---
title: "The Expression Hunter Suite"
author: "James R. Perkins"
date: "`r format(Sys.Date(), '%m/%d/%Y')`"
output:
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highlight: pygments
toc: true
fig_width: 5
vignette: >
%\VignetteIndexEntry{The Expression Hunter Suite}
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---
## Version Info
**R version**: `r R.version.string`
**Bioconductor version**: `r BiocManager::version()`
**Package version**: `r packageVersion("ExpHunterSuite")`
# Introduction
ExpHunterSuite implements a comprehensive protocol for the analysis of
transcriptional data using established *R* packages and combining their
results. It covers all key steps in DEG detection, CEG detection and functional
analysis for RNA-seq data. It has been implemented as an R package
containing functions that can be run interactively. In addition, it also
contains scripts that wrap the functions and can be run directly from the
command line.
# Standard Package Usage
In this section we will describe how the functions in ExpHunterSuite can be used
interactively or joined together in user-written scripts. We will also describe
how the output reports can be generated from this data.
## Differential Expression Analysis
The most basic use of the package is to perform differential expression (DE)
gene analysis. ExpHunterSuite will, following some initial preprocessing, run
the different methods, combine the results, and produce an output report, as
well as a single output table containing the results of all of the methods used,
and their combined scores. The combined scores consist of the mean logFC value
and the combined adjusted p-value (FDR) values, calculated by Fishers method.
To use ExpHunterSuite with only a single DE package, one can run the following
command:
```{r DEA_one_pack, echo=TRUE, results="hide", message=FALSE, warning=FALSE}
library(ExpHunterSuite)
data(toc)
data(target)
degh_out_one_pack <- main_degenes_Hunter(raw=toc,
target=target,
modules="D") # D for DESeq2
```
Where toc is a data.frame of aligned reads per samples and target is a
data.frame relating each sample to its sample meta data. Here we include a
minimal example of the target file whcih includes the samples (CTL/epm2a),
the samples condition (Ctrl or Treat):
```{r input_data, echo=TRUE, results="as.is", message=FALSE, warning=FALSE}
head(toc)
head(target)
```
The files containing this data are contained within the extData package
directory and can be accessed in the following manner. We will come back to
them in the section on command line usage.
```{r input_files, echo=TRUE, results="as.is"}
system.file("extData", "table_of_counts.txt", package = "ExpHunterSuite")
system.file("extData", "target.txt", package = "ExpHunterSuite")
```
To use it with multiple packages, one can run the following command:
```{r DEA_multi_pack, echo=TRUE, results="hide", message=FALSE, warning=FALSE}
degh_out_multi_pack <- main_degenes_Hunter(raw=toc,
target=target,
modules="DEL") # D:DESeq2 E:EdgeR, L:limma
```
The output is a list, which includes, in slot *DE_all_genes* a data.frame
containing, for each gene,
logFC/p-values/adjusted p-values for the different DE methods implemented:
```{r standard_DEA_results, echo=TRUE}
head(degh_out_multi_pack$DE_all_genes)
```
It also contains information on whether the genes are considered to be DE, in
the column *genes_tag* The tag *PREVALENT_DEGS* refers to those genes that are
considered significant in at least n of the DE methods used
*POSSIBLE_DEGS* are those considered significant by at least one method. As
such, *PREVALENT_DEGS* and *POSSIBLE_DEGS* will be the same
when n = 1. N is controlled by the argument *minlibraries*.
To be considered significant for a given method, a gene must have an
adjusted-pvalue < 0.05 and |logFC| > 1; these values are adjustable
using the arguments *p_val_cutoff* and *lfc*.
The *genes_tag* columns includes the labels *NOT_DEGS* and *FILTERED_OUT* to
refer to those genes not detected as DE by at least one DE method
and those that do not pass the initial low-count filtering step, controlled by
parameters *reads* and *minlibraries*.
There is another column, *combined_FDR* – which is POS/NEG depending on whether
the combined adjusted p-value as described above is less
than or equal to 0.05 (or whatever the value of the argument *p_val_cutoff* is).
### More complex model designs.
In order to control for specific variables (such as individuals in paired
designs, potential confounding factors such as age, etc.),
For example, if we consider our previous experiment, but add an extra column to
the target, indicating different age groupings for the
samples we obtain the following:
```{r standard_DEA_model_target, echo=TRUE, results="as.is"}
target_multi <- data.frame(target,
age_group = c("adult", "child", "adult", "child", "adult", "adult", "child"))
target_multi
```
We may wish to control for the effects of age_group on the experiment.
