# Setting up the workflow and first steps

# 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.

In this vignette we will explain how to properly setup the workflow and the first steps of data import and data cleaning.

# Setting up your workflow with dynamic vars

This section demonstrates how to properly setup your workflow with `ISAnalytics` using the “dynamic vars” system.

From `ISAnalytics 1.5.4` onwards, a new system here referred to as “dynamic vars” has been implemented to improve the flexibility of the package, by allowing multiple input formats based on user needs rather than enforcing hard-coded names and structures. In this way, users that do not follow the standard name conventions used by the package have to put minimal effort into making their inputs compliant to the package requirements.

There are 5 main categories of inputs you can customize:

- The “mandatory IS vars”: this set of variables is used to uniquely identify integration events across several functions implemented in the package
- The “annotation IS vars”: this set of variables holds the names of the columns that contain genomic annotations
- The “association file columns”: this set contains information on how metadata is structured
- The “VISPA2 stats specs”: this set contains information on the format of pool statistics files produced automatically by VISPA2
- The “matrix files suffixes”: this set contains all default file names for each quantification type and it is used by automated import functions

## General approach

The general approach is based on the specification of predefined tags and their associated information in the form of simple data frames with a standard structure, namely:

names | types | transform | flag | tag
--- | --- | --- | --- | ---
`<name of the column>` | `<type>` | `<a lambda or NULL>` | `<flag>` | `<tag>`

where

- `names` contains the name of the column as a character
- `types` contains the type of the column. Type should be expressed as a string and should be in one of the allowed types
  - `char` for character (strings)
  - `int` for integers
  - `logi` for logical values (TRUE / FALSE)
  - `numeric` for numeric values
  - `factor` for factors
  - `date` for generic date format - note that functions that need to read and parse files will try to guess the format and parsing may fail
  - One of the accepted date/datetime formats by lubridate, you can use `ISAnalytics::date_formats()` to view the accepted formats
- `transform`: a purrr-style lambda that is applied immediately after importing. This is useful to operate simple transformations like removing unwanted characters or rounding to a certain precision. Please note that these lambdas need to be functions that accept a vector as input and only operate a transformation, aka they output a vector of the same length as the input. For more complicated applications that may require the value of other columns, appropriate functions should be manually applied post-import.
- `flag`: as of now, it should be set either to `required` or `optional` - some functions internally check for only required tags presence and if those are missing from inputs they fail, signaling failure to the user
- `tag`: a specific tag expressed as a string - see Section @ref(tags)

Dynamic variables general approach

## Tags

As already mentioned, certain functions included in the package require the presence of specific tags (and associated column names) in the input to work properly. You can always check what a tag means and where it is used by using the function `inspect_tags()` and providing in input the tags you want to check as a character vector.

    inspect_tags("chromosome")
    #> * TAG: chromosome
    #> ℹ Description: Number of the chromosome
    #> ℹ Functions that use it: top_targeted_genes, CIS_grubbs, compute_near_integrations

You should make sure the tag matches the right information in your inputs by looking at the description of the tag. It is also possible to add entries that are not associated with any tag. Here is an overview of the critical tags for each category.

### Mandatory IS vars

The presence of all mandatory IS vars is also checked and used in other functions - for example, when importing matrices it is ensured that all mandatory variables are present in the input, as declared in the look up table. Some functions may require information that needs to be specified as input, always check the documentation if you have doubts.

### Annotation IS vars

Although genomic annotations are not necessarily required to work with ISAnalytics, some operations do require annotation - if you’re working with matrices that are not annotated you can either annotate them with a tool of your choice or skip the steps that require annotation.

### Association file columns

Some tags in this table are not associated with any function yet, but they exist for potential new features that will be added in the future.

### VISPA2 stats specs

NOTE: VISPA2 stats files usually follow standard naming conventions. If the pipeline launch was configured with default parameters, do not change this lookup table.

## Customizing dynamic vars

For each category of dynamic vars there are 3 functions:

- A getter - returns the current lookup table
- A setter - allows the user to change the current lookup table
- A resetter - reverts all changes to defaults

Setters will take in input the new variables, validate and eventually change the lookup table. If validation fails an error will be thrown instead, inviting the user to review the inputs. Moreover, if some of the critical tags for the category are missing, a warning appears, with a list of the missing ones.

Let’s take a look at some examples.

On package loading, all lookup tables are set to default values. For example, for mandatory IS vars we have:

    mandatory_IS_vars(TRUE)
    #> # A tibble: 3 × 5
    #>   names             types transform flag     tag
    #>   <chr>             <chr> <list>    <chr>    <chr>
    #> 1 chr               char  <NULL>    required chromosome
    #> 2 integration_locus int   <NULL>    required locus
    #> 3 strand            char  <NULL>    required is_strand

Let’s suppose our matrices follow a different standard, and integration events are characterized by 5 fields, like so (the example contains random data):

chrom | position | strand | gap | junction
--- | --- | --- | --- | ---
“chr1” | 342543 | “+” | 100 | 50
… | … | … | … | …

To make this work with ISAnalytics functions, we need to compile the lookup table like this:

    new_mand_vars <- tibble::tribble(
        ~names, ~types, ~transform, ~flag, ~tag,
        "chrom", "char", ~ stringr::str_replace_all(.x, "chr", ""), "required",
        "chromosome",
        "position", "int", NULL, "required", "locus",
        "strand", "char", NULL, "required", "is_strand",
        "gap", "int", NULL, "required", NA_character_,
        "junction", "int", NULL, "required", NA_character_
    )

Notice that we have specified a transformation for the “chromosome” tag: in this case we would like to have only the number of the chromosome without the prefix “chr” - this lambda will get executed immediately after import.

To set the new variables simply do:

    set_mandatory_IS_vars(new_mand_vars)
    #> Mandatory IS vars successfully changed
    mandatory_IS_vars(TRUE)
    #> # A tibble: 5 × 5
    #>   names    types transform flag     tag
    #>   <chr>    <chr> <list>    <chr>    <chr>
    #> 1 chrom    char  <formula> required chromosome
    #> 2 position int   <NULL>    required locus
    #> 3 strand   char  <NULL>    required is_strand
    #> 4 gap      int   <NULL>    required <NA>
    #> 5 junction int   <NULL>    required <NA>

If you don’t specify a critical tag, a warning message is displayed:

    new_mand_vars[1, ]$tag <- NA_character_
    set_mandatory_IS_vars(new_mand_vars)
    #> 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
    #> ℹ To see where these are involved type `inspect_tags(c('chromosome'))`
    #> Mandatory IS vars successfully changed
    mandatory_IS_vars(TRUE)
    #> # A tibble: 5 × 5
    #>   names    types transform flag     tag
    #>   <chr>    <chr> <list>    <chr>    <chr>
    #> 1 chrom    char  <formula> required <NA>
    #> 2 position int   <NULL>    required locus
    #> 3 strand   char  <NULL>    required is_strand
    #> 4 gap      int   <NULL>    required <NA>
    #> 5 junction int   <NULL>    required <NA>

If you change your mind and want to go back to defaults:

    reset_mandatory_IS_vars()
    #> Mandatory IS vars reset to default
    mandatory_IS_vars(TRUE)
    #> # A tibble: 3 × 5
    #>   names             types transform flag     tag
    #>   <chr>             <chr> <list>    <chr>    <chr>
    #> 1 chr               char  <NULL>    required chromosome
    #> 2 integration_locus int   <NULL>    required locus
    #> 3 strand            char  <NULL>    required is_strand

The principle is the same for annotation IS vars, association file columns and VISPA2 stats specs. Here is a summary of the functions for each:

- mandatory IS vars: `mandatory_IS_vars()`, `set_mandatory_IS_vars()`, `reset_mandatory_IS_vars()`
- annotation IS vars: `annotation_IS_vars()`, `set_annotation_IS_vars()`, `reset_annotation_IS_vars()`
- association file columns: `association_file_columns()`, `set_af_columns_def()`, `reset_af_columns_def()`
- VISPA2 stats specs: `iss_stats_specs()`, `set_iss_stats_specs()`, `reset_iss_stats_specs`

Matrix files suffixes work slightly different:

    matrix_file_suffixes()
    #> # A tibble: 10 × 3
    #>    quantification   matrix_type   file_suffix
    #>    <chr>            <chr>         <chr>
    #>  1 seqCount         annotated     seqCount_matrix.no0.annotated.tsv.gz
    #>  2 seqCount         not_annotated seqCount_matrix.tsv.gz
    #>  3 fragmentEstimate annotated     fragmentEstimate_matrix.no0.annotated.tsv.gz
    #>  4 fragmentEstimate not_annotated fragmentEstimate_matrix.tsv.gz
    #>  5 barcodeCount     annotated     barcodeCount_matrix.no0.annotated.tsv.gz
    #>  6 barcodeCount     not_annotated barcodeCount_matrix.tsv.gz
    #>  7 cellCount        annotated     cellCount_matrix.no0.annotated.tsv.gz
    #>  8 cellCount        not_annotated cellCount_matrix.tsv.gz
    #>  9 ShsCount         annotated     ShsCount_matrix.no0.annotated.tsv.gz
    #> 10 ShsCount         not_annotated ShsCount_matrix.tsv.gz

To change this lookup table use the function `set_matrix_file_suffixes()`: the function will ask to specify a suffix for each quantification and for both annotated and not annotated versions. These suffixes are used in the automated matrix import function when scanning the file system.

