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beavr's Introduction

BEAVR

A Browser-based tool for the Exploration And Visualization of RNAseq data

BEAVR is a graphical tool to automate analysis and exploration of small and large RNAseq datasets using DESeq2.

Publication: Perampalam, P., Dick, F.A. BEAVR: a browser-based tool for the exploration and visualization of RNA-seq data. BMC Bioinformatics 21, 221 (2020). https://doi.org/10.1186/s12859-020-03549-8

The easiest way to get started with BEAVR is to use our Docker container!

Enter: docker run -ti -p 3838:3838 pirunthan/beavr:latest

Then open your browser and enter localhost:3838 or 127.0.0.1:3838 in the address bar to start using BEAVR.

Check the Docker section for more detailed instructions for using Docker on Windows, Mac OS, and Linux.

Check the Troubleshooting section for some common issues and solutions.

Please note: results from the latest version of BEAVR (1.0.10) may differ slightly from those of the initial publication version (version 1.0.8). This is not due to changes in BEAVR itself, but a result of updates to dependencies required by BEAVR, such as the DESeq2 package or packages used to generate plots.

Another note before you continute: BEAVR requires raw read counts, such as those generated by the STAR aligner. Why isn't this step included in BEAVR? Well, that was part of the initial goal and an early version of BEAVR did include this - but it meant end users required a very powerful computer BEAVR because the alignment process is very computatioanlly demanding. Ultimately it was decided to remove the alignment step from BEAVR.

By removing the alignment step, BEAVR becomes a very portable tool that can run on the average computer.

If you are not comfortable using STAR or Linux shell commands, I have an easy to follow tutorial which you can follow to quickly create a Google cloud virtual machine to run STAR aligner.

But keep an eye out - a standalone, graphical frontend for alignment is on the way soon!

Table of contents

Installation & Requirements

We provide three ways to install and use BEAVR. They vary in ease and speed to get BEAVR running on your computer: 1. Use a Docker container - the easiest and fastest method! 2. Setup a new R environment with the automated installer - for those who don't want to install Docker 3. Run in your existing R installation - for those who already have R installed

Use the Docker container

The easiet and quickest way to install and use BEAVR - especially for those who have no R, programming, or command line experence - is to use our Docker container.

For those who are familiar with Docker, you can pull the latest version of BEAVR using docker pull pirunthan/beavr:latest and you can run the container using docker run -ti -p 3838:3838 pirunthan/beavr:latest.

For those who are new to Docker, follow these instructions to get started:

Docker on Windows

  1. Install Docker from the Docker website using their setup wizard:

  2. Download and extract the BEAVR-Docker setup package for Windows:

  3. Run (pull) our Docker container:

    • Double-click Run-BEAVR.bat in the BEAVR-Docker setup package you downloaded above (you may need to right-click and run as administrator)

      Or

    You can also just enter this command at in a command prompt window or in Windows Power Shell:

    docker run -ti -p 3838:3838 pirunthan/beavr:latest

  4. Open your browser and enter localhost:3838 in the address bar to use BEAVR.

Once the Docker container is downloaded to your computer, you can also access it from the Docker Dashboard as shown below (to get to the Dashboard, right-click the Docker whale icon in the system tray in the bottom right and then click Dashboard):

Image of Docker Dashboard

You can use this interface to start, stop, or open a browser to localhost:3838 (circled in red) without using Run-BEAVR.bat or Run-BEAVR.sh executable scripts as explained in step 3 above.

However, keep in mind that this only runs your current locally downloaded version of BEAVR. To get the most up-to-date version, follow step 3 to update your local copy.

Docker on Mac OS

  1. Install Docker from the Docker website using their setup wizard:

  2. To download and run the BEAVR container you can either copy and paste the following command in Terminal:

    docker run -ti -p 3838:3838 pirunthan/beavr:latest

    Or

Do the following steps instead to use our automated script: (You only have to download this once and do steps 1-3 once)

  1. Download and extract the BEAVR-Docker setup package for Mac OS:

  2. Before you can execute the setup script, you will likely have to allow execute permissions or Mac OS will give you an error. You can do this as follows by copying and pasting this command into Terminal:

    chmod +x ~/Downloads/BEAVR-Docker-Mac/Run-BEAVR.command

    Where ~/Downloads/ is the path where you extracted the BEAVR-Docker-Mac folder from step 3 (usually your Downloads folder or ~/Desktop/ if you moved it to your desktop.)

  3. If you double-click Run-BEAVR.command, you will likely get another error because you didn't download it from the App store.

Image of chmod in MacOS-2

  1. To get around this, right-click the file and go to Open and then click Open in the popup box:

Image of chmod in MacOS-2

This will start BEAVR in the Docker container.

