get_de_genes: Find differentially expressed genes
In brickyyyy/SC3min: Minimized Single-Cell Consensus Clustering
Description
Usage
Arguments
Value
Examples
Description
Differential expression is calculated using the non-parametric Kruskal-Wallis test.
A significant p-value indicates that gene expression in at least one cluster
stochastically dominates one other cluster. Note that the calculation of
differential expression after clustering can introduce a bias in the
distribution of p-values, and thus we advise to use the p-values
for ranking the genes only.
Usage
1
get_de_genes(dataset, labels)
Arguments
dataset
expression matrix
labels
cell labels corresponding to the columns of the expression matrix
Value
a numeric vector containing the differentially expressed genes and
correspoding p-values
Examples
1
2
d <- get_de_genes(yan[1:10,], as.numeric(ann[,1]))
head(d)
brickyyyy/SC3min documentation built on May 8, 2019, 8:07 a.m.
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Description Usage Arguments Value Examples
Description
Differential expression is calculated using the non-parametric Kruskal-Wallis test.
A significant p-value indicates that gene expression in at least one cluster
stochastically dominates one other cluster. Note that the calculation of
differential expression after clustering can introduce a bias in the
distribution of p-values, and thus we advise to use the p-values
for ranking the genes only.
Usage
1 | get_de_genes(dataset, labels)
|
Arguments
dataset |
expression matrix |
labels |
cell labels corresponding to the columns of the expression matrix |
Value
a numeric vector containing the differentially expressed genes and correspoding p-values
Examples
1 2 | d <- get_de_genes(yan[1:10,], as.numeric(ann[,1]))
head(d)
|
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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