This can be achieved using the argument *model_variables*. The variables given
to this argument will be used in the model when calculating
differential expression between the Treat and Ctrl samples:
```{r standard_DEA_model_execute, results="hide", message=FALSE, warning=FALSE}
degh_out_model <- main_degenes_Hunter(raw=toc, target=target_multi,
modules="D", model_variables="age_group")
```
This works by using the variable age_group to create a linear model formula to
be passed to the different DE methods (with the exception of NOISeq).
The output has the same structure as the original analysis.
Custom model designs can also be specified in the *model_variables* argument,
based on the R model syntax, see *help("formula")* for more details. If a
custom formula is used, the *custom_model* argument must be set to true.
## Co-expression Analysis
Co-expression analysis is included via the R package Weighted correlation
network analysis (WGCNA). The idea is to look for groups (modules) of genes
showing correlated expression.
The groups can then be correlated with experimental factors, such as treatment
vs. non treatment, as well as other groupings such as the age grouping mentioned
earlier, or numeric factors such as known values of metabolites related to the
experiment.
WGCNA is activated using by adding "W" to the *modules* argument. The traits to
be correlated with the modules are specified using the *string_factors* and
*numeric_factors* options:
```{r standard_CEA, results="hide", message=FALSE, warning=FALSE}
degh_out_coexp <- main_degenes_Hunter(raw=toc, target=target_multi,
modules="DW", string_factors="age_group")
```
Please note that WGCNA requires a normalized expression matrix as input, as such
it cannot be run alone, it must be run alongside at least one DE method, which
is specified with the argument *WGCNA_norm_method*.
## Functional Analysis
```{r standard_FA, results="as.is", eval=FALSE}
fh_out_one_pack <- functional_hunter( #Perform enrichment analysis
degh_out_one_pack,
'Mouse', # Use specified organism database
func_annot_db = "gKR", # Enrichment analysis for GO, KEGG and Reactome
GO_subont = "BMC",
analysis_type= "o" # Use overepresentation analysis only (Not GSEA)
)
fh_out_coexp <- functional_hunter( # Perform enrichment analisys
degh_out_coexp,
'Mouse', # Use specified organism database
func_annot_db = "gKR", # Enrichment analysis for GO, KEGG and Reactome
GO_subont = "BMC",
analysis_type= "o" # Use overepresentation analysi only (Not GSEA)
)
```
## Obtaining Reports
To obtain highly detailed html reports including multiple plots to visualize
the data and the results of the different analysis methods,
the following commands can be used:
```{r write_reports, echo=TRUE, eval=FALSE}
print(getwd())
write_expression_report(exp_results=degh_out_coexp)
write_enrich_files(func_results=fh_out_one_pack)
write_functional_report(hunter_results=degh_out_coexp,
func_results=fh_out_coexp)
```
In all cases, the output folder for each report can be specified with the
*output_files* option.
# Command-line Package Usage
The package also includes a number of scripts, in the folder *inst/scripts*,
which can be used to run the above functions from the command line.
```{r input_files_cl, echo=TRUE, results="hide"}
input_toc <- system.file("extdata", "table_of_counts.txt",
package = "mypackage")
input_toc
input_target <- system.file("extdata", "target.txt", package = "mypackage")
input_target
```
We recommend the user first creates a folder in which to install the
ExpHunterSuite command line scripts, then copies the scripts there and make
them command line accesible using these commands:
```bash
mkdir install_folder
Rscript -e "ExpHunterSuite::install_DEgenes_hunter('install_folder')"
export PATH=path_to_install_folder:$PATH
```
This export PATH can also be added to the .bashrc or .bash_profile files.
The user can then run the protocol from the command line with scripts such as
the following, which will implement the functions and create the
output reports, all from a single script.
```bash
degenes_Hunter.R -t $TARGET_FILE -i $TOC -o $EXP_RESULTS
functional_Hunter.R -i $EXP_RESULTS -m Organism -o FUNC_RESULTS
```
Full details of the arguments to give the the script can be found by running
*degenes_Hunter.R -h* or *functional_Hunter.R -h*. More examples are given in
the README file for this packet