  1. Open your browser and enter localhost:3838 in the address bar to use BEAVR.

Once the Docker container is downloaded to your computer, you can also access it from the Docker Dashboard as shown below (to get to the Dashboard, click the Docker whale icon in system tray in the top right of your screen and then click Dashboard):

Image of Docker Dashboard

You can use this interface to start, stop, or open a browser to localhost:3838 (circled in red) without using Run-BEAVR.bat or Run-BEAVR.sh executable scripts as explained in step 3 above.

However, keep in mind that this only runs your current locally downloaded version of BEAVR. To get the most up-to-date version, follow step 2 or step 6 to update your local copy.

Docker on Linux (Ubuntu)

  1. Download and extract the BEAVR Docker setup package for Linux:

  2. Install Docker for your operating system: Use our automated installer in the BEAVR-Docker-Linux package you extracted above (named Docker-setup-ubuntu.sh). You will need to open Terminal and enter this command to execute it:

    bash Docker-setup-ubuntu.sh

    Note, if you get permission errors, you may need to give the script executable permission

    chmod +x Docker-setup-ubuntu.sh

  3. Run (pull) our Docker container: Enter bash Run-BEAVR.sh in Terminal.

    Note, if you get permission errors, you may need to give the script executable permission

    chmod +x Run-BEAVR.sh

    Or

    You can also just enter this command:

    docker run -ti -p 3838:3838 pirunthan/beavr:latest

  4. Open your browser and enter localhost:3838 in the address bar to use BEAVR.

Setup a new R environment with the automated installer

If you prefer not to install Docker and you do not already have R installed on your computer, you can follow these steps to easily install and configure R for BEAVR.

A new R environment in Windows

  1. Download and install R for Windows using the installation wizard:

    • Download the latest version of R here

  2. Download and install RTools for Windows using the installation wizard:

    • Download the latest version of RTools here

  3. Download and extract the BEAVR setup files from here

  4. Look in the "Setup" folder to find the automated installer for Windows:

    • Right click on the file named Configure-BEAVR-Windows.bat and click run as administrator (agree to any warnings/prompts to run it)

    This will open a command prompt window.

  5. You will be prompted to "enter the path to your Rscript.exe:"

    • Rscript.exe is located in a folder named bin within the path where you installed R (in step 1).
    • E.g. C:\Program Files\R\R-VERSION\bin\Rscript.exe, where VERSION is the version of R you have installed.
    • For example, for R version 4.0.2, this will be C:\Program Files\R\R-4.0.2\bin\Rscript.exe.

    This will download and install the R packages required for BEAVR. Once finished, you can close the command prompt window.

  6. To run BEAVR:

    • Double-click Run-BEAVR-Windows.bat

    This will open a new command prompt window. Leave it open to keep BEAVR running.

  7. Open your browser and go to 127.0.0.1:3838 to start using BEAVR.

A new R environment in Mac OS

  1. Download and Install R for your operating system using the installation wizard:

    • Download R 3.6.3 for Catalina here
    • Download R 3.6.3 for El Capitan and higher here

  2. Download and extract the BEAVR setup files from here

  3. Before you can execute the install scripts, you will likely have to allow execute permissions or Mac OS will give you an error. You can do this as follows for both configuration scripts by copying and pasting these commands in Terminal:

    chmod +x ~/Downloads/BEAVR/Setup/Configure-BEAVR-MacOS.command
chmod +x ~/Downloads/BEAVR/Run-BEAVR-MacOS.command

    Where ~/Downloads/ is the path where you extracted BEAVR above (usually your Downloads folder or ~/Desktop/ if you moved it to your desktop.)

  4. Look in the "Setup" folder to find the automated installer for Mac OS named Configure-BEAVR-MacOS.command. If you double-click the Configure-BEAVR-MacOS.command installer, you will likely get another error because you didn't download it from the App store.

Image of chmod in MacOS-2

  1. To get around this, right-click and go to Open and then click Open in the popup box:

Image of chmod in MacOS-2

This will download and install the R packages required for BEAVR.

Note: You may get a popup asking for permission to install Xcode. You must agree to this install and let it finish before continuing!

  1. To run BEAVR:

    • Right-click Run-BEAVR-MacOS.command, click Open and then click Open again in the popup box.

  2. Open your browser and go to localhost:3838 or 127.0.0.1:3838 to start using BEAVR.

A new R environment in Linux

  1. Download and extract the BEAVR setup files for Linux:

  2. Run the automated installer (named Configure-BEAVR-Linux.sh). You will need to open Terminal and enter this command to execute it:

    bash Configure-BEAVR-Linux.sh

    This will install the latest version of R and also download and configure all of the required R packages for BEAVR automatically.

    Note, if you get permission errors, you may need to give the script executable permission

    chmod +x Configure-BEAVR-Linux.sh

  3. To run BEAVR, enter this command from Terminal:

    bash Run-BEAVR-Linux.sh

    Note, if you get permission errors, you may need to give the script executable permission

    chmod +x Run-BEAVR-Linux.sh

  4. Open your browser and go to localhost:3838 or 127.0.0.1:3838 to start using BEAVR.

Run in your existing R installation

If you already have a working installation of R on your computer (version 3.5+), then you can follow these steps to install the required R packages to run BEAVR on any operating system.