To reset all lookup tables to their default configurations you can also use the function `reset_dyn_vars_config()`, which reverts all changes.

## FAQs

### Do I have to do this every time the package loads?

No, if you frequently have to work with a non-standard settings profile, you can use the functions `export_ISA_settings()` and `import_ISA_settings()`: these functions allow the import/export of setting profiles in *.json format.

Once you set your variables for the first time through the procedure described before, simply call the export function and all will be saved to a json file, which can then be imported for the next workflow.

# Reporting progress

From `ISAnalytics 1.7.4`, functions that make use of parallel workers or process long tasks report progress via the functions offered by progressr. To enable progress bars for all functions in ISAnalytics do

    enable_progress_bars()

before calling other functions. For customizing the appearance of the progress bar please refer to `progressr` documentation.

# Introduction to `ISAnalytics` import functions family

In this section we’re going to explain more in detail how functions of the import family should be used, the most common workflows to follow and more.

## Designed to work with VISPA2 pipeline

The vast majority of the functions included in this package is designed to work in combination with VISPA2 pipeline (Giulio Spinozzi Andrea Calabria, 2017). If you don’t know what it is, we strongly recommend you to take a look at these links:

- Article: VISPA2: Article
- BitBucket Wiki: VISPA2 Wiki

## File system structure generated

VISPA2 produces a standard file system structure starting from a folder you specify as your workbench or root. The structure always follows this schema:

- root/
  - Optional intermediate folders
    - Projects (PROJECTID)
      - bam
      - bcmuxall
      - bed
      - iss
        - Pools (concatenatePoolIDSeqRun)
      - quality
      - quantification
        - Pools (concatenatePoolIDSeqRun)
      - report

Most of the functions implemented expect a standard file system structure as the one described above.

## Notation

We call an “integration matrix” a tabular structure characterized by:

- k mandatory columns of genomic features that characterize a viral insertion site in the genome, which are specified via `mandatory_IS_vars()`. By default they’re set to `chr`, `integration_locus` and `strand`
- a (optional) annotation columns, provided via `annotation_IS_vars()`. By default they’re set to `GeneName` and `GeneStrand`
- A variable number n of sample columns containing the quantification of the corresponding integration site

    #> # A tibble: 3 × 8
    #>   chr   integration_locus strand GeneName     GeneStrand  exp1  exp2  exp3
    #>   <chr>             <dbl> <chr>  <chr>        <chr>      <dbl> <dbl> <dbl>
    #> 1 1                 12324 +      NFATC3       +           4553  5345    NA
    #> 2 6                657532 +      LOC100507487 +             76   545     5
    #> 3 7                657532 +      EDIL3        -             NA    56    NA

The package uses a more compact form of these matrices, limiting the amount of NA values and optimizing time and memory consumption. For more info on this take a look at: Tidy data

While integration matrices contain the actual data, we also need associated sample metadata to perform the vast majority of the analyses. `ISAnalytics` expects the metadata to be contained in a so called “association file”, which is a simple tabular file.

To generate a blank association file you can use the function `generate_blank_association_file`. You can also view the standard column names with `association_file_columns()`.

## Importing metadata

To import metadata we use `import_association_file()`. This function is not only responsible for reading the file into the R environment as a data frame, but it is capable to perform a file system alignment operation, that is, for each project and pool contained in the file, it scans the file system starting from the provided root to check if the corresponding folders (contained in the appropriate column) can be found. Remember that to work properly, this operation expects a standard folder structure, such as the one provided by VISPA2. This function also produces an interactive HTML report.

    fs_path <- generate_default_folder_structure()
    withr::with_options(list(ISAnalytics.reports = FALSE), code = {
        af <- import_association_file(fs_path$af, root = fs_path$root)
    })
    #> *** Association file import summary ***
    #> ℹ For detailed report please set option 'ISAnalytics.reports' to TRUE
    #> Parsing problems detected: FALSE
    #> Date parsing problems: FALSE
    #> Column problems detected: FALSE
    #> NAs found in important columns: FALSE
    #> File system alignment: no problems detected

    #> # A tibble: 6 × 74
    #>   ProjectID FUSIONID  PoolID TagSequence SubjectID VectorType VectorID
    #>   <chr>     <chr>     <chr>  <chr>       <chr>     <chr>      <chr>
    #> 1 PJ01      ET#382.46 POOL01 LTR75LC38   PT001     lenti      GLOBE
    #> 2 PJ01      ET#381.40 POOL01 LTR53LC32   PT001     lenti      GLOBE
    #> 3 PJ01      ET#381.9  POOL01 LTR83LC66   PT001     lenti      GLOBE
    #> 4 PJ01      ET#381.71 POOL01 LTR27LC94   PT001     lenti      GLOBE
    #> 5 PJ01      ET#381.2  POOL01 LTR69LC52   PT001     lenti      GLOBE
    #> 6 PJ01      ET#382.28 POOL01 LTR37LC2    PT001     lenti      GLOBE
    #> # ℹ 67 more variables: ExperimentID <chr>, Tissue <chr>, TimePoint <chr>,
    #> #   DNAFragmentation <chr>, PCRMethod <chr>, TagIDextended <chr>,
    #> #   Keywords <chr>, CellMarker <chr>, TagID <chr>, NGSProvider <chr>,
    #> #   NGSTechnology <chr>, ConverrtedFilesDir <chr>, ConverrtedFilesName <chr>,
    #> #   SourceFileFolder <chr>, SourceFileNameR1 <chr>, SourceFileNameR2 <chr>,
    #> #   DNAnumber <chr>, ReplicateNumber <int>, DNAextractionDate <date>,
    #> #   DNAngUsed <dbl>, LinearPCRID <chr>, LinearPCRDate <date>, …

### Function arguments

You can change several arguments in the function call to modify the behavior of the function.

- `root`
  - Set it to `NULL` if you only want to import the association file without file system alignment. Beware that some of the automated import functionalities won’t work!
  - Set it to a non-empty string (path on disk): in this case, the column associated with the tag `proj_folder` (by default `PathToFolderProjectID`) in the file should contain relative file paths, so if for example your root is set to “/home” and your project folder in the association file is set to “/PJ01”, the function will check that the directory exists under “/home/PJ01”
  - Set it to an empty string: ideal if you want to store paths in the association file as absolute file paths. In this case if your project folder is in “/home/PJ01” you should have this path in the `PathToFolderProjectID` column and set `root` = “”
- `dates_format`: a string that is useful for properly parsing dates from tabular formats
- `separator`: the column separator used in the file. Defaults to “\t”, other valid separators are “,” (comma), “;” (semi-colon)
- `filter_for`: you can set this argument to a named list of filters, where names are column names. For example `list(ProjectID = "PJ01")` will return only those rows whose attribute “ProjectID” equals “PJ01”
- `import_iss`: either `TRUE` or `FALSE`. If set to `TRUE`, performs an internal call to `import_Vispa2_stats()` (see next section), and appends the imported files to metadata
- `convert_tp`: either `TRUE` or `FALSE`. Converts the column containing the time point expressed in days in months and years (with custom logic).
- `report_path`
  - Set it to `NULL` to avoid the production of a report
  - Set it to a folder (if it doesn’t exist, it gets automatically created)
- `...`: additional named arguments to pass to `import_Vispa2_stats()` if you chose to import VISPA2 stats

For further details view the dedicated function documentation.

