# Getting started with ISAnalytics

# Introduction

ISAnalytics is an R package developed to analyze gene therapy vector insertion sites data identified from genomics next generation sequencing reads for clonal tracking studies.

# Installation and options

`ISAnalytics` can be installed quickly in different ways:

- You can install it via Bioconductor
- You can install it via GitHub using the package `devtools`

There are always 2 versions of the package active:

- `RELEASE` is the latest stable version
- `DEVEL` is the development version, it is the most up-to-date version where all new features are introduced

## Installation from bioconductor

RELEASE version:

    if (!requireNamespace("BiocManager", quietly = TRUE))
        install.packages("BiocManager")
    BiocManager::install("ISAnalytics")

DEVEL version:

    if (!requireNamespace("BiocManager", quietly = TRUE))
        install.packages("BiocManager")

    # The following initializes usage of Bioc devel
    BiocManager::install(version='devel')

    BiocManager::install("ISAnalytics")

## Installation from GitHub

RELEASE:

    if (!require(devtools)) {
      install.packages("devtools")
    }
    devtools::install_github("calabrialab/ISAnalytics",
                             ref = "RELEASE_3_21",
                             dependencies = TRUE,
                             build_vignettes = TRUE)

DEVEL:

    if (!require(devtools)) {
      install.packages("devtools")
    }
    devtools::install_github("calabrialab/ISAnalytics",
                             ref = "devel",
                             dependencies = TRUE,
                             build_vignettes = TRUE)

## Setting options

`ISAnalytics` has a verbose option that allows some functions to print additional information to the console while they’re executing. To disable this feature do:

    # DISABLE
    options("ISAnalytics.verbose" = FALSE)

    # ENABLE
    options("ISAnalytics.verbose" = TRUE)

Some functions also produce report in a user-friendly HTML format, to set this feature:

    # DISABLE HTML REPORTS
    options("ISAnalytics.reports" = FALSE)

    # ENABLE HTML REPORTS
    options("ISAnalytics.reports" = TRUE)

# Setting up the workflow

In the newer version of ISAnalytics, we introduced a “dynamic variables system”, to allow more flexibility in terms of input formats. Before starting with the analysis workflow, you can specify how your inputs are structured so that the package can process them. For more information on how to do this take a look at `vignette("workflow_start", package = "ISAnalytics")`.

# The first steps

The first steps of the analysis workflow involve the import and parsing of data and metadata files from disk.

- Import metadata with `import_association_file()` and/or `import_Vispa2_stats()`
- Import data with `import_single_Vispa2Matrix()` or `import_parallel_Vispa2Matrices()`

Refer to the vignette `vignette("workflow_start", package = "ISAnalytics")` for more details.

# Data cleaning and pre-processing

ISAnalytics offers several different functions for cleaning and pre-processing your data.

- Recalibration: identifies integration events that are near to each other and condenses them into a single event whenever appropriate - `compute_near_integrations()`
- Outliers identification and removal: identifies samples that are considered outliers according to user-defined logic and filters them out - `outlier_filter()`
- Collision removal: identifies collision events between independent samples - `remove_collisions()`, see also the dedicated vignette `vignette("workflow_start", package = "ISAnalytics")`
- Filter based on cell lineage purity: identifies and removes contamination between different cell types - `purity_filter()`
- Data and metadata aggregation: allows the union of biological samples from single pcr replicates or other arbitrary aggregations - `aggregate_values_by_key()`, `aggregate_metadata()`, see also the dedicated vignette `vignette("workflow_start", package = "ISAnalytics")`

# Answering biological questions

You can answer very different biological questions by using the provided functions with appropriate inputs.

- Descriptive statistics: `sample_statistics()`
- IS relative abundance: `compute_abundance()`, `integration_alluvial_plot()`
- Top abundant IS: `top_integrations()`
- Top targeted genes: `top_targeted_genes()`
- Grubbs test for common insertion sites (CIS): `CIS_grubbs()`, `CIS_volcano_plot()`
- Fisher’s exact test for gene frequency and IS distribution on target genome: `gene_frequency_fisher()`, `fisher_scatterplot()`, `circos_genomic_density()`
- Clonal sharing analyses: `is_sharing()`, `iss_source()`, `sharing_heatmap()`, `sharing_venn()`
- Estimate HSPCs population size: `HSC_population_size_estimate()`, `HSC_population_plot()`

For more, please refer to the full function reference.