Note, the required packages are as follows:

#CRAN packages
colourpicker
data.table
devtools
DT
ggplot2
ggpubr
ggrepel
ggraph
gridExtra
pheatmap
RColorBrewer
scales
shiny
shinydashboard
shinyWidgets
shiny
shinydashboard
shinyjqui
shinyWidgets
shinycssloaders
shinyalert
circlize

#Bioconductor packages
DESeq2
vsn
apeglm
org.Hs.eg.db
org.Mm.eg.db
ReactomePA
enrichplot

#GitHub packages
kevinblighe/EnhancedVolcano
jokergoo/ComplexHeatmap

Use your existing R installation in Windows

Note: R has been tested to work on R 3.6+

  1. Download and extract the BEAVR setup files from here

  2. If you don't have RTools installed already, download and install RTools for Windows using the installation wizard:

    • If you R 4.0.x installed, download RTools here
    • If you have an older version of R installed, download the appropriate version of RTools here

  3. Look in the "Setup" folder to find the automated installer for Windows:

    • Right click on the file named Configure-BEAVR-Windows.bat and click run as administrator (agree to any warnings/prompts to run it)

    This will open a command prompt window.

  4. You will be prompted to "enter the path to your Rscript.exe:"

    • Rscript.exe is located in a folder named bin within the path where you installed R (in step 1).
    • E.g. C:\Program Files\R\R-VERSION\bin\Rscript.exe, where VERSION is the version of R you have installed.
    • For example, for R version 4.0.2, this will be C:\Program Files\R\R-4.0.2\bin\Rscript.exe.

    This will download and install the R packages required for BEAVR. Once finished, you can close the command prompt window.

  5. To run BEAVR:

    • Double-click Run-BEAVR-Windows.bat

    This will open a new command prompt window. Leave it open to keep BEAVR running.

    Or

    You can open the start.R file in RStudio and click Run App button in the top right

    Or

    You can run BEAVR from the R command prompt (after changing the working directory to where you extracted BEAVR)

    setwd('\path\to\where\you\extracted\BEAVR\')
library(shiny)
library(shinydashboard)
runApp(port=3838)

  6. Open your browser and go to 127.0.0.1:3838 to start using BEAVR.

Use your existing R installation in Mac OS

  1. Download and extract the BEAVR setup files from here

  2. Before you can execute the install scripts, you will likely have to allow execute permissions or Mac OS will give you an error. You can do this as follows for both configuration scripts by copying and pasting these commands in Terminal:

    chmod +x ~/Downloads/BEAVR/Setup/Configure-BEAVR-MacOS.command
chmod +x ~/Downloads/BEAVR/Run-BEAVR-MacOS.command

    Where ~/Downloads/ is the path where you extracted BEAVR above (usually your Downloads folder or ~/Desktop/ if you moved it to your desktop.)

  3. Look in the "Setup" folder to find the automated installer for Mac OS named Configure-BEAVR-MacOS.command. If you double-click the Configure-BEAVR-MacOS.command installer, you will likely get another error because you didn't download it from the App store.

Image of chmod in MacOS-2

  1. To get around this, right-click the file and go to Open and then click Open in the popup box:

Image of chmod in MacOS-2

This will download and install the R packages required for BEAVR.

Note: You may get a popup asking for permission to install Xcode. You must agree to this install and let it finish before continuing!

  1. To run BEAVR:

    • Right-click Run-BEAVR-MacOS.command, click Open and then click Open again in the popup box.

    Or

    You can open the start.R file in RStudio and click Run App button in the top right

    Or

    You can run BEAVR from the R command prompt (after changing the working directory to where you extracted BEAVR)

    setwd('\path\to\where\you\extracted\BEAVR\')
library(shiny)
library(shinydashboard)
runApp(port=3838)

  2. Open your browser and go to localhost:3838 or 127.0.0.1:3838 to start using BEAVR.

Use your existing R installation in Linux

  1. Download and extract the BEAVR setup files for Linux:

  2. Run the automated R script file to install all the required packages automatically (or you may install them manually; see the complete list above)

    sudo Rscript installpkgs.R

  3. To run BEAVR, enter this command from Terminal from the BEAVR folder:

    bash Run-BEAVR-Linux.sh

    Note, if you get permission errors, you may need to give the script executable permission

    chmod +x Run-BEAVR-Linux.sh

    Or

    Rsript start.R

    Or

    You can run BEAVR from the R command prompt (after changing the working directory to where you extracted BEAVR)

    setwd('\path\to\where\you\extracted\BEAVR\')
library(shiny)
library(shinydashboard)
runApp(port=3838)

  4. Open your browser and go to localhost:3838 or 127.0.0.1:3838 to start using BEAVR.

Installing BEAVR on a server with multi-user support

If you wish to have BEAVR running on a centralized server for your research group, you or your system administrator can follow the instructions below. We implement this using Docker and ShinyProxy which allows each user to be sandboxed in a unique Docker instance. These instructions are provided for Linux/Ubuntu servers (for now)

  1. Download and extract the BEAVR-multiuser-server-setup.tar.gz setup package from here

    Note, if you get any permission errors running the automated configuration scripts, allow permissions for the scripts:

    chmod +x Docker-setup-ubuntu.sh OpenJDK-setup.sh ShinyProxy-setup.sh

    From within the folder where you extracted the files.