NOTE: the function supports files in various formats as long as the correct separator is provided. It also accepts files in `*.xlsx` and `*.xls` formats but we do not recommend using these since the report won’t include a detailed summary of potential parsing problems.

The interactive report includes useful information such as

- General issues: parsing problems, missing columns, NA values in important columns etc. This allows you to immediately spot problems and correct them before proceeding with the analyses
- File system alignment issues: very useful to know if all data can be imported or folders are missing
- Info on VISPA2 stats (if `import_iss` was `TRUE`)

## Importing VISPA2 stats files

VISPA2 automatically produces summary files for each pool holding information that can be useful for other analyses downstream, so it is recommended to import them in the first steps of the workflow. To do that, you can use `import_VISPA2_stats`:

    vispa_stats <- import_Vispa2_stats(
        association_file = af,
        join_with_af = FALSE,
        report_path = NULL
    )

    #> # A tibble: 6 × 14
    #>   POOL     TAG       RUN_NAME     PHIX_MAPPING PLASMID_MAPPED_BYPOOL BARCODE_MUX
    #>   <chr>    <chr>     <chr>               <dbl>                 <dbl>       <dbl>
    #> 1 POOL01-1 LTR75LC38 PJ01|POOL01…     43586699               2256176      645026
    #> 2 POOL01-1 LTR53LC32 PJ01|POOL01…     43586699               2256176      652208
    #> 3 POOL01-1 LTR83LC66 PJ01|POOL01…     43586699               2256176      451519
    #> 4 POOL01-1 LTR27LC94 PJ01|POOL01…     43586699               2256176      426500
    #> 5 POOL01-1 LTR69LC52 PJ01|POOL01…     43586699               2256176       18300
    #> 6 POOL01-1 LTR37LC2  PJ01|POOL01…     43586699               2256176      729327
    #> # ℹ 8 more variables: LTR_IDENTIFIED <dbl>, TRIMMING_FINAL_LTRLC <dbl>,
    #> #   LV_MAPPED <dbl>, BWA_MAPPED_OVERALL <dbl>, ISS_MAPPED_OVERALL <dbl>,
    #> #   RAW_READS <lgl>, QUALITY_PASSED <lgl>, ISS_MAPPED_PP <lgl>

The function requires as input the imported and file system aligned association file and it will scan the `iss` folder for files that match some known prefixes (defaults are already provided but you can change them as you see fit). You can either choose to join the imported data frames with the association file in input and obtain a single data frame or keep it as it is, just set the parameter `join_with_af` accordingly. At the end of the process an HTML report is produced, signaling potential problems.

You can directly call this function when you import the association file by setting the `import_iss` argument of `import_association_file` to `TRUE`.

## Importing a single integration matrix

If you want to import a single integration matrix you can do so by using the `import_single_Vispa2Matrix()` function. This function reads the file and converts it into a tidy structure: several different formats can be read, since you can specify the column separator.

    matrix_path <- fs::path(
        fs_path$root,
        "PJ01",
        "quantification",
        "POOL01-1",
        "PJ01_POOL01-1_seqCount_matrix.no0.annotated.tsv.gz"
    )
    matrix <- import_single_Vispa2Matrix(matrix_path)

    #> # A tibble: 802 × 7
    #>    chr   integration_locus strand GeneName     GeneStrand CompleteAmplificatio…¹
    #>    <chr>             <int> <chr>  <chr>        <chr>      <chr>
    #>  1 16             68164148 +      NFATC3       +          PJ01_POOL01_LTR75LC38…
    #>  2 4             129390130 +      LOC100507487 +          PJ01_POOL01_LTR75LC38…
    #>  3 5              84009671 -      EDIL3        -          PJ01_POOL01_LTR75LC38…
    #>  4 12             54635693 -      CBX5         -          PJ01_POOL01_LTR75LC38…
    #>  5 2             181930711 +      UBE2E3       +          PJ01_POOL01_LTR75LC38…
    #>  6 20             35920986 +      MANBAL       +          PJ01_POOL01_LTR75LC38…
    #>  7 22             26900625 +      TFIP11       -          PJ01_POOL01_LTR75LC38…
    #>  8 3             106580075 +      LINC00882    -          PJ01_POOL01_LTR75LC38…
    #>  9 1              16186297 -      SPEN         +          PJ01_POOL01_LTR75LC38…
    #> 10 17             61712419 +      MAP3K3       +          PJ01_POOL01_LTR75LC38…
    #> # ℹ 792 more rows
    #> # ℹ abbreviated name: ¹​CompleteAmplificationID
    #> # ℹ 1 more variable: Value <int>

For details on usage and arguments view the dedicated function documentation.

## Automated integration matrices import

Integration matrices import can be automated when when the association file is imported with the file system alignment option. `ISAnalytics` provides a function, `import_parallel_Vispa2Matrices()`, that allows to do just that in a fast and efficient way.

    withr::with_options(list(ISAnalytics.reports = FALSE), {
        matrices <- import_parallel_Vispa2Matrices(af,
            c("seqCount", "fragmentEstimate"),
            mode = "AUTO"
        )
    })

## Function arguments

Let’s see how the behavior of the function changes when we change arguments.

### `association_file` argument

You can supply a data frame object, imported via `import_association_file()` (see Section @ref(metadata)) or a string (the path to the association file on disk). In the first scenario it is necessary to perform file system alignment, since the function scans the folders contained in the column `Path_quant`, while in the second case you should also provide as additional named argument (to `...`) an appropriate `root`: the function will internally call `import_association_file()`, if you don’t have specific needs we recommend doing the 2 steps separately and provide the association file as a data frame.

### `quantification_type` argument

For each pool there may be multiple available quantification types, that is, different matrices containing the same samples and same genomic features but a different quantification. A typical workflow contemplates `seqCount` and `fragmentEstimate`, all the supported quantification types can be viewed with `quantification_types()`.

### `matrix_type` argument

As we mentioned in Section @ref(notation), annotation columns are optional and may not be included in some matrices. This argument allows you to specify the function to look for only a specific type of matrix, either `annotated` or `not_annotated`.

File suffixes for matrices are specified via `matrix_file_suffixes()`.

### `workers` argument

Sets the number of parallel workers to set up. This highly depends on the hardware configuration of your machine.

### `multi_quant_matrix` argument

When importing more than one quantification at once, it can be very handy to have all data in a single data frame rather than two. If set to `TRUE` the function will internally call `comparison_matrix()` and produce a single data frames that has a dedicated column for each quantification. For example, for the matrices we’ve imported before:

    #> # A tibble: 6 × 8
    #>   chr   integration_locus strand GeneName     GeneStrand CompleteAmplificationID
    #>   <chr>             <int> <chr>  <chr>        <chr>      <chr>
    #> 1 16             68164148 +      NFATC3       +          PJ01_POOL01_LTR75LC38_…
    #> 2 4             129390130 +      LOC100507487 +          PJ01_POOL01_LTR75LC38_…
    #> 3 5              84009671 -      EDIL3        -          PJ01_POOL01_LTR75LC38_…
    #> 4 12             54635693 -      CBX5         -          PJ01_POOL01_LTR75LC38_…
    #> 5 2             181930711 +      UBE2E3       +          PJ01_POOL01_LTR75LC38_…
    #> 6 20             35920986 +      MANBAL       +          PJ01_POOL01_LTR75LC38_…
    #> # ℹ 2 more variables: fragmentEstimate <dbl>, seqCount <int>

### `report_path` argument

As other import functions, also `import_parallel_Vispa2Matrices()` produces an interactive report, use this argument to set the appropriate path were the report should be saved.

### `mode` argument

Since `ISAnalytics 1.8.3` this argument can only be set to `AUTO`.

What do you want to import?
In a fully automated mode, the function will try to import everything that is contained in the input association file. This means that if you need to import only a specific set of projects/pools, you will need to filter the association file accordingly prior calling the function (you can easily do that via the `filter_for` argument as explained in Section @ref(metadata)).