# Working with other kinds of data

ISAnalytics is designed to be flexible concerning input formats, thus it is suited to process various kinds of data provided the correct dynamic configuration is set.

We demonstrate this with an example that uses barcodes data. The matrix is publicly available here (Ferrari Samuele Jacob Aurelien, 2020), metadata was provided to us by the authors and it is available in the package additional files.

    library(ISAnalytics)

    # Set appropriate data and metadata specs ----
    metadata_specs <- tibble::tribble(
        ~names, ~types, ~transform, ~flag, ~tag,
        "ProjectID", "char", NULL, "required", "project_id",
        "SubjectID", "char", NULL, "required", "subject",
        "Tissue", "char", NULL, "required", "tissue",
        "TimePoint", "int", NULL, "required", "tp_days",
        "CellMarker", "char", NULL, "required", "cell_marker",
        "ID", "char", NULL, "required", "pcr_repl_id",
        "SourceFileName", "char", NULL, "optional", NA_character_,
        "Link", "char", NULL, "optional", NA_character_
    )
    set_af_columns_def(metadata_specs)
    #> Warning: Warning: important tags missing
    #> ℹ Some tags are required for proper execution of some functions. If these tags are not provided, execution of dependent functions might fail. Review your inputs carefully.
    #> ℹ Missing tags: pool_id, fusion_id, tag_seq, vector_id, tag_id, pcr_replicate, vispa_concatenate, proj_folder
    #> ℹ To see where these are involved type `inspect_tags(c('pool_id','fusion_id','tag_seq','vector_id','tag_id','pcr_replicate','vispa_concatenate','proj_folder'))`
    #> Association file columns specs successfully changed

    mandatory_specs <- tibble::tribble(
        ~names, ~types, ~transform, ~flag, ~tag,
        "BarcodeSeq", "char", NULL, "required", NA_character_
    )
    set_mandatory_IS_vars(mandatory_specs)
    #> Warning: Warning: important tags missing
    #> ℹ Some tags are required for proper execution of some functions. If these tags are not provided, execution of dependent functions might fail. Review your inputs carefully.
    #> ℹ Missing tags: chromosome, locus, is_strand
    #> ℹ To see where these are involved type `inspect_tags(c('chromosome','locus','is_strand'))`
    #> Mandatory IS vars successfully changed

    # Files ----
    data_folder <- tempdir()
    utils::unzip(zipfile = system.file("testdata",
                                       "testdata.zip",
                                       package = "ISAnalytics"),
                 exdir = data_folder, overwrite = TRUE)
    meta_file <- "barcodes_example_af.tsv.xz"
    matrix_file <- "GSE144340_Matrix_542.tsv.xz"

    # Data import ----
    af <- import_association_file(fs::path(data_folder, meta_file),
        report_path = NULL
    )
    af
    #> # A tibble: 15 × 10
    #>    ProjectID    SubjectID Tissue TimePoint CellMarker ID    SourceFileName Link
    #>    <chr>        <chr>     <chr>      <int> <chr>      <chr> <chr>          <chr>
    #>  1 PMID32601433 A0        BM            21 Whole      BM_A0 GSE144340_Mat… http…
    #>  2 PMID32601433 A0        PB            21 Whole      PB21… GSE144340_Mat… http…
    #>  3 PMID32601433 A1        BM            21 Whole      BM_A1 GSE144340_Mat… http…
    #>  4 PMID32601433 A1        PB            21 Whole      PB21… GSE144340_Mat… http…
    #>  5 PMID32601433 A2        PB            21 Whole      PB21… GSE144340_Mat… http…
    #>  6 PMID32601433 A3        PB            21 Whole      PB21… GSE144340_Mat… http…
    #>  7 PMID32601433 A4        BM            21 Whole      BM_A4 GSE144340_Mat… http…
    #>  8 PMID32601433 A4        PB            21 Whole      PB21… GSE144340_Mat… http…
    #>  9 PMID32601433 C0        PB            21 Whole      PB21… GSE144340_Mat… http…
    #> 10 PMID32601433 C1        BM            21 Whole      BM_C1 GSE144340_Mat… http…
    #> 11 PMID32601433 C1        PB            21 Whole      PB21… GSE144340_Mat… http…
    #> 12 PMID32601433 C2        BM            21 Whole      BM_C2 GSE144340_Mat… http…
    #> 13 PMID32601433 C2        PB            21 Whole      PB21… GSE144340_Mat… http…
    #> 14 PMID32601433 C3        BM            21 Whole      BM_C3 GSE144340_Mat… http…
    #> 15 PMID32601433 C3        PB            21 Whole      PB21… GSE144340_Mat… http…
    #> # ℹ 2 more variables: TimepointMonths <chr>, TimepointYears <chr>