  2. If you already have Docker installed on your Ubuntu server, skip to step 3. Otherwise, in the setup package you just downloaded, run the Docker installer by entering this command in Terminal (this will remove any previous version of Docker!):

    bash Docker-setup-ubuntu.sh

  3. If you already have Java 8 runtime environment installed on your Ubuntu server, skip to step 4. Otherwise, in the setup package you just downloaded, run the OpenJDK installer by entering this command in Terminal (you can use another distribution of JDK like Oracle as well):

    bash OpenJDK-setup.sh

  4. Finally, run the script ShinyProxy-setup.sh to configure Docker and setup ShinyProxy:

    bash ShinyProxy-setup.sh

  5. Configure ShinyProxy settings for user access:

    • If you look in the setup package you downloaded in step 1, you will find a file named application.yml

      • The bottom part of this file is pre-configured for BEAVR already.

      • In the top portion of the file, you will find the configuration line for the port (default 8080) and for user access control:

      • You can keep the default "simple" authentication method and specify user names and passwords in this file (note this file is not encrypted!)

      • You can also LDAP authentication or social authentication

      • You can set this to "none" to have no authentication so anyone with the address can access the server

      • See the ShinyProxy documentation for more information regarding authentication

  6. To start the ShinyProxy server, enter this command in Terminal:

    java -jar shinyproxy-2.3.0.jar

BEAVR Tutorial

Input files (and example files)

BEAVR requires two file inputs:

  1. Read count table file
  2. Sample treatment matrix file

See the Examples folder for examples of these two files prepared for the Sehrawat et al. (2018) dataset.

These examples should also be in the BEAVR setup files you downloaded. Otherwise, you can download them from here

Preparing the read count table file

The read counts table file contains all the raw reads for all the samples in your experiment in a tab-delimited (.txt) or comma-separated (.csv) file type.

The table must be arranged as follows:

  1. The first column must contain ENSEMBL IDs for every gene. The heading name for this column must be gene_id.
  2. The next n columns must contain the raw read counts for each n samples. Label the heading name for each column with a unique sample/replicate identifier.

Here is what it looks like for the Sehrawat et al. (2018) dataset in Microsoft Excel: Image of read count table

The gene_id column contains ENSEMBL IDs for each gene. The columns labelled DMSO_24h-1, DMSO_24h-2, DMSO_24h-3, SP2509_24h-1, SP2509_24h-2, SP2509_24h-3 are the unique samples/replicates in the experiment and contain the raw, unnormalized read quantities for each gene for eacn sample.

Preparing the sample treatment matrix file

The sample treatment matrix file informs BEAVR which columns in the read count file belong to which treatment groups (ie. Untreated and Treated, or Wildtype and Mutant). The file type may be tab-delimited (.txt) or comma-separated (.csv).

The table must be arranged as follows:

  1. The first column must list the sample or replicate identifiers of each sample you have in your read counts file. For example, for n samples in the read counts file, you must have n rows in the column data file. Each row is a unique sample. The heading name for this column can be left blank (it is not used).
    • Note: it is critical that the order of the samples here (each row) is in the same order as the samples (each column) in the read count table file!
  2. The second column identifies which treatment condition/group the samples belong to. The heading name for this column must be condition. For example, in each row of this column, you must identify that respective sample as belonging to Untreated or Treated or Wildtype or Mutant.
  3. In the third column, you can specify additional characteristics for each sample. For example, you can specify different genotype groups or replicates like Replicate-A, Replicate-B, and Replicate-C (must be alphanumeric). The heading name for this column must be replicate.

Here is the sample treatment matrix file prepared for the Sehrawat et al. (2018) dataset in Microsoft Excel: Image of read count table

Loading your data into BEAVR

On the Load Data tab, select the files you have prepared. Make sure you select the correct file type format for each file.

Image of Load Data tab

Experiment settings

On the Settings tab, you can select a few options such as the reference organism, the control condition and the treatment condition, the false discovery rate used for statistics, and the minimum read count required for each gene (genes below this value will be dropped from analysis).

Image of Settings tab

Differential gene expression analysis (DGE)

Click on the Gene Table tab to begin calculations. You will see a progress bar in the bottom right-hand corner of the window. The results will be displayed in a table format which you can search, order and filter and download using the sidebar.

