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Baseline CompaRe

Baseline CompaRe is an R Shiny app designed to help with the analysis of E8.5-10.5 mouse trancriptomics data, particularly in cases where the embryos of interest are developmentally delayed. We have produced RNA-seq data for wild-type embryos for stages from 4 to 36 somites to form a baseline for comparison to experimental samples. This allows us to prioritise genes that are more likely to be differentially expressed due to the condition of interest, rather than due to developmental delay (see Collins et al. (2019)).


Please contact the Busch Lab ( for futher information.


For instructions about downloading the Shiny app for use locally, please see the Installation section.

The app assumes you have count data for Ensembl gene ids produced from Illumina RNA-seq. Count data for the mutant lines presented in Collins et al. 2019 are available for download from Figshare. See the Preprocessed data section. Our baseline data was produced from Illumina RNA-seq, mapped with TopHat2 and counted with HTSeq-count.

File Upload

Upload a samples file and a counts file.

Principal Component Analysis (PCA)

When the "Analyse Data" button is clicked, the App does PCA on the data to tell if the experimental samples are compatible with the baseline data. The experimental samples should not cluster on their own in the PCA. It is possible that mutant samples will cluster away from the baseline, but the sibling embryos should be close to similarly staged baseline embryos as in the example below. Two plots are produced, one containing baseline samples that match the stages represented in the experimental data and one with all the baseline samples.

If your samples are clustered away from the baseline samples, this analysis is probably not appropriate.

PCA: Baseline sample for experimental stages included

PCA plot of demo data

PCA: All Baseline samples included

PCA plot of demo data with all Baseline samples

Use the "PCA Options" sidepanel to select which Principal Components are plotted against each other and whether to plot sample names. The plots can de downloaded in various formats. "Download Current Plot" saves only the current plot of two principal components plotted against each other. "Download all PCs" produces a pdf file of each component plotted against the next. The current plot can also be downloaded as a .rda file for loading into R and customising later.

DESeq2 Analysis

Once the PCA is finished, the DESeq2 analysis is started. For full details of the method, please see Collins et al. Briefly, DESeq2 is run three different ways (shown below) and the significant gene lists are overlapped to produce the results set.

Analysis Set Samples DESEq2 model
Experiment Only Experimental Data only counts ~ (sex +) condition
Experiment + Baseline Experimental Data and stage-matched Baseline samples counts ~ (sex +) condition
Experiment + Baseline with Stage Experimental Data and stage-matched Baseline samples counts ~ (sex +) stage + condition


The lists of significantly differentially expressed genes from the three different analyses are overlapped and the results sets are produced as in the diagram below.

DESeq2 results sets

The results sets can be selected by clicking on the button on the left of the "Results" tab. The Mutant Response set should be enriched for genes whose differential expression is due to the condition and not due to stage differences between mutant and sibling samples. The Experimental Only significant results are available as well for comparison, as is a table of all the genes in the analysis with log2 fold changes and adjusted pvalues for each DESeq2 run.

The full results table can be downloaded and a text file of saved as a .rda file for loading into R.

Count Plot

Clicking on a line of the results table produces a count plot for the selected gene showing the normalised counts as calculated by DESeq2. Shapes indicate Condition and Stage is plotted as colour.

Mutant Response Example

Count Plot

Delay Example

Count Plot showing Delay effect

Preprocessed Data

All the count data for the lines processed as part of the DMDD project are available for download from Figshare. The Figshare fileset consists of files of counts for each individual mutant line and a single sample info file for all lines. There is also a tar gzipped archive file for downloading everything at once.

To use this data, download the appropriate count file and the samples file. You will need to make a separate samples file containg only the samples for the line to be analysed. For example if are using the count file for the Hira mutant line (Hira-deseq2-blacklist-adj-gt-adj-sex-outliers.tsv) then the samples file should look like this.

condition group stage somite_number
Hira_het1 het M 27somites 27
Hira_het2 het F 24somites 24
Hira_het3 het M 26somites 26
Hira_het4 het F 22somites 22
Hira_het5 het M 26somites 26
Hira_het6 het F 24somites 24
Hira_hom1 hom M 14somites 14
Hira_hom2 hom F 10somites 10
Hira_hom3 hom M 16somites 16
Hira_hom4 hom F 8somites 8
Hira_hom6 hom F 8somites 8
Hira_hom7 hom F 8somites 8
Hira_wt2 wt F 25somites 25
Hira_wt3 wt F 26somites 26
Hira_wt4 wt F 23somites 23
Hira_wt5 wt F 22somites 22
Hira_wt6 wt M 28somites 28

It is possible to use this app to simply visualise the count data, but in that case the analysis must still be run first, because the count plots are generated from the results table. If you would like to view the data for one of the mutant lines that does not show developmental delay, you can still use this app. However you should only use the Experiment Samples Only table in the RESULTS tab.


The code can be download from GitHub or using git git clone

The App can also be run directly from GitHub in Rstudio.


To keep the downloaded files for running more than once, supply a destdir

runGitHub('richysix/baseline_compare', destdir = 'path/to/destdir')
# to run the app again


These packages can be installed using install.packages

  • shiny
  • shinycssloaders
  • shinyjs
  • shinyBS
  • DT
  • DESeq2
  • ggplot2
  • reshape2
  • scales
  • svglite

These packages need to be installed from GitHub



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