How to deal with duplicates?
When scanning folders for files that match a given pattern (in our case the function looks for matrices that match the quantification type and the matrix type), it is very possible that the same folder contains multiple files for the same quantification. Of course this is not recommended, we suggest to move the duplicated files in a sub directory or remove them if they’re not necessary, but in case this happens, you need to set two other arguments (described in the next sub sections) to “help” the function discriminate between duplicates. Please note that if such discrimination is not possible no files are imported.

### `patterns` argument

Providing a set of patterns (interpreted as regular expressions) helps the function to choose between duplicated files if any are found. If you’re confident your folders don’t contain any duplicates feel free to ignore this argument.

### `matching_opt` argument

This argument is relevant only if `patterns` isn’t `NULL`. Tells the function how to match the given patterns if multiple are supplied: `ALL` means keep only those files whose name matches all the given patterns, `ANY` means keep only those files whose name matches any of the given patterns and `OPTIONAL` expresses a preference, try to find files that contain the patterns and if you don’t find any return whatever you find.

### `...` argument

Additional named arguments to supply to `comparison_matrix()` and `import_single_Vispa2_matrix`

## Notes

Earlier versions of the package featured two separated functions, `import_parallel_Vispa2Matrices_auto()` and `import_parallel_Vispa2Matrices_interactive()`. Those functions are now officially deprecated (since `ISAnalytics 1.3.3`) and will be defunct on the next release cycle.

# Data cleaning and pre-processing

This section goes more in detail on some data cleaning and pre-processing operations you can perform with this package.

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()`
- 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()`

## Removing collisions

In this section we illustrate the functions dedicated to collision removal.

### What is a collision and why should you care?

We’re not going into too much detail here, but we’re going to explain in a very simple way what a “collision” is and how the function in this package deals with them.

We say that an integration (aka a unique combination of `mandatory_IS_vars()`) is a collision if this combination is shared between different independent samples: an independent sample is a unique combination of metadata fields specified by the user. The reason behind this is that it’s highly improbable to observe the very same integration in two different independent samples and this phenomenon might be an indicator of some kind of contamination in the sequencing phase or in PCR phase, for this reason we might want to exclude such contamination from our analysis. `ISAnalytics` provides a function that processes the imported data for the removal or reassignment of these “problematic” integrations, `remove_collisions()`.

The processing is done using the sequence count value, so the corresponding matrix is needed for this operation.

### The logic behind the function

The `remove_collisions()` function follows several logical steps to decide whether an integration is a collision and if it is it decides whether to re-assign it or remove it entirely based on different criteria.

#### Identifying the collisions

The function uses the information stored in the association file to assess which independent samples are present and counts the number of independent samples for each integration: those who have a count > 1 are considered collisions.

Example of collisions: the same integration (1, 123454, +) is found in 2 different independent samples ((SUBJ01, PJ01) & (SUBJ02, PJ01))
chr | integration_locus | strand | seqCount | CompleteAmplificationID | SubjectID | ProjectID
--- | --- | --- | --- | --- | --- | ---
1 | 123454 | + | 653 | SAMPLE1 | SUBJ01 | PJ01
1 | 123454 | + | 456 | SAMPLE2 | SUBJ02 | PJ01

#### Re-assign vs remove

Once the collisions are identified, the function follows 3 steps where it tries to re-assign the combination to a single independent sample. The criteria are:

1. Compare dates: if it’s possible to have an absolute ordering on dates, the integration is re-assigned to the sample that has the earliest date. If two samples share the same date it’s impossible to decide, so the next criteria is tested
2. Compare replicate number: if a sample has the same integration in more than one replicate, it’s more probable the integration is not an artifact. If it’s possible to have an absolute ordering, the collision is re-assigned to the sample whose grouping is largest
3. Compare the sequence count value: if the previous criteria wasn’t sufficient to make a decision, for each group of independent samples it’s evaluated the sum of the sequence count value - for each group there is a cumulative value of the sequence count and this is compared to the value of other groups. If there is a single group which has a ratio n times bigger than other groups, this one is chosen for re-assignment. The factor n is passed as a parameter in the function (`reads_ratio`), the default value is 10.

If none of the criteria were sufficient to make a decision, the integration is simply removed from the matrix.

### Usage

    data("integration_matrices", package = "ISAnalytics")
    data("association_file", package = "ISAnalytics")
    ## Multi quantification matrix
    no_coll <- remove_collisions(
        x = integration_matrices,
        association_file = association_file,
        report_path = NULL
    )
    #> Identifying collisions...
    #> Processing collisions...
    #> Finished!
    ## Matrix list
    separated <- separate_quant_matrices(integration_matrices)
    no_coll_list <- remove_collisions(
        x = separated,
        association_file = association_file,
        report_path = NULL
    )
    #> Identifying collisions...
    #> Processing collisions...
    #> Finished!
    ## Only sequence count
    no_coll_single <- remove_collisions(
        x = separated$seqCount,
        association_file = association_file,
        quant_cols = c(seqCount = "Value"),
        report_path = NULL
    )
    #> Identifying collisions...
    #> Processing collisions...
    #> Finished!

Important notes on the association file:

- You have to be sure your association file is properly filled out. The function requires you to specify a date column (by default “SequencingDate”), you have to ensure this column doesn’t contain NA values or incorrect values.

The function accepts different inputs, namely:

- A multi-quantification matrix: this is always the recommended approach
- A named list of matrices where names are quantification types in `quantification_types()`
- The single sequence count matrix: this is not the recommended approach since it requires a realignment step for other quantification matrices if you have them.

If the option `ISAnalytics.reports` is active, an interactive report in HTML format will be produced at the specified path.

### Re-align other matrices

If you’ve given as input the standalone sequence count matrix to `remove_collisions()`, to realign other matrices you have to call the function `realign_after_collisions()`, passing as input the processed sequence count matrix and the named list of other matrices to realign. NOTE: the names in the list must be quantification types.

    other_realigned <- realign_after_collisions(
        sc_matrix = no_coll_single,
        other_matrices = list(fragmentEstimate = separated$fragmentEstimate)
    )

## Performing data and metadata aggregation

In this section we’re going to explain in detail how to use functions of the aggregate family, namely:

1. `aggregate_metadata()`
2. `aggregate_values_by_key()`

### Aggregating metadata

We refer to information contained in the association file as “metadata”: sometimes it’s useful to obtain collective information based on a certain group of variables we’re interested in. The function `aggregate_metadata()` does just that: according to the grouping variables, meaning the names of the columns in the association file to perform a `group_by` operation with,it creates a summary. You can fully customize the summary by providing a “function table” that tells the function which operation should be applied to which column and what name to give to the output column. A default is already supplied:

    #> # A tibble: 15 × 4
    #>    Column               Function  Args  Output_colname
    #>    <chr>                <list>    <lgl> <chr>
    #>  1 FusionPrimerPCRDate  <formula> NA    {.col}_min
    #>  2 LinearPCRDate        <formula> NA    {.col}_min
    #>  3 VCN                  <formula> NA    {.col}_avg
    #>  4 ng DNA corrected     <formula> NA    {.col}_avg
    #>  5 Kapa                 <formula> NA    {.col}_avg
    #>  6 ng DNA corrected     <formula> NA    {.col}_sum
    #>  7 ulForPool            <formula> NA    {.col}_sum
    #>  8 BARCODE_MUX          <formula> NA    {.col}_sum
    #>  9 TRIMMING_FINAL_LTRLC <formula> NA    {.col}_sum
    #> 10 LV_MAPPED            <formula> NA    {.col}_sum
    #> 11 BWA_MAPPED_OVERALL   <formula> NA    {.col}_sum
    #> 12 ISS_MAPPED_OVERALL   <formula> NA    {.col}_sum
    #> 13 PCRMethod            <formula> NA    {.col}
    #> 14 NGSTechnology        <formula> NA    {.col}
    #> 15 DNAnumber            <formula> NA    {.col}

You can either provide purrr-style lambdas (as given in the example above), or simply specify the name of the function and additional parameters as a list in a separated column. If you choose to provide your own table you should maintain the column names for the function to work properly. For more details on this take a look at the function documentation `?default_meta_agg`.

