To install oneSENSE package, start R and run the following code:
if (!requireNamespace("BiocManager", quietly=TRUE))
install.packages("BiocManager")
BiocManager::install("oneSENSE")
Load the package with the following code
## Warning in fun(libname, pkgname): Package 'oneSENSE' is deprecated and will be removed from Bioconductor
## version 3.20
Read the package description to find out more about oneSENSE GUI
## No documentation for 'onesense_GUI()' in specified packages and libraries:
## you could try '??onesense_GUI()'
One-SENSE measures cellular parameters assinged to manually
predefined
catergories, and a one-dimensional map is constructed for each
catergory
using t-SNE. Each dimension is informative and can be annotated
through
the use of heatplots aligned in parallel to each axis, allowing
for
simultaneous visualization of two catergories across a two-dimensional
plot.
The cellular occupancy of the resulting plots alllows for direct
assessment
of the relationships between the categories.
Read more about One-SENSE: here
The easiest way to access oneSENSE is via the Graphics User
Interface(GUI)
provided in the package. After loading the package, simply set the
directory
as instructed in the note above and run the following code:
The interface will appear like below, you can click the information
button
! to check the explanation for each entry and customize
your own analysis.
1. Choose the directory where the FCS files are located.
2. Display the markers you want to select
3. Select the first, second and/or third(optional) category
of markers
you want to group together
4. Input the number you want to subsample from each FCS file under ceil.
5. Input the number of bins you want for the cells to be sorted into
6. Press submit and it will run to produce median heatplots
7. If you wish to do a frequency heatmap, press select
coordinates,
and after selecting coordinates, press generate CSV.
8. Press submit frequency heatplot to generate a different
set of heatplot
Depending on the size of your data, it will take some time to
run
the analysis. Once done, the oneSENSE visualisations will be
displayed.
## R version 4.4.0 alpha (2024-03-27 r86216)
## Platform: aarch64-apple-darwin20
## Running under: macOS Ventura 13.6.5
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.0
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## time zone: America/New_York
## tzcode source: internal
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] oneSENSE_1.26.0 scatterplot3d_0.3-44 shinyFiles_0.9.3
## [4] shiny_1.8.1 webshot_0.5.5
##
## loaded via a namespace (and not attached):
## [1] tidyr_1.3.1 generics_0.1.3 plotly_4.10.4
## [4] sass_0.4.9 utf8_1.2.4 gplots_3.1.3.1
## [7] bitops_1.0-7 KernSmooth_2.23-22 gtools_3.9.5
## [10] digest_0.6.35 magrittr_2.0.3 caTools_1.18.2
## [13] evaluate_0.23 grid_4.4.0 fastmap_1.1.1
## [16] jsonlite_1.8.8 promises_1.2.1 httr_1.4.7
## [19] purrr_1.0.2 fansi_1.0.6 viridisLite_0.4.2
## [22] scales_1.3.0 lazyeval_0.2.2 jquerylib_0.1.4
## [25] cli_3.6.2 rlang_1.1.3 RProtoBufLib_2.16.0
## [28] Biobase_2.64.0 munsell_0.5.0 cachem_1.0.8
## [31] yaml_2.3.8 Rtsne_0.17 cytolib_2.16.0
## [34] tools_4.4.0 dplyr_1.1.4 colorspace_2.1-0
## [37] ggplot2_3.5.0 httpuv_1.6.15 BiocGenerics_0.50.0
## [40] vctrs_0.6.5 R6_2.5.1 mime_0.12
## [43] matrixStats_1.2.0 stats4_4.4.0 lifecycle_1.0.4
## [46] htmlwidgets_1.6.4 S4Vectors_0.42.0 fs_1.6.3
## [49] flowCore_2.16.0 pkgconfig_2.0.3 pillar_1.9.0
## [52] bslib_0.6.2 later_1.3.2 gtable_0.3.4
## [55] data.table_1.15.4 glue_1.7.0 Rcpp_1.0.12
## [58] tidyselect_1.2.1 xfun_0.43 tibble_3.2.1
## [61] knitr_1.45 xtable_1.8-4 htmltools_0.5.8
## [64] rmarkdown_2.26 compiler_4.4.0