    matrix <- import_single_Vispa2Matrix(fs::path(data_folder, matrix_file),
        sample_names_to = "ID"
    )
    #> Warning: compression format not supported by fread
    #> ℹ File will be read using readr
    #> Reading file...
    #> ℹ Mode: classic
    #> Reshaping...
    #> *** File info ***
    #> • --- Annotated: FALSE
    #> • --- Dimensions: 31757 x 16
    #> • --- Read mode: classic
    #> • --- Sample count: 15
    matrix
    #> # A tibble: 33,776 × 3
    #>    BarcodeSeq             ID      Value
    #>    <chr>                  <chr>   <dbl>
    #>  1 AAAAAAAACACGGAGAACGACG PB21_C3     2
    #>  2 AAAAAAAACGCGAACAACTACG PB21_C3     1
    #>  3 AAAAAAAACTCAAAAAAGAAAT BM_C3       1
    #>  4 AAAAAAAATTTACACAAAGAAA BM_A4       1
    #>  5 AAAAAAAATTTTTAAACGTACC BM_A0       1
    #>  6 AAAAAACATATCTATAGTTACC BM_A0       1
    #>  7 AAAAAAGACGACGATAGGCACG PB21_C1     1
    #>  8 AAAAAAGACGTTTATAGGTGTA PB21_A2     1
    #>  9 AAAAAAGACTGCGACAAAAGGG BM_A4       1
    #> 10 AAAAAAGACTTTGATAACCACG PB21_C3     1
    #> # ℹ 33,766 more rows

    # Descriptive stats ----
    desc_stats <- sample_statistics(matrix, af,
        sample_key = pcr_id_column(),
        value_columns = "Value"
    )$metadata |>
        dplyr::rename(distinct_barcodes = "nIS")
    desc_stats
    #> # A tibble: 15 × 29
    #>    ProjectID    SubjectID Tissue TimePoint CellMarker ID    SourceFileName Link
    #>    <chr>        <chr>     <chr>      <int> <chr>      <chr> <chr>          <chr>
    #>  1 PMID32601433 A0        BM            21 Whole      BM_A0 GSE144340_Mat… http…
    #>  2 PMID32601433 A0        PB            21 Whole      PB21… GSE144340_Mat… http…
    #>  3 PMID32601433 A1        BM            21 Whole      BM_A1 GSE144340_Mat… http…
    #>  4 PMID32601433 A1        PB            21 Whole      PB21… GSE144340_Mat… http…
    #>  5 PMID32601433 A2        PB            21 Whole      PB21… GSE144340_Mat… http…
    #>  6 PMID32601433 A3        PB            21 Whole      PB21… GSE144340_Mat… http…
    #>  7 PMID32601433 A4        BM            21 Whole      BM_A4 GSE144340_Mat… http…
    #>  8 PMID32601433 A4        PB            21 Whole      PB21… GSE144340_Mat… http…
    #>  9 PMID32601433 C0        PB            21 Whole      PB21… GSE144340_Mat… http…
    #> 10 PMID32601433 C1        BM            21 Whole      BM_C1 GSE144340_Mat… http…
    #> 11 PMID32601433 C1        PB            21 Whole      PB21… GSE144340_Mat… http…
    #> 12 PMID32601433 C2        BM            21 Whole      BM_C2 GSE144340_Mat… http…
    #> 13 PMID32601433 C2        PB            21 Whole      PB21… GSE144340_Mat… http…
    #> 14 PMID32601433 C3        BM            21 Whole      BM_C3 GSE144340_Mat… http…
    #> 15 PMID32601433 C3        PB            21 Whole      PB21… GSE144340_Mat… http…
    #> # ℹ 21 more variables: TimepointMonths <chr>, TimepointYears <chr>,
    #> #   Value_sum <dbl>, Value_count <int>, Value_shannon <dbl>,
    #> #   Value_simpson <dbl>, Value_invsimpson <dbl>, Value_describe_vars <dbl>,
    #> #   Value_describe_n <dbl>, Value_describe_mean <dbl>, Value_describe_sd <dbl>,
    #> #   Value_describe_median <dbl>, Value_describe_trimmed <dbl>,
    #> #   Value_describe_mad <dbl>, Value_describe_min <dbl>,
    #> #   Value_describe_max <dbl>, Value_describe_range <dbl>, …