Image of Gene Table tab

Once these calculations finish, you can begin to visualize your data through a series of figures and download the results.

Plots, graphs and heatmaps

Each of the other tabs will provide output of plots, graphs, heatmaps and pathway figures for your data

PCA plot

The PCA tab will plot each sample on the same plot and show you the variances between samples.

Image of PCA Plot tab

Sample clustering plot

The Sample Clustering tab will cluster samples by rows and columns depending on the variance.

Image of Sample Clustering tab

Read count plots

The Read Count Plots tab will allow you to plot the normalized read counts for any number of genes you specify.

Image of Read Count Plots tab

You can enter genes in the sidebar separated by a comma (no spaces, as shown in image). You can specify a grid layout to show multiple plots. For example, specify 2 rows by 2 columns to show 4 plots in a square format. Or you can specify 4 rows by 1 column to show them in a stacked column format. You can also customize the position of the legend or not show a legend at all. To show just a single plot, set the grid to 1x1.

You can also chose to display the read counts in a jitter plot instead of a box plot:

Image of Jitter plots

Heatmap

The Heatmap tab will allow you to display the differential expression of genes in a clustered heatmap. You can enter a list of genes separated by a comma to make a heatmap of genes you are interested in. Alternatively, if you want to make a heatmap of the top differentially expressed genes, select the checkbox Show top genes instead and then enter the number of top genes to show (e.g. the top 10, 50, 100, etc.). Note: increasing the number of genes to show will increase processing time to perform clustering.

Image of Heatmap tab

Volcano plot

The Volcano Plot tab will plot the differentially expressed genes in a volcano plot format which, unlike the heatmap, will also display the p value information for each gene.

Image of Volcano Plot tab

If filtering is enabled in the Gene Table tab, then only those filtered genes will be used to make the volcano plot. Otherwise, all the genes from the Gene Table will be used.

Pathway enrichment plot

The Pathway Enrichment Plot tab will perform over-representation analysis using the filtered (or unfiltered) data set from the Gene Table tab. You can set the p value cutoff in the sidebar of the Pathway Enrichment Plot and also set the numnder of pathways to show.

Image Pathway Enrichment Plot tab

You can also change an option in the sidebar to display this data as a dot plot instead of a bar plot.

Pathway enrichment map

The Pathway Enrichment Map tab will perform over-representation analysis and show you all of the pathways as an interconnected map.

Image of Pathway Enrichment Map tab

Pathway enrichment table

The Pathway Enrichment Table tab will show you details of the enrichment results from the Pathway Enrichment Plot tab. You can use the Download Table button in the sidebar to download the table or use the controls in the sidebar to filter the results.

Image of Pathway Enrichment Table tab

Gene set enrichment analysis (GSEA)

Similar to the Pathway Enrichment Map tab, the GSEA Map tab will perform GSEA and show you an interconnected map of pathways.

Image of GSEA Map tab

GSEA running enrichment score plot

The GSEA plot tab, will perform GSEA on your data and plot the running enrichment score. You can select which pathway to plot in the sidebar from a list of enriched pathways.

Image of GSEA Plot tab

GSEA table

Similar to the Pathway Enrichment Table tab, the GSEA Table tab will show you the results of GSEA in tabular form. You can download the table or filter it using the sidebar controls.

Image of GSEA Table tab

Customizing figures

For all figures, the sidebar shows contextual options and parameters that you can configure. These are dependent on the type of plot/tab you are currently viewing. These options may include a list of genes, log2 fold change cutoffs, p value cutoffs, font sizes, colors, or legend placement options.

These parameters are categories in different headings which you can click to expand and collapse.

Image of sidebar

Resizing plots and adjusting the aspect ratio

Any of the figures in BEAVR can be resized to change their size and/or aspect ratio.

To do this, just put your mouse near the edge of the plots (near the black border). Your mouse pointer will change to indicate the area can be resized. Simply drag with your mouse to resize.

Resizing figures

You can hold the Shift key to maintain the same aspect ratio; otherwise you can freely change the aspect ratio.

Saving figures

You can save any figure by using the Save Plot button above each figure.

Saving figures

You can save images as JPG, PDF, PNG, SVG, or TIFF. The Output dpi (dots per inch) setting is only used for PDF and SVG formats (the default is 48 dpi).

