https://github.com/bioconductor-source/timeseriesanalysis

https://github.com/bioconductor-source/timeseriesanalysis

Repository

README.Rmd

---
output: github_document
---



```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```

# TimeSeriesAnalysis




TiSA: TimeSeriesAnalysis - Is a transcriptomic analysis tool for both RNA sequencing and microarray data

## Overview

TimeSeriesAnalysis (TiSA) is an analysis and visualization package for RNAseq and
microarray data. TS extracts significant genes from time course
transcriptomic data by performing differential gene expression on both
the conditional and temporal axes. It then employs partitioning
algorithm based on recursive thresholding (PART) clustering to identify
small genomic clusters of relevance, followed by running the clusters
through gprofiler to reveal the biological relevance of each cluster.

## TS performs: 

* Data normalization and processing 
* PCA plots 
* Differential gene expression (conditional and temporal) 
* PART clustering 
* Heatmaps for both differential expression summary and clustering results
* Trajectory of identified clusters 
* Gprofiler (functional enrichment) analysis of clusters 
* Dotplots and MDS plots of Gprofiler results 
* Nearest ancestor clustering of GOs 
* GO ancestor queries


## Installation

**A Bioconductor release is in progress**

You can install the development version of TimeSeriesAnalysis like so:

``` r
install.packages("devtools")
devtools::install_github("Ylefol/TimeSeriesAnalysis")
```
To instal Devtools, visit the 
[Devtools main page](https://www.r-project.org/nosvn/pandoc/devtools.html)

Certain bioconductor packages will have to be installed before
installation of TimeSeriesAnalysis. The code snippet below gives the method along with
some of the packages that should be installed via this method.

``` r
if (!require("BiocManager", quietly = TRUE))
  install.packages("BiocManager")
bio_pkgs <- c('DESeq2','GOSemSim','GO.db','limma','ComplexHeatmap',
'SummarizedExperiment','org.Hs.eg.db','org.Mm.eg.db','org.Ce.eg.db',
BiocFileCache)
BiocManager::install(bio_pkgs)
```

## Rmarkdown format

TimeSeriesAnalysis was developed with user-friendliness in mind, the
core script of the package is a Rmarkdown file with explanations of what
each code block performs. Computationally intensive code blocks save
their work in order to avoid loss of computation time.
TimeSeriesAnalysis comes with a test dataset. Users are recommended to
first run the package with the test data to then modify the Rmarkdown
script with the necessary information for their purposes (input files,
organism, genes of interest etc…). The rmarkdown script and the expected
result from an example run can be found and downloaded from this
repository, they are located in the ‘rmarkdown_method’ folder. If users
prefer a script approach, the equivalent of the rmarkdown report is
provided in two scripts, one for the computation tasks and a second for
the analyses. These two scripts are found in the ‘script_method’ folder.

Both the rmarkdown and script methods are found in the ‘supp_to_bioconductor’ branch.

## Data

Three different datasets have been used to test this pipeline, one of which has been
preserved as an example datasets for the vignettes of this pipeline. More information
on the other datasets can be viewed through the publications listed below.

The PBMC dataset is a time series experiment with three time points that explores 
and compares three AICDA/AID (Activation Induced Cytidine DeAminase) activation cocktails.
The experiment seeked to identify which activation cocktail properly activated AID
through both the expression of the AID gene and the activation of class switch recombination.











## Tutorial

A tutorial can be found within the [pipelines website](https://ylefol.github.io/TimeSeriesAnalysis/). 
Additionally the documentation can be found in the references tab.

## Microarray based data

For microarray data, a streamlined method is in the works. Currently microarray data needs to be inputed 
as a Elist, specifically an E list saved as a rds object.
``` r
my_path_data<-'data/micro_arr/my_limma_dta.rds'
my_path_sample_dta<-'data/micro_arr/sample_file.csv'

#Set-up time series object parameters
diff_exp_type<-'limma' 
```
The rds file given to 'my_path_data' contains the Elist produced by limma processing
of microarray data. It is also important to set the differential gene expression type (diff_exp_type)
to 'limma'. 

## Using the pipeline

NOTE: You must still install the TimeSeriesAnalysis package as defined above

Since the pipeline's source code is available, individuals are free to take the code
and adapt it as needed. However for users who would like the easiest approach to use the
pipeline as is, it is recommended to clone or download this github repository to your local computer
and work from the Rmarkdown method folder within the repository.
Cloning or downloading can be done from the [TimeSeriesAnalysis github page](https://github.com/Ylefol/TimeSeriesAnalysis)
by using the green button called 'code'.
From there, the Rmarkdown file can be edited.

To edit the file, it is recommended to use rstudio, which can be downloaded [here](https://www.rstudio.com/products/rstudio/download/).
Note that it is recommended to download the free version of RStudio Desktop, not RStudio Server.

The only code block of code is the parameter set-up block (2nd) as well as the title
at the very top of the Rmarkdown document.

## Launching the pipeline

The pipeline can be launched from rstudio itself by using the 'knit' button, or it can be launched from a command line using the
following command from the TimeSeriesAnalysis repository:
```{r, engine = 'bash', eval = FALSE}
Rscript -e "rmarkdown::render('rmarkdown_method/TS_analysis.Rmd',output_file='TS_analysis.html')"
```

## Publication

A manuscript detailing the TimeSeriesAnalysis pipeline published with NAR genomics and bioinformatics.
[TiSA: TimeSeriesAnalysis—a pipeline for the analysis of longitudinal transcriptomics data](https://academic.oup.com/nargab/article/5/1/lqad020/7069286)
The version/DOI of this pipeline used within the manuscript is 10.5281/zenodo.7616032.

A ageing related study was performed in part through the use of TiSA.
[Krill oil protects dopaminergic neurons from age-related degeneration through temporal transcriptome rewiring and suppression of several hallmarks of aging](https://www.aging-us.com/article/204375/text)


## Additional information
The 'clusterGenomics' package was no longer maintained on CRAN and therefore the necessary
scripts were brought over to this package for it's implementation without the need
to download the clusterGenomics package from source.
Within this package, in the 'R/clusterGenomics.R' code was written by the authors of 
clusterGenomics. It was imported to TimeSeriesAnalysis by the authors of
TimeSeriesAnalysis.

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