#### Typical workflow

1. Import the association file via `import_assocition_file()`. If you need more information on import function please view the vignette “How to use import functions”: `vignette("how_to_import_functions", package="ISAnalytics")`.
2. Perform aggregation

    data("association_file", package = "ISAnalytics")
    aggregated_meta <- aggregate_metadata(association_file = association_file)

    #> # A tibble: 20 × 19
    #>    SubjectID CellMarker Tissue TimePoint FusionPrimerPCRDate_min
    #>    <chr>     <chr>      <chr>  <chr>     <date>
    #>  1 PT001     MNC        BM     0030      2016-11-03
    #>  2 PT001     MNC        BM     0060      2016-11-03
    #>  3 PT001     MNC        BM     0090      2016-11-03
    #>  4 PT001     MNC        BM     0180      2016-11-03
    #>  5 PT001     MNC        BM     0360      2017-04-21
    #>  6 PT001     MNC        PB     0030      2016-11-03
    #>  7 PT001     MNC        PB     0060      2016-11-03
    #>  8 PT001     MNC        PB     0090      2016-11-03
    #>  9 PT001     MNC        PB     0180      2016-11-03
    #> 10 PT001     MNC        PB     0360      2017-04-21
    #> 11 PT002     MNC        BM     0030      2017-04-21
    #> 12 PT002     MNC        BM     0060      2017-05-05
    #> 13 PT002     MNC        BM     0090      2017-05-05
    #> 14 PT002     MNC        BM     0180      2017-05-16
    #> 15 PT002     MNC        BM     0360      2018-03-12
    #> 16 PT002     MNC        PB     0030      2017-04-21
    #> 17 PT002     MNC        PB     0060      2017-05-05
    #> 18 PT002     MNC        PB     0090      2017-05-05
    #> 19 PT002     MNC        PB     0180      2017-05-05
    #> 20 PT002     MNC        PB     0360      2018-03-12
    #> # ℹ 14 more variables: LinearPCRDate_min <date>, VCN_avg <dbl>,
    #> #   `ng DNA corrected_avg` <dbl>, Kapa_avg <dbl>, `ng DNA corrected_sum` <dbl>,
    #> #   ulForPool_sum <dbl>, BARCODE_MUX_sum <int>, TRIMMING_FINAL_LTRLC_sum <int>,
    #> #   LV_MAPPED_sum <int>, BWA_MAPPED_OVERALL_sum <int>,
    #> #   ISS_MAPPED_OVERALL_sum <int>, PCRMethod <chr>, NGSTechnology <chr>,
    #> #   DNAnumber <chr>

### Aggregation of values by key

`ISAnalytics` contains useful functions to aggregate the values contained in your imported matrices based on a key, aka a single column or a combination of columns contained in the association file that are related to the samples.

#### Typical workflow

1. Import your association file
2. Import integration matrices via `import_parallel_Vispa2Matrices()`
3. Perform aggregation

    data("integration_matrices", package = "ISAnalytics")
    data("association_file", package = "ISAnalytics")
    aggreg <- aggregate_values_by_key(
        x = integration_matrices,
        association_file = association_file,
        value_cols = c("seqCount", "fragmentEstimate")
    )

    #> # A tibble: 1,074 × 11
    #>    chr   integration_locus strand GeneName GeneStrand SubjectID CellMarker
    #>    <chr>             <dbl> <chr>  <chr>    <chr>      <chr>     <chr>
    #>  1 1               8464757 -      RERE     -          PT001     MNC
    #>  2 1               8464757 -      RERE     -          PT001     MNC
    #>  3 1               8607357 +      RERE     -          PT001     MNC
    #>  4 1               8607357 +      RERE     -          PT001     MNC
    #>  5 1               8607357 +      RERE     -          PT001     MNC
    #>  6 1               8607362 -      RERE     -          PT001     MNC
    #>  7 1               8850362 +      RERE     -          PT002     MNC
    #>  8 1              11339120 +      UBIAD1   +          PT001     MNC
    #>  9 1              11339120 +      UBIAD1   +          PT001     MNC
    #> 10 1              11339120 +      UBIAD1   +          PT001     MNC
    #>    Tissue TimePoint seqCount_sum fragmentEstimate_sum
    #>    <chr>  <chr>            <dbl>                <dbl>
    #>  1 BM     0030               542                 3.01
    #>  2 BM     0060                 1                 1.00
    #>  3 BM     0060                 1                 1.00
    #>  4 BM     0180              1096                 5.01
    #>  5 BM     0360               330                34.1
    #>  6 BM     0180              1702                 4.01
    #>  7 BM     0360               562                 3.01
    #>  8 BM     0060              1605                 8.03
    #>  9 PB     0060                 1                 1.00
    #> 10 PB     0180                 1                 1.00
    #> # ℹ 1,064 more rows

The function `aggregate_values_by_key` can perform the aggregation both on the list of matrices and a single matrix.

##### Changing parameters to obtain different results

The function has several different parameters that have default values that can be changed according to user preference.

1. Changing the `key` value
   You can change the value of the parameter key as you see fit. This parameter should contain one or multiple columns of the association file that you want to include in the grouping when performing the aggregation. The default value is set to `c("SubjectID", "CellMarker", "Tissue", "TimePoint")` (same default key as the `aggregate_metadata` function).

    agg1 <- aggregate_values_by_key(
        x = integration_matrices,
        association_file = association_file,
        key = c("SubjectID", "ProjectID"),
        value_cols = c("seqCount", "fragmentEstimate")
    )

    #> # A tibble: 577 × 9
    #>    chr   integration_locus strand GeneName GeneStrand SubjectID ProjectID
    #>    <chr>             <dbl> <chr>  <chr>    <chr>      <chr>     <chr>
    #>  1 1               8464757 -      RERE     -          PT001     PJ01
    #>  2 1               8607357 +      RERE     -          PT001     PJ01
    #>  3 1               8607362 -      RERE     -          PT001     PJ01
    #>  4 1               8850362 +      RERE     -          PT002     PJ01
    #>  5 1              11339120 +      UBIAD1   +          PT001     PJ01
    #>  6 1              12341466 -      VPS13D   +          PT002     PJ01
    #>  7 1              14034054 -      PRDM2    +          PT002     PJ01
    #>  8 1              16186297 -      SPEN     +          PT001     PJ01
    #>  9 1              16602483 +      FBXO42   -          PT001     PJ01
    #> 10 1              16602483 +      FBXO42   -          PT002     PJ01
    #>    seqCount_sum fragmentEstimate_sum
    #>           <dbl>                <dbl>
    #>  1          543                 4.01
    #>  2         1427                40.1
    #>  3         1702                 4.01
    #>  4          562                 3.01
    #>  5         1607                10.0
    #>  6         1843                 8.05
    #>  7         1938                 3.01
    #>  8         3494                16.1
    #>  9         2947                 9.04
    #> 10           30                 2.00
    #> # ℹ 567 more rows

2. Changing the `lambda` value
   The `lambda` parameter indicates the function(s) to be applied to the values for aggregation. `lambda` must be a named list of either functions or purrr-style lambdas: if you would like to specify additional parameters to the function the second option is recommended. The only important note on functions is that they should perform some kind of aggregation on numeric values: this means in practical terms they need to accept a vector of numeric/integer values as input and produce a SINGLE value as output. Valid options for this purpose might be: `sum`, `mean`, `median`, `min`, `max` and so on.

    agg2 <- aggregate_values_by_key(
        x = integration_matrices,
        association_file = association_file,
        key = "SubjectID",
        lambda = list(mean = ~ mean(.x, na.rm = TRUE)),
        value_cols = c("seqCount", "fragmentEstimate")
    )

    #> # A tibble: 577 × 8
    #>    chr   integration_locus strand GeneName GeneStrand SubjectID seqCount_mean
    #>    <chr>             <dbl> <chr>  <chr>    <chr>      <chr>             <dbl>
    #>  1 1               8464757 -      RERE     -          PT001              272.
    #>  2 1               8607357 +      RERE     -          PT001              285.
    #>  3 1               8607362 -      RERE     -          PT001              851
    #>  4 1               8850362 +      RERE     -          PT002              562
    #>  5 1              11339120 +      UBIAD1   +          PT001              321.
    #>  6 1              12341466 -      VPS13D   +          PT002             1843
    #>  7 1              14034054 -      PRDM2    +          PT002             1938
    #>  8 1              16186297 -      SPEN     +          PT001              699.
    #>  9 1              16602483 +      FBXO42   -          PT001              982.
    #> 10 1              16602483 +      FBXO42   -          PT002               30
    #>    fragmentEstimate_mean
    #>                    <dbl>
    #>  1                  2.01
    #>  2                  8.02
    #>  3                  2.01
    #>  4                  3.01
    #>  5                  2.01
    #>  6                  8.05
    #>  7                  3.01
    #>  8                  3.22
    #>  9                  3.01
    #> 10                  2.00
    #> # ℹ 567 more rows

Note that, when specifying purrr-style lambdas (formulas), the first parameter needs to be set to `.x`, other parameters can be set as usual.