Troubleshooting

Issue Solution
Bug: PCA plot, gene read count plot, and volcano plot won't auto-update after changing the treatment condition in experiments with >2 conditions/treatments. The results table will update correctly. Change one of the parameters in the sidebar for these plots, or just refresh the page, and reload the input files.
Error message:ncol(countData) == nrow(colData) is not TRUE The samples (columns) in your read count table file do not match the samples (rows) in your sample treatment matrix file. Please check to make sure sample names match and that you've selected the correct files (and formats: .csv or .txt) to load into BEAVR.
Error message:None of the keys entered are valid keys for 'ENSEMBL'. Please use the keys method to see a listing of valid arguments This means the ENSEMBL IDs contained in your read count table file cannot be mapped to the reference genome you selected in the Experiment settings tab. Please verify you have selected the correct one.
Error message:mapIds must have at least one key to match against. This error typically occurs when the read count table file is not in the correct format/file type. Please save the file as "CSV (Comma delimited) (.csv)" and not "CSV UTF-8 (Comma delimited) (.csv)".
Error message:unused arguments (pointSize = input$volcanoPointSize, labSize = input$volcanoFontSize_label, legendLabels = c("Not Significant", expression(Log[2] ~ FC ~ only), "p-value only", expression(p - value ~ and ~ log[2] ~ FC)), labCol = "black") This error occurs because you have the incorrect version of the EnhancedVolcano plot installed and BEAVR is loading that one instead of the correct developmental version of the package. To fix this, remove the EnhancedVolcano package by entering this in R: remove.packages("EnhancedVolcano"), restart your R session. Then install the correct version of the package with: devtools::install_github("kevinblighe/EnhancedVolcano"). If you don't have the devtools package, then install it with install.packages("devtools").
Error message:lfcShrink: coef %in% resultsNamesDDS is not TRUE The condition names you entered in your sample treatment matrix file contain characters other than letters, numbers, underscores, or periods. Spaces or special characters are not permitted.

beavr's People

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beavr's Issues

Error message after setting up R environment

After running the setup and then run files as described for R, I am able to access the online browser, then when I proceed to the gene tables tab, the analysis only gets so far and then gives the error message: error in evaluating the argument 'x' in selecting a method for function 'as.data.frame': None of the keys entered are valid keys for 'ENSEMBL'. Please use the keys method to see a listing of valid arguments.

I've ensured my files are set up exactly as laid out in the example.

I tried docker, got me so far as the same step but it won't run the image because I am apparently not on awd.

Any insight would be truly appreciated, I was really looking forward to the results of this analysis!

Pathway Enrichment and GSEA tools do not work

Hello,

Thank you for providing BEAVR. I instaled it and could run your example files.
Unfortunately, i face a problem in performing the Pathway Enrichment or GSEA tools. The other tools work for me.

As far as i understood, the problem seems to be caused by the Gene table output.(?)
I use the Docker and get a Warning like this: "Warning: Error in if: missing value where TRUE/FALSE needed".
In the Gene table not all GeneIDs are linked to a gene name, meaning some gene names are missing.
I work with mouse genes.
Could this be the problem? Do you have an idea what to do?
Do you need anything else to understand my issue?

best

Read Count Table File Size Limit

Hello,

Thank you for providing very detailed installation instructions, I was able to install BEAVR and run the sample dataset without any issues. When I attempted to upload my RNA-seq data into BEAVR, I exceeded the file size limit for the read count table file. May you please let me know what this maximum file size limit is?

Multivariate analysis

Hi, I am very interested in your tool and it seems very convenient. From the description, it appears that it supports 2 group analysis only. Is that correct? I have a dataset with 4 groups and another with 9 groups that need to be analyzed, can your tool be used for multivariate analysis or is it planned in future versions?

Add reference genome

Hi,

Thanks for developing a convenient tool for the biologist, like me, to quickly establish a local analysis platform. However, there are only Human and Mouse reference genomes in the database. Is it possible to add other reference genomes to the database by ourself? And how ? Thanks for your kindly help.

Bug: The tag provided is not a shiny tag. Action abort?

After start:

Warning in FUN(X[[i]], ...) :
The tag provided is not a shiny tag. Action abort.
... ...
Warning in FUN(X[[i]], ...) :
The tag provided is not a shiny tag. Action abort.
Warning in FUN(X[[i]], ...) :
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Is this a bug?

An error has occurred. Check your logs or contact the app author for clarification.

After loading data and adjusting settings, once I click on Gene Table I get this message after a moment of waiting:
An error has occurred. Check your logs or contact the app author for clarification.

I did notice some errors after installing BEAVR-Docker in Windows. Not sure if these at all are related. Sorry for the long block below.

