scafari: Exploring scDNA-seq data

scafari is a Shiny app for the analysis of scDNA-seq data provided in .h5 file. The analysis is divided into four steps, represented by corresponding tabs: “Sequencing”, “Panel”, “Variants” and “Explore Variants”.

Requirements

To run scafari, you need R (Version 4.5 or higher) and R Shiny.

Installation

To install scafari, open R and install the package.

BiocManager::install('scafari')

scafari is available as R package and as shiny app. A detailed description how to use the R packages is in the vignette in /vignettes directory.

Running the scafari shiny app

launch the app using launchScafariShiny()

Input

.h5 files are accepted as input. After upload it is evaluated if all important information is inlcuded in the .h5 file.
a test dataset is available in this repo in the testdata directory

Features

Upload

data upload
input checkup

Sequencing

basic information
log-log plot
sequencing statistics table
mapping statistics table
tapestri table
R1 to R2 bar plot

Panel

panel information table
amplicon overview table
amplicon distribution plot
amplicon performance plot
amplicon uniformity plot

Variants

interactive filtering
variant annotation
filtered and annotated variants table
VAF heatmap with genotype annotation
genotype distribution and quality plot

Explore variants

interactive variant selection
elbow plot (k-means)
cluster plot (k-means)
VAF, cluster and genotype plots splitted by clones

Demo

tables are downloaded to your Downloads directory.

Upload

just upload your .h5 file

Sequencing

hit the "Sequencing" tab
scroll through the page
explore the interactive bar plot

Panel

hit the "Panel" tab
the read counts are normalized and annotated in the background. Therefore, it can take some time until everything is processed and the plots are loaded

Variants

click on the “Variants” tab
if necessary change default filtering parameters
start with filtering the data by click on the Apply Filtering button
the filtering can take some time (☕)
explore the filtered variants

Explore variants

Variant selection

hit the "Explore variants" tab
with the results in the "Variants" tab you identify your variants of interest
select those variants in the table on the left by clicking on them. You can deselect variants by clicking them a second time. On the right is a VAF heatmap which might help you to find you variants of interest
if you selected all variants of interest click on Select variants
the heatmap on the right will now be actualized and you can check you selection
- if you want to change something you can change the selection on the table on the left and than hit Select variants again
- if you want to continue hit Continue with this variant selection

Cell clustering

select the number of centroids for the k-means clustering. The elbow plots will help you doing it
if you know the number of clusteres you want to use. Change the number in Number of clusters by clicking on the small triangles
hit Start kmeans

Explore clusters

you can see which variants are included in the analysis under "Variants included in Clustering:"

R Session

> sessionInfo()
R version 4.5.0 (2025-04-11)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 24.04.2 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.12.0 
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0  LAPACK version 3.12.0

locale:
 [1] LC_CTYPE=de_DE.UTF-8       LC_NUMERIC=C               LC_TIME=de_DE.UTF-8        LC_COLLATE=de_DE.UTF-8     LC_MONETARY=de_DE.UTF-8    LC_MESSAGES=de_DE.UTF-8    LC_PAPER=de_DE.UTF-8      
 [8] LC_NAME=C                  LC_ADDRESS=C               LC_TELEPHONE=C             LC_MEASUREMENT=de_DE.UTF-8 LC_IDENTIFICATION=C       

time zone: Europe/Berlin
tzcode source: system (glibc)

attached base packages:
[1] stats4    stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] scafari_0.99.12             SingleCellExperiment_1.30.1 SummarizedExperiment_1.38.1 Biobase_2.68.0              GenomicRanges_1.60.0        GenomeInfoDb_1.44.0         IRanges_2.42.0             
 [8] S4Vectors_0.46.0            BiocGenerics_0.54.0         generics_0.1.4              MatrixGenerics_1.20.0       matrixStats_1.5.0           rhdf5_2.52.0                shiny_1.10.0               