You can also use in `lambda` functions that produce data frames or lists. In this case all variables from the produced data frame will be included in the final data frame. For example:

    agg3 <- aggregate_values_by_key(
        x = integration_matrices,
        association_file = association_file,
        key = "SubjectID",
        lambda = list(describe = ~ list(psych::describe(.x))),
        value_cols = c("seqCount", "fragmentEstimate")
    )

    #> # A tibble: 577 × 8
    #>    chr   integration_locus strand GeneName GeneStrand SubjectID
    #>    <chr>             <dbl> <chr>  <chr>    <chr>      <chr>
    #>  1 1               8464757 -      RERE     -          PT001
    #>  2 1               8607357 +      RERE     -          PT001
    #>  3 1               8607362 -      RERE     -          PT001
    #>  4 1               8850362 +      RERE     -          PT002
    #>  5 1              11339120 +      UBIAD1   +          PT001
    #>  6 1              12341466 -      VPS13D   +          PT002
    #>  7 1              14034054 -      PRDM2    +          PT002
    #>  8 1              16186297 -      SPEN     +          PT001
    #>  9 1              16602483 +      FBXO42   -          PT001
    #> 10 1              16602483 +      FBXO42   -          PT002
    #>    seqCount_describe fragmentEstimate_describe
    #>    <list>            <list>
    #>  1 <psych [1 × 13]>  <psych [1 × 13]>
    #>  2 <psych [1 × 13]>  <psych [1 × 13]>
    #>  3 <psych [1 × 13]>  <psych [1 × 13]>
    #>  4 <psych [1 × 13]>  <psych [1 × 13]>
    #>  5 <psych [1 × 13]>  <psych [1 × 13]>
    #>  6 <psych [1 × 13]>  <psych [1 × 13]>
    #>  7 <psych [1 × 13]>  <psych [1 × 13]>
    #>  8 <psych [1 × 13]>  <psych [1 × 13]>
    #>  9 <psych [1 × 13]>  <psych [1 × 13]>
    #> 10 <psych [1 × 13]>  <psych [1 × 13]>
    #> # ℹ 567 more rows

3. Changing the `value_cols` value
   The `value_cols` parameter tells the function on which numeric columns of x the functions should be applied. Note that every function contained in `lambda` will be applied to every column in `value_cols`: resulting columns will be named as “original name_function applied”.

    agg4 <- aggregate_values_by_key(
        x = integration_matrices,
        association_file = association_file,
        key = "SubjectID",
        lambda = list(sum = sum, mean = mean),
        value_cols = c("seqCount", "fragmentEstimate")
    )

    #> # A tibble: 577 × 10
    #>    chr   integration_locus strand GeneName GeneStrand SubjectID seqCount_sum
    #>    <chr>             <dbl> <chr>  <chr>    <chr>      <chr>            <dbl>
    #>  1 1               8464757 -      RERE     -          PT001              543
    #>  2 1               8607357 +      RERE     -          PT001             1427
    #>  3 1               8607362 -      RERE     -          PT001             1702
    #>  4 1               8850362 +      RERE     -          PT002              562
    #>  5 1              11339120 +      UBIAD1   +          PT001             1607
    #>  6 1              12341466 -      VPS13D   +          PT002             1843
    #>  7 1              14034054 -      PRDM2    +          PT002             1938
    #>  8 1              16186297 -      SPEN     +          PT001             3494
    #>  9 1              16602483 +      FBXO42   -          PT001             2947
    #> 10 1              16602483 +      FBXO42   -          PT002               30
    #>    seqCount_mean fragmentEstimate_sum fragmentEstimate_mean
    #>            <dbl>                <dbl>                 <dbl>
    #>  1          272.                 4.01                  2.01
    #>  2          285.                40.1                   8.02
    #>  3          851                  4.01                  2.01
    #>  4          562                  3.01                  3.01
    #>  5          321.                10.0                   2.01
    #>  6         1843                  8.05                  8.05
    #>  7         1938                  3.01                  3.01
    #>  8          699.                16.1                   3.22
    #>  9          982.                 9.04                  3.01
    #> 10           30                  2.00                  2.00
    #> # ℹ 567 more rows

4. Changing the `group` value
   The `group` parameter should contain all other variables to include in the grouping besides `key`. By default this contains `c("chr", "integration_locus","strand", "GeneName", "GeneStrand")`. You can change this grouping as you see fit, if you don’t want to add any other variable to the key, just set it to `NULL`.

    agg5 <- aggregate_values_by_key(
        x = integration_matrices,
        association_file = association_file,
        key = "SubjectID",
        lambda = list(sum = sum, mean = mean),
        group = c(mandatory_IS_vars()),
        value_cols = c("seqCount", "fragmentEstimate")
    )

    #> # A tibble: 577 × 8
    #>    chr   integration_locus strand SubjectID seqCount_sum seqCount_mean
    #>    <chr>             <dbl> <chr>  <chr>            <dbl>         <dbl>
    #>  1 1               8464757 -      PT001              543          272.
    #>  2 1               8607357 +      PT001             1427          285.
    #>  3 1               8607362 -      PT001             1702          851
    #>  4 1               8850362 +      PT002              562          562
    #>  5 1              11339120 +      PT001             1607          321.
    #>  6 1              12341466 -      PT002             1843         1843
    #>  7 1              14034054 -      PT002             1938         1938
    #>  8 1              16186297 -      PT001             3494          699.
    #>  9 1              16602483 +      PT001             2947          982.
    #> 10 1              16602483 +      PT002               30           30
    #>    fragmentEstimate_sum fragmentEstimate_mean
    #>                   <dbl>                 <dbl>
    #>  1                 4.01                  2.01
    #>  2                40.1                   8.02
    #>  3                 4.01                  2.01
    #>  4                 3.01                  3.01
    #>  5                10.0                   2.01
    #>  6                 8.05                  8.05
    #>  7                 3.01                  3.01
    #>  8                16.1                   3.22
    #>  9                 9.04                  3.01
    #> 10                 2.00                  2.00
    #> # ℹ 567 more rows

# Analysis use-case example: shared integration sites

An integration site is always characterized by a triple of values: `(chr, integration_locus, strand)`, hence these attributes are always present in integration matrices.

We can aggregate our data in different ways according to our needs, obtaining therefore different groups. Each group has an associated set of integration sites.

NOTE: aggregating data is not mandatory, since sharing functions in ISAnalytics only count distinct integration sites and do not require any quantification. The only important thing is that columns that are included in the specified key are also included in the input matrices.

    ## Aggregation by standard key
    agg <- aggregate_values_by_key(integration_matrices,
        association_file,
        value_cols = c("seqCount", "fragmentEstimate")
    )
    agg <- agg |> dplyr::filter(TimePoint %in% c("0030", "0060"))

    #> # A tibble: 419 × 11
    #>    chr   integration_locus strand GeneName GeneStrand SubjectID CellMarker
    #>    <chr>             <dbl> <chr>  <chr>    <chr>      <chr>     <chr>
    #>  1 1               8464757 -      RERE     -          PT001     MNC
    #>  2 1               8464757 -      RERE     -          PT001     MNC
    #>  3 1               8607357 +      RERE     -          PT001     MNC
    #>  4 1              11339120 +      UBIAD1   +          PT001     MNC
    #>  5 1              11339120 +      UBIAD1   +          PT001     MNC
    #>  6 1              16186297 -      SPEN     +          PT001     MNC
    #>  7 1              16186297 -      SPEN     +          PT001     MNC
    #>  8 1              16602483 +      FBXO42   -          PT001     MNC
    #>  9 1              25337264 -      MIR4425  +          PT002     MNC
    #> 10 1              25337264 -      MIR4425  +          PT002     MNC
    #>    Tissue TimePoint seqCount_sum fragmentEstimate_sum
    #>    <chr>  <chr>            <dbl>                <dbl>
    #>  1 BM     0030               542                 3.01
    #>  2 BM     0060                 1                 1.00
    #>  3 BM     0060                 1                 1.00
    #>  4 BM     0060              1605                 8.03
    #>  5 PB     0060                 1                 1.00
    #>  6 BM     0030                 1                 1.00
    #>  7 PB     0060                 1                 1.00
    #>  8 BM     0060              2947                 9.04
    #>  9 BM     0030                23                 9.14
    #> 10 PB     0060                36                 7.07
    #> # ℹ 409 more rows

An integration site is shared between two or more groups if the same triple is observed in all the groups considered.

