The CEMiTool
R
package provides users with
an easy-to-use method to automatically run gene co-expression analyses.
In addition, it performs gene set enrichment analysis and over
representation analysis for the gene modules returned by the
analysis.
For the most basic usage of CEMiTool
only a
data.frame
containing expression data with gene symbols in
the rows and sample names in the columns is needed, as following:
BiocManager::install("CEMiTool")
library("CEMiTool")
# load expression data
data(expr0)
head(expr0[,1:4])
## X1913_d0 X1913_d3 X1913_d7 X1911_d0
## XIST 13.061894 13.290272 13.360468 13.178729
## DDX3Y 3.410819 3.164874 3.599792 3.400613
## RPS4Y1 6.326861 5.915121 6.341564 5.905167
## USP9Y 3.237749 3.362508 3.320674 3.365530
## CYorf15B 3.980988 4.201731 4.235020 4.046716
## EIF1AY 3.379857 3.229973 3.150274 3.196610
In this usage, the cemitool
function receives the
expression data, performs the co-expression modules analysis and returns
a CEMiTool
object:
cem <- cemitool(expr0)
To see a summary of the slots inside the CEMiTool
, just
call cem
cem
## CEMiTool Object
## - Number of modules: 4
## - Modules: (data.frame: 257x2):
## genes modules
## 1 HBA1 Not.Correlated
## 2 RPS26 Not.Correlated
## 3 LYZ Not.Correlated
## - Expression file: data.frame with 4000 genes and 45 samples
## - Selected data: 257 genes selected
## - Gene Set Enrichment Analysis: null
## - Over Representation Analysis: null
## - Profile plot: ok
## - Enrichment plot: null
## - ORA barplot: null
## - Beta x R2 plot: null
## - Mean connectivity plot: null
The cemitool()
function automatically executes some
common analyses, depending on the input data. The following sections
describes how to perform each of these analyses separately. Details on
how to perform all analyses together are at the end of this
vignette.
As a default, the cemitool
function first performs a
filtering of the gene expression data before running the remaining
analyses. This filtering is done in accordance to gene variance. In this
example the filtering step has reduced the gene number to 257.
The module analysis has produced 4 modules and the allocation of
genes to modules can be seen with the module_genes
function:
# inspect modules
nmodules(cem)
## [1] 4
head(module_genes(cem))
## genes modules
## 1 HBA1 Not.Correlated
## 2 RPS26 Not.Correlated
## 3 LYZ Not.Correlated
## 4 PPBP M3
## 5 abParts35 Not.Correlated
## 6 NAMPT M2
Genes that are allocated to Not.Correlated
are genes
that are not clustered into any module.
If you wish to adjust the module definition parameters of your
CEMiTool
object, use find_modules(cem)
.
You can use the get_hubs
function to identify the top
n
genes with the highest connectivity in each module:
hubs <- get_hubs(cem,n)
. A summary statistic of the
expression data within each module (either the module mean or eigengene)
can be obtained using: summary <- mod_summary(cem)
The information generated by CEMiTool, including tables and images
can be accessed by generating a report of the CEMiTool
object:
generate_report(cem)
Also, you can create tables with the analyses results using:
write_files(cem)
Plots containing analysis results can be saved using:
save_plots(cem, "all")
## $M1
##
## $M2
##
## $M3
##
## $Not.Correlated
##
## $beta_r2_plot
##
## $mean_k_plot
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More information can be included in CEMiTool to build a more complete
object and generate richer reports about the expression data. Sample
annotation can be supplied in a data.frame
that specifies a
class for each sample. Classes can represent different conditions,
phenotypes, cell lines, time points, etc. In this example, classes are
defined as the time point at which the samples were collected.
# load your sample annotation data
data(sample_annot)
head(sample_annot)
## SampleName Class
## 1 X1913_d0 g0
## 2 X1911_d0 g0
## 3 X1908_d0 g0
## 4 X1909_d0 g0
## 5 X1910_d0 g0
## 6 X1912_d0 g0
Now you can construct a CEMiTool
object with both
expression data and sample annotation:
# run cemitool with sample annotation
cem <- cemitool(expr0, sample_annot)
The sample annotation of your CEMiTool object can be retrieved and
reassigned using the sample_annotation(cem)
function. This
function can also be used to define the columns with sample names and
sample groupings (which are “SampleName” and “Class”, by default):
sample_annotation(cem,
sample_name_column="SampleName",
class_column="Class") <- sample_annot
When sample annotation is provided, the cemitool
function will automatically evaluate how the modules are up or down
regulated between classes. This is performed using the gene set
enrichment analysis function from the fgsea
package.
You can generate a plot of how the enrichment of the modules varies
across classes with the plot_gsea
function. The size and
intensity of the circles in the figure correspond to the Normalised
Enrichment Score (NES), which is the enrichment score for a module in
each class normalised by the number of genes in the module. This
analysis is automatically run by the cemitool
function, but
it can be independently run with the function
mod_gsea(cem)
.
# generate heatmap of gene set enrichment analysis
cem <- mod_gsea(cem)
cem <- plot_gsea(cem)
show_plot(cem, "gsea")
## $enrichment_plot
You can generate a plot that displays the expression of each gene
within a module using the plot_profile
function:
# plot gene expression within each module
cem <- plot_profile(cem)
plots <- show_plot(cem, "profile")
plots[1]
## $M1