loaded via a namespace (and not attached):
  [1] fs_1.6.6                 ProtGenerics_1.40.0      bitops_1.0-9             devtools_2.4.5           fontawesome_0.5.3        httr_1.4.7               RColorBrewer_1.1-3       doParallel_1.0.17       
  [9] profvis_0.4.0            tools_4.5.0              backports_1.5.0          R6_2.6.1                 DT_0.33                  lazyeval_0.2.2           rhdf5filters_1.20.0      GetoptLong_1.0.5        
 [17] urlchecker_1.0.1         withr_3.0.2              prettyunits_1.2.0        GGally_2.2.1             gridExtra_2.3            textshaping_1.0.1        factoextra_1.0.7         cli_3.6.5               
 [25] shinyjs_2.1.0            ggbio_1.56.0             labeling_0.4.3           sass_0.4.10              askpass_1.2.1            systemfonts_1.2.3        Rsamtools_2.24.0         txdbmaker_1.4.1         
 [33] foreign_0.8-86           dbscan_1.2.2             R.utils_2.13.0           stringdist_0.9.15        dichromat_2.0-0.1        sessioninfo_1.2.3        styler_1.10.3            BSgenome_1.76.0         
 [41] rstudioapi_0.17.1        RSQLite_2.3.11           shape_1.4.6.1            BiocIO_1.18.0            crosstalk_1.2.1          car_3.1-3                dplyr_1.1.4              Matrix_1.6-5            
 [49] waldo_0.6.1              abind_1.4-8              R.methodsS3_1.8.2        lifecycle_1.0.4          yaml_2.3.10              carData_3.0-5            biocViews_1.76.0         SparseArray_1.8.0       
 [57] BiocFileCache_2.16.0     grid_4.5.0               blob_1.2.4               promises_1.3.2           crayon_1.5.3             miniUI_0.1.2             lattice_0.22-5           GenomicFeatures_1.60.0  
 [65] KEGGREST_1.48.0          sys_3.4.3                pillar_1.11.0            knitr_1.50               ComplexHeatmap_2.24.0    rjson_0.2.23             codetools_0.2-19         glue_1.8.0              
 [73] data.table_1.17.2        remotes_2.5.0            vctrs_0.6.5              png_0.1-8                testthat_3.2.3           gtable_0.3.6             cachem_1.1.0             xfun_0.52               
 [81] S4Arrays_1.8.0           mime_0.13                iterators_1.0.14         ellipsis_0.3.2           usethis_3.1.0            bit64_4.6.0-1            progress_1.2.3           filelock_1.0.3          
 [89] rprojroot_2.0.4          R.cache_0.17.0           bslib_0.9.0              rpart_4.1.23             colorspace_2.1-1         DBI_1.2.3                Hmisc_5.2-3              shinycustomloader_0.9.0 
 [97] nnet_7.3-19              tidyselect_1.2.1         processx_3.8.6           waiter_0.2.5             bit_4.6.0                compiler_4.5.0           curl_6.2.2               httr2_1.1.2             
[105] graph_1.86.0             BiocCheck_1.44.2         htmlTable_2.4.3          xml2_1.3.8               plotly_4.10.4            desc_1.4.3               DelayedArray_0.34.1      rtracklayer_1.68.0      
[113] checkmate_2.3.2          scales_1.4.0             RBGL_1.84.0              callr_3.7.6              rappdirs_0.3.3           stringr_1.5.1            digest_0.6.37            shinyBS_0.61.1          
[121] rmarkdown_2.29           XVector_0.48.0           htmltools_0.5.8.1        pkgconfig_2.0.3          base64enc_0.1-3          dbplyr_2.5.0             fastmap_1.2.0            ensembldb_2.32.0        
[129] rlang_1.1.6              GlobalOptions_0.1.2      htmlwidgets_1.6.4        UCSC.utils_1.4.0         farver_2.1.2             jquerylib_0.1.4          jsonlite_2.0.0           BiocParallel_1.42.0     
[137] R.oo_1.27.1              VariantAnnotation_1.54.1 RCurl_1.98-1.17          magrittr_2.0.3           Formula_1.2-5            GenomeInfoDbData_1.2.14  credentials_2.0.2        Rhdf5lib_1.30.0         
[145] Rcpp_1.0.14              shinycssloaders_1.1.0    stringi_1.8.7            brio_1.1.5               MASS_7.3-60.0.1          org.Hs.eg.db_3.21.0      plyr_1.8.9               pkgbuild_1.4.7          
[153] ggstats_0.9.0            ggrepel_0.9.6            parallel_4.5.0           Biostrings_2.76.0        hms_1.1.3                circlize_0.4.16          ps_1.9.1                 igraph_2.1.4            
[161] ggpubr_0.6.0             RUnit_0.4.33.1           markdown_2.0             ggsignif_0.6.4           reshape2_1.4.4           biomaRt_2.64.0           pkgload_1.4.0            XML_3.99-0.18           
[169] evaluate_1.0.3           biovizBase_1.56.0        BiocManager_1.30.25      foreach_1.5.2            httpuv_1.6.16            RANN_2.6.2               tidyr_1.3.1              openssl_2.3.2           
[177] purrr_1.0.4              clue_0.3-66              ggplot2_3.5.2            BiocBaseUtils_1.10.0     broom_1.0.8              xtable_1.8-4             restfulr_0.0.15          AnnotationFilter_1.32.0 
[185] roxygen2_7.3.2           rstatix_0.7.2            later_1.4.2              ragg_1.4.0               viridisLite_0.4.2        gert_2.1.5               OrganismDbi_1.50.0       tibble_3.2.1            
[193] memoise_2.0.1            AnnotationDbi_1.70.0     GenomicAlignments_1.44.0 cluster_2.1.6            BiocStyle_2.36.0            gson_0.1.0                  ape_5.8