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Digest: sha256:5dc40e0ca36d169a753be5012d3afa2bf2a2e10d05d88e29e695056b08dc7761
Status: Downloaded newer image for pirunthan/beavr:latest
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[2022-02-09T23:56:56.579] [INFO] shiny-server - Shiny Server v1.5.13.943 (Node.js v12.14.1)
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[2022-02-09T23:56:56.602] [WARN] shiny-server - Running as root unnecessarily is a security risk! You could be running more securely as non-root.
[2022-02-09T23:56:56.604] [INFO] shiny-server - Starting listener on http://[::]:3838
[2022-02-09T23:58:04.468] [INFO] shiny-server - Created bookmark state directory: /var/lib/shiny-server/bookmarks
[2022-02-09T23:58:04.468] [INFO] shiny-server - Created user bookmark state directory: /var/lib/shiny-server/bookmarks/shinyf5043192: Waiting
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Bioconductor version 3.10 (BiocManager 1.30.10), ?BiocManager::install for help
Bioconductor version '3.10' is out-of-date; the current release version '3.14'
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circlize version 0.4.8
CRAN page: https://cran.r-project.org/package=circlize
Github page: https://github.com/jokergoo/circlize
Documentation: http://jokergoo.github.io/circlize_book/book/

If you use it in published research, please cite:
Gu, Z. circlize implements and enhances circular visualization
  in R. Bioinformatics 2014.
========================================


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If you use ReactomePA in published research, please cite:
Guangchuang Yu, Qing-Yu He. ReactomePA: an R/Bioconductor package for reactome pathway analysis and visualization. Molecular BioSystems 2016, 12(2):477-479

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Github page: https://github.com/jokergoo/ComplexHeatmap
Documentation: http://jokergoo.github.io/ComplexHeatmap-reference

If you use it in published research, please cite:
Gu, Z. Complex heatmaps reveal patterns and correlations in multidimensional
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Warning in FUN(X[[i]], ...) :
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^AIf you use it in published research, please cite:
Gu, Z. circlize implements and enhances circular visualization
  in R. Bioinformatics 2014.
========================================


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Welcome to Bioconductor

    Vignettes contain introductory material; view with
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Registered S3 method overwritten by 'enrichplot':
  method               from
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ReactomePA v1.30.0  For help: https://guangchuangyu.github.io/ReactomePA

If you use ReactomePA in published research, please cite:
Guangchuang Yu, Qing-Yu He. ReactomePA: an R/Bioconductor package for reactome pathway analysis and visualization. Molecular BioSystems 2016, 12(2):477-479

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========================================
ComplexHeatmap version 2.5.2
Bioconductor page: http://bioconductor.org/packages/ComplexHeatmap/
Github page: https://github.com/jokergoo/ComplexHeatmap
Documentation: http://jokergoo.github.io/ComplexHeatmap-reference

If you use it in published research, please cite:
Gu, Z. Complex heatmaps reveal patterns and correlations in multidimensional
  genomic data. Bioinformatics 2016.

This message can be suppressed by:
  suppressPackageStartupMessages(library(ComplexHeatmap))
========================================
! pheatmap() has been masked by ComplexHeatmap::pheatmap(). 90% of the arguments
   in the original pheatmap() are identically supported in the new function. You
   can still use the original function by explicitly calling pheatmap::pheatmap().


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Warning in FUN(X[[i]], ...) :
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converting counts to integer mode
estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testing
-- replacing outliers and refitting for 134 genes
-- DESeq argument 'minReplicatesForReplace' = 7
-- original counts are preserved in counts(dds)
estimating dispersions
fitting model and testing
using 'apeglm' for LFC shrinkage. If used in published research, please cite:
    Zhu, A., Ibrahim, J.G., Love, M.I. (2018) Heavy-tailed prior distributions for
    sequence count data: removing the noise and preserving large differences.
    Bioinformatics. https://doi.org/10.1093/bioinformatics/bty895
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Digest: sha256:5dc40e0ca36d169a753be5012d3afa2bf2a2e10d05d88e29e695056b08dc7761
Status: Downloaded newer image for pirunthan/beavr:latest
77be22d2817d: Downloading [======>                                            ]  26.79MB/205.7MB
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[2022-02-09T23:56:56.579] [INFO] shiny-server - Shiny Server v1.5.13.943 (Node.js v12.14.1)
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Bioconductor version 3.10 (BiocManager 1.30.10), ?BiocManager::install for help
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  is available with R version '4.1'; see https://bioconductor.org/install

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========================================
circlize version 0.4.8
CRAN page: https://cran.r-project.org/package=circlize
Github page: https://github.com/jokergoo/circlize
Documentation: http://jokergoo.github.io/circlize_book/book/

If you use it in published research, please cite:
Gu, Z. circlize implements and enhances circular visualization
  in R. Bioinformatics 2014.
========================================


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Registered S3 method overwritten by 'enrichplot':
  method               from
  fortify.enrichResult DOSE
ReactomePA v1.30.0  For help: https://guangchuangyu.github.io/ReactomePA

If you use ReactomePA in published research, please cite:
Guangchuang Yu, Qing-Yu He. ReactomePA: an R/Bioconductor package for reactome pathway analysis and visualization. Molecular BioSystems 2016, 12(2):477-479

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Loading required package: grid
========================================
ComplexHeatmap version 2.5.2
Bioconductor page: http://bioconductor.org/packages/ComplexHeatmap/
Github page: https://github.com/jokergoo/ComplexHeatmap
Documentation: http://jokergoo.github.io/ComplexHeatmap-reference

If you use it in published research, please cite:
Gu, Z. Complex heatmaps reveal patterns and correlations in multidimensional
  genomic data. Bioinformatics 2016.