    # Aggregation and new stats ----
    agg_key <- c("SubjectID")
    agg <- aggregate_values_by_key(matrix, af,
        key = agg_key,
        group = "BarcodeSeq",
        join_af_by = pcr_id_column()
    )
    agg
    #> # A tibble: 33,267 × 3
    #>    BarcodeSeq             SubjectID Value_sum
    #>    <chr>                  <chr>         <dbl>
    #>  1 AAAAAAAACACGGAGAACGACG C3                2
    #>  2 AAAAAAAACGCGAACAACTACG C3                1
    #>  3 AAAAAAAACTCAAAAAAGAAAT C3                1
    #>  4 AAAAAAAATTTACACAAAGAAA A4                1
    #>  5 AAAAAAAATTTTTAAACGTACC A0                1
    #>  6 AAAAAACATATCTATAGTTACC A0                1
    #>  7 AAAAAAGACGACGATAGGCACG C1                1
    #>  8 AAAAAAGACGTTTATAGGTGTA A2                1
    #>  9 AAAAAAGACTGCGACAAAAGGG A4                1
    #> 10 AAAAAAGACTTTGATAACCACG C3                1
    #> # ℹ 33,257 more rows

    agg_meta_functions <- tibble::tribble(
        ~Column, ~Function, ~Args, ~Output_colname,
        "TimePoint", ~ mean(.x, na.rm = TRUE), NA, "{.col}_avg",
        "CellMarker", ~ length(unique(.x)), NA, "distinct_cell_marker_count",
        "ID", ~ length(unique(.x)), NA, "distinct_id_count"
    )
    agg_meta <- aggregate_metadata(
        af,
        aggregating_functions = agg_meta_functions,
        grouping_keys = agg_key
    )
    agg_meta
    #> # A tibble: 9 × 4
    #>   SubjectID TimePoint_avg distinct_cell_marker_count distinct_id_count
    #>   <chr>             <dbl>                      <int>             <int>
    #> 1 A0                   21                          1                 2
    #> 2 A1                   21                          1                 2
    #> 3 A2                   21                          1                 1
    #> 4 A3                   21                          1                 1
    #> 5 A4                   21                          1                 2
    #> 6 C0                   21                          1                 1
    #> 7 C1                   21                          1                 2
    #> 8 C2                   21                          1                 2
    #> 9 C3                   21                          1                 2

