Launch a Shiny App for interactive exploration of a dataset from the perspective of Principal Components Analysis
pcaExplorer(
dds = NULL,
dst = NULL,
countmatrix = NULL,
coldata = NULL,
pca2go = NULL,
annotation = NULL,
runLocal = TRUE
)A DESeqDataSet object. If not provided, then a countmatrix
and a coldata need to be provided. If none of the above is provided, it is possible
to upload the data during the execution of the Shiny App
A DESeqTransform object. Can be computed from the dds object
if left NULL. If none is provided, then a countmatrix
and a coldata need to be provided. If none of the above is provided, it is possible
to upload the data during the execution of the Shiny App
A count matrix, with genes as rows and samples as columns. If not provided, it is possible to upload the data during the execution of the Shiny App
A data.frame containing the info on the covariates of each sample. If not provided, it is possible to upload the data during the execution of the Shiny App
An object generated by the pca2go function, which contains
the information on enriched functional categories in the genes that show the top or bottom loadings
in each principal component of interest. If not provided, it is possible
to compute live during the execution of the Shiny App
A data.frame object, with row.names as gene identifiers (e.g. ENSEMBL ids)
and a column, gene_name, containing e.g. HGNC-based gene symbols
A logical indicating whether the app is to be run locally or remotely on a server, which determines how documentation will be accessed.
A Shiny App is launched for interactive data exploration
library(airway)
data(airway)
airway
#> class: RangedSummarizedExperiment
#> dim: 63677 8
#> metadata(1): ''
#> assays(1): counts
#> rownames(63677): ENSG00000000003 ENSG00000000005 ... ENSG00000273492
#> ENSG00000273493
#> rowData names(10): gene_id gene_name ... seq_coord_system symbol
#> colnames(8): SRR1039508 SRR1039509 ... SRR1039520 SRR1039521
#> colData names(9): SampleName cell ... Sample BioSample
dds_airway <- DESeq2::DESeqDataSetFromMatrix(assay(airway),
colData = colData(airway),
design = ~dex+cell)
if (FALSE) {
rld_airway <- DESeq2::rlogTransformation(dds_airway)
pcaExplorer(dds_airway, rld_airway)
pcaExplorer(countmatrix = counts(dds_airway), coldata = colData(dds_airway))
pcaExplorer() # and then upload count matrix, covariate matrix (and eventual annotation)
}