## Automated sharing counts

ISAnalytics provides the function `is_sharing()` for computing automated sharing counts. The function has several arguments that can be tuned according to user needs.

## SCENARIO 1: single input data frame and single grouping key

    sharing_1 <- is_sharing(agg,
        group_key = c(
            "SubjectID", "CellMarker",
            "Tissue", "TimePoint"
        ),
        n_comp = 2,
        is_count = TRUE,
        relative_is_sharing = TRUE,
        minimal = TRUE,
        include_self_comp = FALSE,
        keep_genomic_coord = TRUE
    )
    sharing_1
    #> # A tibble: 28 × 10
    #>    g1           g2    shared count_g1 count_g2 count_union  on_g1 on_g2 on_union
    #>    <chr>        <chr>  <int>    <int>    <int>       <int>  <dbl> <dbl>    <dbl>
    #>  1 PT001_MNC_B… PT00…     21       54      114         147 38.9   18.4    14.3
    #>  2 PT001_MNC_B… PT00…      7       54       28          75 13.0   25       9.33
    #>  3 PT001_MNC_B… PT00…      8       54       59         105 14.8   13.6     7.62
    #>  4 PT001_MNC_B… PT00…      0       54       98         152  0      0       0
    #>  5 PT001_MNC_B… PT00…      1       54       33          86  1.85   3.03    1.16
    #>  6 PT001_MNC_B… PT00…      0       54       15          69  0      0       0
    #>  7 PT001_MNC_B… PT00…      0       54       18          72  0      0       0
    #>  8 PT001_MNC_B… PT00…      7      114       28         135  6.14  25       5.19
    #>  9 PT001_MNC_B… PT00…     29      114       59         144 25.4   49.2    20.1
    #> 10 PT001_MNC_B… PT00…      1      114       98         211  0.877  1.02    0.474
    #> # ℹ 18 more rows
    #> # ℹ 1 more variable: is_coord <list>

In this configuration we set:

- A single input data frame: `agg`
- A single grouping key by setting the argument `grouping_key`. In this specific case, our groups will be identified by a unique combination of `SubjectID`, `CellMarker`, `Tissue` and `TimePoint`
- `n_comp` represents the number of comparisons to compute: 2 means we’re interested in knowing the sharing for PAIRS of distinct groups
- We want to keep the counts of distinct integration sites for each group by setting `is_count` to `TRUE`
- `relative_is_sharing` if set to `TRUE` adds sharing expressed as a percentage, more precisely it adds a column `on_g1` that is calculated as the absolute number of shared integrations divided by the cardinality of the first group, `on_g2` is analogous but is computed on the cardinality of the second group and finally `on_union` is computed on the cardinality of the union of the two groups.
- By setting the argument `minimal` to `TRUE` we tell the function to avoid redundant comparisons: in this way only combinations and not permutations are included in the output table
- `include_self_comp` adds rows in the table that are labelled with the same group: these rows always have a 100% sharing with all other groups. There are few scenarios where this is useful, but for now we set it to `FALSE` since we don’t need it
- `keep_genomic_coord` allows us to keep the genomic coordinates of the shared integration sites as a separate table

#### Changing the number of comparisons

    sharing_1_a <- is_sharing(agg,
        group_key = c(
            "SubjectID", "CellMarker",
            "Tissue", "TimePoint"
        ),
        n_comp = 3,
        is_count = TRUE,
        relative_is_sharing = TRUE,
        minimal = TRUE,
        include_self_comp = FALSE,
        keep_genomic_coord = TRUE
    )
    sharing_1_a
    #> # A tibble: 56 × 13
    #>    g1      g2    g3    shared count_g1 count_g2 count_g3 count_union on_g1 on_g2
    #>    <chr>   <chr> <chr>  <int>    <int>    <int>    <int>       <int> <dbl> <dbl>
    #>  1 PT001_… PT00… PT00…      5       54      114       28         166  9.26  4.39
    #>  2 PT001_… PT00… PT00…      6       54      114       59         175 11.1   5.26
    #>  3 PT001_… PT00… PT00…      0       54      114       98         244  0     0
    #>  4 PT001_… PT00… PT00…      0       54      114       33         179  0     0
    #>  5 PT001_… PT00… PT00…      0       54      114       15         161  0     0
    #>  6 PT001_… PT00… PT00…      0       54      114       18         165  0     0
    #>  7 PT001_… PT00… PT00…      1       54       28       59         117  1.85  3.57
    #>  8 PT001_… PT00… PT00…      0       54       28       98         173  0     0
    #>  9 PT001_… PT00… PT00…      0       54       28       33         107  0     0
    #> 10 PT001_… PT00… PT00…      0       54       28       15          90  0     0
    #> # ℹ 46 more rows
    #> # ℹ 3 more variables: on_g3 <dbl>, on_union <dbl>, is_coord <list>

Changing the `n_comp` to 3 means that we want to calculate the sharing between 3 different groups. Note that the `shared` column contains the counts of integrations that are shared by ALL groups, which is equivalent to a set intersection.

Beware of the fact that the more comparisons are requested the more time the computation requires.

#### A case when it is useful to set `minimal = FALSE`

Setting `minimal = FALSE` produces all possible permutations of the groups and the corresponding values. In combination with `include_self_comp = TRUE`, this is useful when we want to know the sharing between pairs of groups and plot results as a heatmap.

    sharing_1_b <- is_sharing(agg,
        group_key = c(
            "SubjectID", "CellMarker",
            "Tissue", "TimePoint"
        ),
        n_comp = 2,
        is_count = TRUE,
        relative_is_sharing = TRUE,
        minimal = FALSE,
        include_self_comp = TRUE
    )
    sharing_1_b
    #> # A tibble: 64 × 9
    #>    g1            g2    shared count_g1 count_g2 count_union on_g1 on_g2 on_union
    #>    <chr>         <chr>  <int>    <int>    <int>       <int> <dbl> <dbl>    <dbl>
    #>  1 PT001_MNC_BM… PT00…     54       54       54          54 100   100      100
    #>  2 PT001_MNC_BM… PT00…    114      114      114         114 100   100      100
    #>  3 PT001_MNC_PB… PT00…     59       59       59          59 100   100      100
    #>  4 PT002_MNC_BM… PT00…     98       98       98          98 100   100      100
    #>  5 PT002_MNC_PB… PT00…     18       18       18          18 100   100      100
    #>  6 PT002_MNC_PB… PT00…     15       15       15          15 100   100      100
    #>  7 PT001_MNC_PB… PT00…     28       28       28          28 100   100      100
    #>  8 PT002_MNC_BM… PT00…     33       33       33          33 100   100      100
    #>  9 PT001_MNC_BM… PT00…     21       54      114         147  38.9  18.4     14.3
    #> 10 PT001_MNC_BM… PT00…     21      114       54         147  18.4  38.9     14.3
    #> # ℹ 54 more rows
    heatmaps <- sharing_heatmap(sharing_1_b)

The function `sharing_heatmap()` automatically plots sharing between 2 groups. There are several arguments to this function that allow us to obtain heatmaps for the absolute sharing values or the relative (percentage) values.

    heatmaps$absolute

    heatmaps$on_g1

    heatmaps$on_union

Beware of the fact that calculating all permutations takes longer than calculating only distinct combinations, therefore if you don’t need your results plotted or you have more than 2 groups to compare you should stick with `minimal = TRUE` and `include_self_comp = FALSE`.