This message can be suppressed by:
  suppressPackageStartupMessages(library(ComplexHeatmap))
========================================
! pheatmap() has been masked by ComplexHeatmap::pheatmap(). 90% of the arguments
   in the original pheatmap() are identically supported in the new function. You
   can still use the original function by explicitly calling pheatmap::pheatmap().


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Warning in FUN(X[[i]], ...) :
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Warning in FUN(X[[i]], ...) :
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^AIf you use it in published research, please cite:
Gu, Z. circlize implements and enhances circular visualization
  in R. Bioinformatics 2014.
========================================


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    dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep,
    grepl, intersect, is.unsorted, lapply, Map, mapply, match, mget,
    order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,
    rbind, Reduce, rownames, sapply, setdiff, sort, table, tapply,
    union, unique, unsplit, which, which.max, which.min


Attaching package: ‘S4Vectors’

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Loading required package: IRanges

Attaching package: ‘IRanges’

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Loading required package: GenomicRanges
Loading required package: GenomeInfoDb
Loading required package: SummarizedExperiment
Loading required package: Biobase
Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.

Loading required package: DelayedArray
Loading required package: matrixStats

Attaching package: ‘matrixStats’

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Loading required package: BiocParallel

Attaching package: ‘DelayedArray’

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Loading required package: AnnotationDbi



Registered S3 method overwritten by 'enrichplot':
  method               from
  fortify.enrichResult DOSE
ReactomePA v1.30.0  For help: https://guangchuangyu.github.io/ReactomePA

If you use ReactomePA in published research, please cite:
Guangchuang Yu, Qing-Yu He. ReactomePA: an R/Bioconductor package for reactome pathway analysis and visualization. Molecular BioSystems 2016, 12(2):477-479

Attaching package: ‘enrichplot’

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Loading required package: grid
========================================
ComplexHeatmap version 2.5.2
Bioconductor page: http://bioconductor.org/packages/ComplexHeatmap/
Github page: https://github.com/jokergoo/ComplexHeatmap
Documentation: http://jokergoo.github.io/ComplexHeatmap-reference

If you use it in published research, please cite:
Gu, Z. Complex heatmaps reveal patterns and correlations in multidimensional
  genomic data. Bioinformatics 2016.

This message can be suppressed by:
  suppressPackageStartupMessages(library(ComplexHeatmap))
========================================
! pheatmap() has been masked by ComplexHeatmap::pheatmap(). 90% of the arguments
   in the original pheatmap() are identically supported in the new function. You
   can still use the original function by explicitly calling pheatmap::pheatmap().


Attaching package: ‘ComplexHeatmap’

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Warning in FUN(X[[i]], ...) :
  The tag provided is not a shiny tag. Action abort.
Warning in FUN(X[[i]], ...) :
  The tag provided is not a shiny tag. Action abort.
Warning in FUN(X[[i]], ...) :
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Warning in FUN(X[[i]], ...) :
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Warning in FUN(X[[i]], ...) :
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Warning in FUN(X[[i]], ...) :
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Warning in FUN(X[[i]], ...) :
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Warning in FUN(X[[i]], ...) :
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Warning in FUN(X[[i]], ...) :
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Warning in FUN(X[[i]], ...) :
  The tag provided is not a shiny tag. Action abort.
Warning in FUN(X[[i]], ...) :
  The tag provided is not a shiny tag. Action abort.
Warning in FUN(X[[i]], ...) :
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Warning in FUN(X[[i]], ...) :
  The tag provided is not a shiny tag. Action abort.
Warning in FUN(X[[i]], ...) :
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Warning in FUN(X[[i]], ...) :
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Warning in FUN(X[[i]], ...) :
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converting counts to integer mode
estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testing
-- replacing outliers and refitting for 134 genes
-- DESeq argument 'minReplicatesForReplace' = 7
-- original counts are preserved in counts(dds)
estimating dispersions
fitting model and testing
using 'apeglm' for LFC shrinkage. If used in published research, please cite:
    Zhu, A., Ibrahim, J.G., Love, M.I. (2018) Heavy-tailed prior distributions for
    sequence count data: removing the noise and preserving large differences.
    Bioinformatics. https://doi.org/10.1093/bioinformatics/bty895
Warning: Error in mapIds_base: mapIds must have at least one key to match against.
  127: stop
  126: mapIds_base
  125: mapIds
  123: <reactive:calc_res> [/srv/shiny-server/server.R#307]
  107: calc_res
  102: exprFunc [/srv/shiny-server/server.R#351]
  101: widgetFunc
  100: func
   87: origRenderFunc
   86: renderFunc
   82: origRenderFunc
   81: output$calc_res_values
    1: runApp

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