    agg_stats <- sample_statistics(agg, agg_meta,
        sample_key = agg_key,
        value_columns = "Value_sum"
    )$metadata |>
        dplyr::rename(distinct_barcodes = "nIS")
    agg_stats
    #> # A tibble: 9 × 23
    #>   SubjectID TimePoint_avg distinct_cell_marker…¹ distinct_id_count Value_sum_sum
    #>   <chr>             <dbl>                  <int>             <int>         <dbl>
    #> 1 A0                   21                      1                 2        326467
    #> 2 A1                   21                      1                 2        378987
    #> 3 A2                   21                      1                 1        124676
    #> 4 A3                   21                      1                 1        180497
    #> 5 A4                   21                      1                 2        473256
    #> 6 C0                   21                      1                 1         59966
    #> 7 C1                   21                      1                 2        399316
    #> 8 C2                   21                      1                 2        492590
    #> 9 C3                   21                      1                 2        341166
    #> # ℹ abbreviated name: ¹​distinct_cell_marker_count
    #> # ℹ 18 more variables: Value_sum_count <int>, Value_sum_shannon <dbl>,
    #> #   Value_sum_simpson <dbl>, Value_sum_invsimpson <dbl>,
    #> #   Value_sum_describe_vars <dbl>, Value_sum_describe_n <dbl>,
    #> #   Value_sum_describe_mean <dbl>, Value_sum_describe_sd <dbl>,
    #> #   Value_sum_describe_median <dbl>, Value_sum_describe_trimmed <dbl>,
    #> #   Value_sum_describe_mad <dbl>, Value_sum_describe_min <dbl>, …

    # Abundance ----
    abundance <- compute_abundance(agg, columns = "Value_sum", key = agg_key)
    abundance
    #> # A tibble: 33,267 × 5
    #>    BarcodeSeq  SubjectID Value_sum Value_sum_RelAbundance Value_sum_PercAbunda…¹
    #>    <chr>       <chr>         <dbl>                  <dbl>                  <dbl>
    #>  1 AAAAAAAACA… C3                2             0.00000586               0.000586
    #>  2 AAAAAAAACG… C3                1             0.00000293               0.000293
    #>  3 AAAAAAAACT… C3                1             0.00000293               0.000293
    #>  4 AAAAAAAATT… A4                1             0.00000211               0.000211
    #>  5 AAAAAAAATT… A0                1             0.00000306               0.000306
    #>  6 AAAAAACATA… A0                1             0.00000306               0.000306
    #>  7 AAAAAAGACG… C1                1             0.00000250               0.000250
    #>  8 AAAAAAGACG… A2                1             0.00000802               0.000802
    #>  9 AAAAAAGACT… A4                1             0.00000211               0.000211
    #> 10 AAAAAAGACT… C3                1             0.00000293               0.000293
    #> # ℹ 33,257 more rows
    #> # ℹ abbreviated name: ¹​Value_sum_PercAbundance

    reset_dyn_vars_config()
    #> Mandatory IS vars reset to default
    #> Annotation IS vars reset to default
    #> Association file columns specs reset to default
    #> ISS stats specs reset to default
    #> Matrix suffixes specs reset to default

# Using the Shiny interface

The package provides a simple Shiny interface for data exploration and plotting. To start the interface use:

    NGSdataExplorer()

The application main page will show a loading screen for a file. It is possible to load files also from the R environment, for example, before opening the app, we can load the included association file:

    data("association_file")

Once in the application we can choose `"association_file"` from the R environment loading option screen and click on “Import data”. Once data is imported, we can click on the “Explore” tab in the upper navbar: here we will see 2 tabs, one allows interactive exploration of data in tabular form, in the other tab we can plot data. It is possible to customize several different parameters for the plot and finally save it to file with the dedicated button at the end of the page.

The Shiny interface is still currently under active development and new features will be added in the near future.

# Ensuring reproducibility of results

Several implemented functions produce static HTML reports that can be saved on disk, or tabular files. Reports contain the relevant information on how the function was called, inputs and outputs statistics, and session info for reproducibility.

# Browse documentation online and keep updated

ISAnalytics has it’s dedicated package website where you can browse the documentation and vignettes easily, in addition to keeping up to date with all relevant updates. Visit the website at https://calabrialab.github.io/ISAnalytics/

# Problems?

If you have any issues the documentation can’t solve, get in touch by opening an issue on GitHub or contacting the maintainers

# Reproducibility

The following session information was included to ensure reproducibility of results and environment tracking, as recommended by Bioconductor guidelines.

