Introduction to iSEEde
Kevin Rue-Albrecht
University of Oxfordkevin.rue-albrecht@imm.ox.ac.uk
31 October 2024
Source:vignettes/iSEEde.Rmd
iSEEde.RmdBasics
Install iSEEde
is an open-source statistical environment which can be easily modified to enhance its functionality via packages. iSEEde is an package available via the Bioconductor repository for packages. can be installed on any operating system from CRAN after which you can install iSEEde by using the following commands in your session:
if (!requireNamespace("BiocManager", quietly = TRUE)) {
install.packages("BiocManager")
}
BiocManager::install("iSEEde")
## Check that you have a valid Bioconductor installation
BiocManager::valid()Required knowledge
iSEEde is based on many other packages and in particular on those that have implemented the infrastructure needed for dealing with omics data and interactive visualisation. That is, packages like SummarizedExperiment, SingleCellExperiment, iSEE and shiny.
If you are asking yourself the question “Where do I start using Bioconductor?” you might be interested in this blog post.
Asking for help
As package developers, we try to explain clearly how to use our
packages and in which order to use the functions. But
and Bioconductor have a steep learning curve so it is
critical to learn where to ask for help. The blog post quoted above
mentions some but we would like to highlight the Bioconductor support site
as the main resource for getting help: remember to use the
iSEEde tag and check the older posts.
Other alternatives are available such as creating GitHub issues and
tweeting. However, please note that if you want to receive help you
should adhere to the posting
guidelines. It is particularly critical that you provide a small
reproducible example and your session information so package developers
can track down the source of the error.
Citing iSEEde
We hope that iSEEde will be useful for your research. Please use the following information to cite the package and the overall approach. Thank you!
## Citation info
citation("iSEEde")
#> To cite package 'iSEEde' in publications use:
#>
#> Rue-Albrecht K (2024). _iSEEde: iSEE extension for panels related to differential
#> expression analysis_. R package version 1.5.0, <https://github.com/iSEE/iSEEde>.
#>
#> A BibTeX entry for LaTeX users is
#>
#> @Manual{,
#> title = {iSEEde: iSEE extension for panels related to differential expression analysis},
#> author = {Kevin Rue-Albrecht},
#> year = {2024},
#> note = {R package version 1.5.0},
#> url = {https://github.com/iSEE/iSEEde},
#> }Quick start to using to iSEEde
The example below demonstrates how to use iSEEde functionality in a simple differential expression workflow.
Specifically, the DESeq2
package is used to perform a simple differential expression analysis.
Then, the results of one contrast – extracted using the function
DESeq2::results() function – are embedded into the SummarizedExperiment
object airway using the function
iSEEde::embedContrastResults(). This ensures that the
differential expression results for that contrast are accessible to
specialised interactive panels in iSEE
applications.
library("iSEEde")
library("airway")
library("DESeq2")
library("iSEE")
# Example data ----
data("airway")
airway$dex <- relevel(airway$dex, "untrt")
dds <- DESeqDataSet(airway, ~ 0 + dex + cell)
dds <- DESeq(dds)
res_deseq2 <- results(dds, contrast = list("dextrt", "dexuntrt"))
head(res_deseq2)
#> log2 fold change (MLE): dextrt vs dexuntrt
#> Wald test p-value: dextrt vs dexuntrt
#> DataFrame with 6 rows and 6 columns
#> baseMean log2FoldChange lfcSE stat pvalue padj
#> <numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
#> ENSG00000000003 708.602170 -0.3812539 0.100654 -3.787752 0.000152016 0.00128292
#> ENSG00000000005 0.000000 NA NA NA NA NA
#> ENSG00000000419 520.297901 0.2068127 0.112219 1.842944 0.065337213 0.19646961
#> ENSG00000000457 237.163037 0.0379205 0.143445 0.264356 0.791505314 0.91141884
#> ENSG00000000460 57.932633 -0.0881679 0.287142 -0.307054 0.758802543 0.89500551
#> ENSG00000000938 0.318098 -1.3782416 3.499906 -0.393794 0.693733216 NA
# iSEE / iSEEde ---
airway <- embedContrastResults(res_deseq2, airway, name = "dex: trt vs untrt")
contrastResults(airway)
#> DataFrame with 63677 rows and 1 column
#> dex: trt vs untrt
#> <iSEEDESeq2Results>
#> ENSG00000000003 <iSEEDESeq2Results>
#> ENSG00000000005 <iSEEDESeq2Results>
#> ENSG00000000419 <iSEEDESeq2Results>
#> ENSG00000000457 <iSEEDESeq2Results>
#> ENSG00000000460 <iSEEDESeq2Results>
#> ... ...
#> ENSG00000273489 <iSEEDESeq2Results>
#> ENSG00000273490 <iSEEDESeq2Results>
#> ENSG00000273491 <iSEEDESeq2Results>
#> ENSG00000273492 <iSEEDESeq2Results>
#> ENSG00000273493 <iSEEDESeq2Results>
app <- iSEE(airway, initial = list(
DETable(ContrastName="dex: trt vs untrt", HiddenColumns = c("baseMean",
"lfcSE", "stat"), PanelWidth = 4L),
VolcanoPlot(ContrastName="dex: trt vs untrt", PanelWidth = 4L),
MAPlot(ContrastName="dex: trt vs untrt", PanelWidth = 4L)
))
if (interactive()) {
shiny::runApp(app)
}
Reproducibility
The iSEEde package (Rue-Albrecht, 2024) was made possible thanks to:
- R (R Core Team, 2024)
- BiocStyle (Oleś, 2024)
- knitr (Xie, 2024)
- RefManageR (McLean, 2017)
- rmarkdown (Allaire, Xie, Dervieux, McPherson, Luraschi, Ushey, Atkins, Wickham, Cheng, Chang, and Iannone, 2024)
- sessioninfo (Wickham, Chang, Flight, Müller, and Hester, 2021)
- testthat (Wickham, 2011)
This package was developed using biocthis.
Code for creating the vignette
## Create the vignette
library("rmarkdown")
system.time(render("iSEEde.Rmd", "BiocStyle::html_document"))
## Extract the R code
library("knitr")
knit("iSEEde.Rmd", tangle = TRUE)Date the vignette was generated.
#> [1] "2024-10-31 10:10:42 UTC"Wallclock time spent generating the vignette.
#> Time difference of 22.597 secssession information.
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#> ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────Bibliography
This vignette was generated using BiocStyle (Oleś, 2024) with knitr (Xie, 2024) and rmarkdown (Allaire, Xie, Dervieux et al., 2024) running behind the scenes.
Citations made with RefManageR (McLean, 2017).
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