Gene Length-Dependent Expression Analysis Tool in Neuronal Cells
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README.md

LONGO: Gene Length-Dependent Expression Analysis Tool in Neuronal Cells

Documentation is also available on GitHub Pages: https://biohpc.github.io/LONGO/

LONGO is designed to have two different uses. One through a shiny interface and another through R. Both of results of the program are the same. The shiny interface will allow the user to alter certain variables in the analysis to see how they will affect the result. This can be useful when interpreting new data.

LONGO is designed to take in a data file with a gene identifier to get the gene name and length. Occasionally there will be multiple probes going to a single gene. LONGO can handle this in two different ways. The default way is to get the mean expression values. The other option is to only keep the probe that has the highest overall expression values. If a read has no identified gene name or length in BioMart, the read is removed.

After getting the gene names and lengths the data is sorted by length. A rolling window is used to create bins. The user can change the size of the window as well as the step size of the window. These rolling window values are then plotted. The P value plot shows the p value comparisons of the values making up the windows to the window values in the control.

Pre-requisites:

  • biomaRt package

    • install by using the following R commands:

      > source("https://bioconductor.org/biocLite.R")  
      > biocLite("biomaRt")  
      
  • edgeR package

    • install by using the following R commands:

      > source("https://bioconductor.org/biocLite.R")  
      > biocLite("edgeR")  
      
  • preprocessCore package

    • install by using the following R commands:

      > source("https://bioconductor.org/biocLite.R")  
      > biocLite("preprocessCore")  
      
  • topGO package

    • install by using the following R commands:

      > source("https://bioconductor.org/biocLite.R")  
      > biocLite("topGO")  
      
  • Rgraphviz package

    • install by using the following R commands:

      > source("https://bioconductor.org/biocLite.R")  
      > biocLite("Rgraphviz")
      
  • shiny package:

    • install by using the following R command:

      > install.packages("shiny")  
      
  • DT package

    • install by using the following R command:

      > install.packages("DT")  
      
  • data.table package

    • install by using the following R command:

      > install.packages("data.table")  
      
  • hash package:

    • install by using the following R command:

      > install.packages("hash")
      

Installing LONGO Package:

To install the LONGO package all the prerequisites above need to be installed. After confirming those packages are installed start RStudio. From there follow the instructions below:

  • Tools dropdown -> Install Packages…
  • Change the Install from dropdown to Package Archive File
  • Locate the LONGO tarball (LONGO_X.X.X.tar.gz)
  • Click install

Another option is to use the follow R line:

install.packages(“~/LONGO_X.X.X.tar.gz”, repos = NULL, type = “source”)

Pre-processing:

In order to use LONGO the data needs to be in a specific format. This format has the gene identifier in the first column and all of the other columns are expression values. The first row can be a header. The script file in the LONGO-script directory has multiple examples of pre-processing. The example dataset used in the examples below is GSE69480 and can be downloaded from here.

Usage with LONGO():

  • Launch LONGO

    LONGO()

figure 1

  • Load pre-processed data
    • Select options for data file
    • Example data will be a tsv with a file extension of .txt
  • Select species
    • For the example data will be 'hsapiens_gene_ensembl'
  • Select gene identifier
    • Make sure gene identifier is in the first column
    • For the example data this will be 'affy_primeview'
  • Confirm data and options are accurate, then click submit

figure 2

  • Wait until the analysis completes
  • Data Table output tab has the gene name, length for the data

figure 3

  • LONGO Output tab has the LONGO plot and few other statistical plots
    • Can adjust these variables to quickly see how they affect the plots

figure 4

  • Long Gene Quotient tab has the long gene quotient plot

figure 5

  • The raw data for all of these plots are available to be downloaded via download buttons
  • The GO Analysis tab provides options allowing graphing of the GO enrichment analysis

figure 6

Usage with LONGOcmd():

The LONGOcmd function will automatically write the output data files to your working directory. This can allow faster data analysis if you know the values to use. LONGOcmd can also be used with an R dataframe as the input file as long as it satisfies the format described in the pre-processing section above. The shiny interface is more beginner friendly while the LONGOcmd() requires more specific knowledge at the start. The LONGOcmd() function uses the same techniques but requires only the initial input. If you know the BioMart species database and gene identifier, you can use this for faster analysis. The example gives an overview of the possible input variables and their defaults.

  • Use the following R command:

    LONGOcmd(fileLocation = path_to_file, {separator = ","}, {header = TRUE}, {commentChar = "!"}, {species = "hsapiens_gene_ensembl"}, {libraryType = "affy_primeview"}, {multiProbes = "mean"}, {windowSize = 200}, {stepSize = 40}, {windowStyle = "mean"}, {filterData = TRUE}, {normalizeData = TRUE}, {controlColumn = 2})

  • Output files are written to the working directory