    #> R version 4.6.1 (2026-06-24)
    #> Platform: x86_64-pc-linux-gnu
    #> Running under: Ubuntu 26.04 LTS
    #>
    #> Matrix products: default
    #> BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
    #> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.32.so;  LAPACK version 3.12.0
    #>
    #> locale:
    #>  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C
    #>  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8
    #>  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8
    #>  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C
    #>  [9] LC_ADDRESS=C               LC_TELEPHONE=C
    #> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
    #>
    #> time zone: Etc/UTC
    #> tzcode source: system (glibc)
    #>
    #> attached base packages:
    #> [1] stats     graphics  grDevices utils     datasets  methods   base
    #>
    #> other attached packages:
    #> [1] ISAnalytics_1.23.1 RefManageR_1.4.0   BiocStyle_2.41.0
    #>
    #> loaded via a namespace (and not attached):
    #>   [1] mnormt_2.1.2        writexl_1.5.4       permute_0.9-10
    #>   [4] readxl_1.5.0        datamods_1.5.3      rlang_1.3.0
    #>   [7] magrittr_2.0.5      rio_1.3.0           otel_0.2.0
    #>  [10] e1071_1.7-17        compiler_4.6.1      mgcv_1.9-4
    #>  [13] vctrs_0.7.3         stringr_1.6.0       pkgconfig_2.0.3
    #>  [16] crayon_1.5.3        fastmap_1.2.0       backports_1.5.1
    #>  [19] utf8_1.2.6          promises_1.5.0      phosphoricons_0.2.1
    #>  [22] rmarkdown_2.31      tzdb_0.5.0          purrr_1.2.2
    #>  [25] bit_4.6.0           xfun_0.60           cachem_1.1.0
    #>  [28] jsonlite_2.0.0      later_1.4.8         BiocParallel_1.47.0
    #>  [31] psych_2.6.5         parallel_4.6.1      cluster_2.1.8.2
    #>  [34] R6_2.6.1            bslib_0.11.0        stringi_1.8.7
    #>  [37] RColorBrewer_1.1-3  shinybusy_0.3.3     parallelly_1.48.0
    #>  [40] lubridate_1.9.5     jquerylib_0.1.4     cellranger_1.1.0
    #>  [43] Rcpp_1.1.2          iterators_1.0.14    knitr_1.51
    #>  [46] future.apply_1.20.2 readr_2.2.0         Matrix_1.7-5
    #>  [49] splines_4.6.1       httpuv_1.6.17       timechange_0.4.0
    #>  [52] tidyselect_1.2.1    yaml_2.3.12         vegan_2.7-5
    #>  [55] codetools_0.2-20    listenv_1.0.0       lattice_0.22-9
    #>  [58] tibble_3.3.1        plyr_1.8.9          shiny_1.14.0
    #>  [61] withr_3.0.3         S7_0.2.2            evaluate_1.0.5
    #>  [64] future_1.70.0       proxy_0.4-29        xml2_1.6.0
    #>  [67] pillar_1.11.1       BiocManager_1.30.27 KernSmooth_2.23-26
    #>  [70] foreach_1.5.2       generics_0.1.4      vroom_1.7.1
    #>  [73] hms_1.1.4           ggplot2_4.0.3       scales_1.4.0
    #>  [76] globals_0.19.1      xtable_1.8-8        class_7.3-23
    #>  [79] glue_1.8.1          toastui_0.4.0       maketools_1.3.2
    #>  [82] tools_4.6.1         sys_3.4.3           data.table_1.18.4
    #>  [85] reactable_0.4.5     buildtools_1.0.0    fs_2.1.0
    #>  [88] grid_4.6.1          tidyr_1.3.2         bibtex_0.5.2
    #>  [91] nlme_3.1-170        cli_3.6.6           doFuture_1.2.2
    #>  [94] dplyr_1.2.1         gtable_0.3.6        sass_0.4.10
    #>  [97] digest_0.6.39       classInt_0.4-11     htmlwidgets_1.6.4
    #> [100] farver_2.1.2        htmltools_0.5.9     lifecycle_1.0.5
    #> [103] httr_1.4.8          shinyWidgets_0.9.1  mime_0.13
    #> [106] bit64_4.8.2         MASS_7.3-66

# Bibliography

[1] B. S. Ferrari Samuele Jacob Aurelien. “Efficient gene editing of human long-term hematopoietic stem cells validated by clonal tracking”. In: Nat Biotechnol 38, 1298–1308 (Nov. 2020). DOI: https://doi.org/10.1038/s41587-020-0551-y.