can be used with any 'data' table associated with corresponding 'genes' and 'cluster' vectors, meaning that advanced users can perform their own data normalization and cell clustering upfront.

- In case the function **cluster_analysis()** was not executed, this function would work but "specific" interactions would not be annotated as such.

Value

The function returns "paracrine" or "autocrine" interaction lists. The interactions that are both "paracrine" and "autocrine" are annotated as "autocrine|paracrine" and are placed in the "autocrine" group by default.

Examples

data=matrix(runif(1000,0,1),nrow=5,ncol=200)
rownames(data) <- c("A2M","LRP1","AANAT","MTNR1A","ACE")
cluster=c(rep(1,100),rep(2,100))
cell_signaling(data,rownames(data),cluster,int.type="paracrine",write=FALSE)

SCA-IRCM/SingleCellSignalR documentation built on Dec. 11, 2022, 2:30 p.m.