Exploration and Visualisation of Skeletal Transcriptomics Data

Allows search and visualisation of a collection of uniformly processed skeletal transcriptomic datasets. Includes methods to identify datasets where genes of interest are differentially expressed and find datasets with a similar gene expression pattern to a query dataset Soul J, Hardingham TE, Boot-Handford RP, Schwartz JM (2019) .

SkeletalVis

The goal of SkeletalVis is to enable the search and visualisation of uniformly processed skeletal transcriptomic datasets. It provides tools to identify datasets in which genes of interest are differentially expressed and to discover datasets with similar gene expression profiles to a given query.

It additional allow exploration of the OATargets database and prioritised genes for causing an osteoarthritis damage phenotype in animal models.

Installation

You can install the release version of SkeletalVis from CRAN:

install.packages("SkeletalVis")

You can install the development version of SkeletalVis from GitHub with:

# install.packages("pak")
pak::pak("soulj/SkeletalVis")

Example

This is a basic example which shows how to search for a gene of interest across the SkeletalVis data. See the vignette for a full tutorial of SkeletalVis.

# Load the library
library(SkeletalVis)

# Set up the database
skeletalvis <- load_skeletalvis()
#> All files are already present

# Get the differential expression of the gene SOX9 across the database
gene_results <- get_gene_fold_changes(skeletalvis, "SOX9", return_fdr = TRUE)
head(gene_results)
#> # A tibble: 6 × 15
#>   datasetID log2FoldChange       FDR Gene  accession comparisonsText Description
#>   <chr>              <dbl>     <dbl> <chr> <chr>     <chr>           <chr>      
#> 1 E-GEOD-6…           7.15 0         SOX9  E-GEOD-6… SOX9vseGFP      "SOX9 over…
#> 2 GSE15511…           3.11 0         SOX9  GSE155118 Sox5_6_9_OEvsc… "Primary c…
#> 3 E-GEOD-5…          -1.28 2.25e-195 SOX9  E-GEOD-5… Day18vsDay2     "Temporal …
#> 4 GSE11026…           3.47 1.37e-132 SOX9  GSE11026… YAP KO_MSCvsWT… "YAP1 and …
#> 5 GSE12103…           1.99 3.92e-128 SOX9  GSE121033 Lentiviruses e… "Polycomb …
#> 6 GSE12516…           3.18 8.58e-112 SOX9  GSE125167 PLZF_10_days_d… "PLZF knoc…
#> # ℹ 8 more variables: PMID <chr>, Species <chr>, Tissue <chr>, ExpType <chr>,
#> #   Perturbation <chr>, Effect <chr>, Type <chr>, platform <chr>

There are also several built in plotting functions:

# Plot the log2fold change against the FDR for SOX9 across the datasets
volcano_plot(gene_results, number_points = 3)