# 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 1. 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` 2. 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` 3. 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 ```