| Title: | Inferring Functional Gene Co-Expression Networks from Single Cell Data |
| Version: | 1.0.1 |
| Description: | Uses statistical network modeling to understand the co-expression relationships among genes and to construct sparse gene co-expression networks from single-cell gene expression data. |
| License: | GPL-3 |
| Depends: | R (≥ 3.5.0), parallel, glasso |
| Suggests: | knitr, rmarkdown |
| VignetteBuilder: | knitr |
| Encoding: | UTF-8 |
| LazyData: | true |
| RoxygenNote: | 7.0.2 |
| NeedsCompilation: | no |
| Packaged: | 2020-08-15 00:29:18 UTC; wei |
| Author: | Wei Vivian Li 
[aut, cre] |
| Maintainer: | Wei Vivian Li <vivian.li@rutgers.edu> |
| Repository: | CRAN |
| Date/Publication: | 2020-08-26 14:20:02 UTC |
Calculate scLink's correlation matrix
Description
Calculate scLink's correlation matrix
Usage
sclink_cor(expr, ncores, nthre = 20, dthre = 0.9)
Arguments
expr |
A gene expression matrix with rows representing cells and columns representing genes.
Gene names are given as column names. Can be the output of |
ncores |
Number of cores if using parallel computation. |
nthre |
An integer specifying a threshold on the number of complete observations. Defaults to 20. |
dthre |
A number specifying the threshold on dropout probabilities. Defaults to 0.9. |
Value
A correlation matrix for gene co-expression relationships.
Author(s)
Wei Vivian Li, vivian.li@rutgers.edu
Examples
count = readRDS(system.file("extdata", "example.rds", package = "scLink"))
count.norm = sclink_norm(count, scale.factor = 1e6, filter.genes = TRUE, n = 500)
corr = sclink_cor(expr = count.norm, ncores = 1)
Infer gene co-expression networks
Description
Infer gene co-expression networks
Usage
sclink_net(expr, ncores, lda = seq(1, 0.1, -0.05), nthre = 20, dthre = 0.9)
Arguments
expr |
A gene expression matrix with rows representing cells and columns representing genes.
Gene names are given as column names. Can be the output of |
ncores |
Number of cores if using parallel computation. |
lda |
A vector specifying a sequence of lambda values to be used in the penalized likelihood. |
nthre |
An integer specifying a threshold on the number of complete observations. Defaults to 20. |
dthre |
A number specifying the threshold on dropout probabilities. Defaults to 0.9. |
Value
A list for gene co-expression relationships. The list contains a cor element for
scLink's correlation matrix and a summary element for the gene networks. summary is a list
with each element corresponding to the result of one lambda value. Each element of summary
contains the following information:
- adj:
the adjacency matrix specifying the gene-gene edges;
- Sigma:
the estimated concentration matrix;
- nedge:
number of edges in the gene network;
- bic:
BIC score;
- lambda:
value of lambda in the penalty.
Author(s)
Wei Vivian Li, vivian.li@rutgers.edu
Examples
count = readRDS(system.file("extdata", "example.rds", package = "scLink"))
count.norm = sclink_norm(count, scale.factor = 1e6, filter.genes = TRUE, n = 500)
networks = sclink_net(expr = count.norm, ncores = 1, lda = seq(0.5, 0.1, -0.05))
Pre-process data for scLink
Description
Pre-process data for scLink
Usage
sclink_norm(
count,
scale.factor = 1e+06,
filter.genes = FALSE,
gene.names = NULL,
n = 500
)
Arguments
count |
A full gene count matrix with rows representing cells and columns representing genes. Gene names are given as column names. |
scale.factor |
A number specifying the sclae factor used for library size normalization. Defaults to 1e6. |
filter.genes |
A Boolean specifying whether scLink should select genes based on mean expression.
When set to |
gene.names |
A character vector specifying the genes used for network construction.
Only needed when |
n |
An integer specifying the number of genes to be selected by scLink (defaults to 500).
Only needed when |
Value
A transformed and normalized gene expression matrix that can be used for correlation calculation and network construction.
Author(s)
Wei Vivian Li, vivian.li@rutgers.edu
Examples
count = readRDS(system.file("extdata", "example.rds", package = "scLink"))
count.norm = sclink_norm(count, scale.factor = 1e6, filter.genes = TRUE, n = 500)