### SCENARIO 2: single input data frame and multiple grouping keys

In the first scenario, groups were homogeneous, that is, they were grouped all with the same key. In this other scenario we want to start with data contained in just one data frame but we want to compare sets of integrations that are grouped differently. To do this we give as input a list of keys through the argument `group_keys`.

    sharing_2 <- is_sharing(agg,
        group_keys = list(
            g1 = c(
                "SubjectID", "CellMarker",
                "Tissue", "TimePoint"
            ),
            g2 = c("SubjectID", "CellMarker"),
            g3 = c("CellMarker", "Tissue")
        )
    )
    sharing_2
    #> # A tibble: 32 × 12
    #>    g1    g2    g3    shared count_g1 count_g2 count_g3 count_union  on_g1  on_g2
    #>    <chr> <chr> <chr>  <int>    <int>    <int>    <int>       <int>  <dbl>  <dbl>
    #>  1 PT00… PT00… MNC_…     54       54      186      271         310 100    29.0
    #>  2 PT00… PT00… MNC_…     14       54      186      103         211  25.9   7.53
    #>  3 PT00… PT00… MNC_…      1       54      137      271         281   1.85  0.730
    #>  4 PT00… PT00… MNC_…      0       54      137      103         252   0     0
    #>  5 PT00… PT00… MNC_…    114      114      186      271         310 100    61.3
    #>  6 PT00… PT00… MNC_…     35      114      186      103         211  30.7  18.8
    #>  7 PT00… PT00… MNC_…      2      114      137      271         281   1.75  1.46
    #>  8 PT00… PT00… MNC_…      2      114      137      103         292   1.75  1.46
    #>  9 PT00… PT00… MNC_…      9       28      186      271         310  32.1   4.84
    #> 10 PT00… PT00… MNC_…     28       28      186      103         211 100    15.1
    #> # ℹ 22 more rows
    #> # ℹ 2 more variables: on_g3 <dbl>, on_union <dbl>

There are a few things to keep in mind in this case:

- The arguments `group_key` (notice the absence of plural), `n_comp` and `include_self_comp` are ignored: the number of comparisons is automatically detected by counting the provided keys and a self comparison doesn’t make sense since group labels are different
- If you provide a list of identical keys or just one key you fall back to scenario 1

### SCENARIO 3: multiple input data frame and single grouping key

Providing multiple input data frames and the same grouping key is an effective way to reduce the number of comparisons performed. Let’s make an example: suppose we’re interested in comparing groups labelled by a unique combination of `SubjectID`, `CellMarker`, `Tissue` and `TimePoint`, but this time we want the first group to contain only integrations relative to `PT001_MNC_BM_0030` and the second group to contain only integrations relative to `PT001_MNC_BM_0060`.

We’re going to filter the original data frame in order to obtain only relevant data in 2 separated tables and then proceed by calling the function.

    first_sample <- agg |>
        dplyr::filter(
            SubjectID == "PT001", CellMarker == "MNC", Tissue == "BM",
            TimePoint == "0030"
        )
    second_sample <- agg |>
        dplyr::filter(
            SubjectID == "PT001", CellMarker == "MNC", Tissue == "BM",
            TimePoint == "0060"
        )
    sharing_3 <- is_sharing(first_sample, second_sample,
        group_key = c(
            "SubjectID", "CellMarker",
            "Tissue", "TimePoint"
        ),
        is_count = TRUE,
        relative_is_sharing = TRUE,
        minimal = TRUE
    )
    sharing_3
    #> # A tibble: 1 × 9
    #>   g1             g2    shared count_g1 count_g2 count_union on_g1 on_g2 on_union
    #>   <chr>          <chr>  <int>    <int>    <int>       <int> <dbl> <dbl>    <dbl>
    #> 1 PT001_MNC_BM_… PT00…     21       54      114         147  38.9  18.4     14.3

Once again the arguments `n_comp` and `include_self_comp` are ignored: the number of comparisons is equal to the number of data frames in input.

To handle special limit cases, the output group ids are appended with a dash and a number (`-1`, `-2`, …) that indicates the data frame of origin: this is useful in the case group ids are duplicated in the inputs. To understand better let’s make an example:

    sharing_3_a <- is_sharing(
        first_sample, second_sample,
        group_key = c(
            "CellMarker", "Tissue"
        ),
        is_count = TRUE,
        relative_is_sharing = TRUE,
        minimal = FALSE
    )
    sharing_3_a
    #> # A tibble: 2 × 9
    #>   g1       g2       shared count_g1 count_g2 count_union on_g1 on_g2 on_union
    #>   <chr>    <chr>     <int>    <int>    <int>       <int> <dbl> <dbl>    <dbl>
    #> 1 MNC_BM-1 MNC_BM-2     21       54      114         147  38.9  18.4     14.3
    #> 2 MNC_BM-2 MNC_BM-1     21      114       54         147  18.4  38.9     14.3

As you can see, the IDs of group 1 and group 2 are duplicated and without a suffix it would be impossible to know which one came from which data frame. In this way we know that the group “MNC_BM-1” comes from table 1 and has 54 ISs, while “MNC_BM-2” comes from the second input table and has 114 ISs.

### SCENARIO 4: multiple input data frame and multiple grouping keys

Finally, the most general scenario is when we have multiple data frames with multiple keys. In this case the number of data frames must be equal to the number of provided keys and grouping keys are applied in order ( data frame 1 is grouped with key 1, data frame 2 is grouped with key 2, and so on).

    df1 <- agg |>
        dplyr::filter(TimePoint == "0030")
    df2 <- agg |>
        dplyr::filter(TimePoint == "0060")
    df3 <- agg |>
        dplyr::filter(Tissue == "BM")

    keys <- list(
        g1 = c("SubjectID", "CellMarker", "Tissue"),
        g2 = c("SubjectID", "Tissue"),
        g3 = c("SubjectID", "CellMarker", "Tissue")
    )

    sharing_4 <- is_sharing(df1, df2, df3, group_keys = keys)
    sharing_4
    #> # A tibble: 32 × 12
    #>    g1      g2    g3    shared count_g1 count_g2 count_g3 count_union on_g1 on_g2
    #>    <chr>   <chr> <chr>  <int>    <int>    <int>    <int>       <int> <dbl> <dbl>
    #>  1 PT001_… PT00… PT00…     21       54      114      147         147 38.9  18.4
    #>  2 PT001_… PT00… PT00…      0       54      114      126         271  0     0
    #>  3 PT001_… PT00… PT00…      8       54       59      147         175 14.8  13.6
    #>  4 PT001_… PT00… PT00…      0       54       59      126         229  0     0
    #>  5 PT001_… PT00… PT00…      1       54       33      147         179  1.85  3.03
    #>  6 PT001_… PT00… PT00…      1       54       33      126         179  1.85  3.03
    #>  7 PT001_… PT00… PT00…      0       54       18      147         165  0     0
    #>  8 PT001_… PT00… PT00…      0       54       18      126         185  0     0
    #>  9 PT001_… PT00… PT00…      7       28      114      147         166 25     6.14
    #> 10 PT001_… PT00… PT00…      0       28      114      126         260  0     0
    #> # ℹ 22 more rows
    #> # ℹ 2 more variables: on_g3 <dbl>, on_union <dbl>

Notice that in this example the keys for g1 and g3 are the same, meaning the labels of the groups are actually the same, however you should remember that the groups contain a different set of integration sites since they come from different data frames.

## Plotting sharing results

When we have more than 2 comparisons it is convenient to plot them as venn or euler diagrams. ISAnalytics has a fast way to do that through the functions `is_sharing()` and `sharing_venn()`.

    sharing_5 <- is_sharing(agg,
        group_keys = list(
            g1 = c(
                "SubjectID", "CellMarker",
                "Tissue", "TimePoint"
            ),
            g2 = c("SubjectID", "CellMarker"),
            g3 = c("CellMarker", "Tissue")
        ), table_for_venn = TRUE
    )
    sharing_5
    #> # A tibble: 32 × 13
    #>    g1    g2    g3    shared count_g1 count_g2 count_g3 count_union  on_g1  on_g2
    #>    <chr> <chr> <chr>  <int>    <int>    <int>    <int>       <int>  <dbl>  <dbl>
    #>  1 PT00… PT00… MNC_…     54       54      186      271         310 100    29.0
    #>  2 PT00… PT00… MNC_…     14       54      186      103         211  25.9   7.53
    #>  3 PT00… PT00… MNC_…      1       54      137      271         281   1.85  0.730
    #>  4 PT00… PT00… MNC_…      0       54      137      103         252   0     0
    #>  5 PT00… PT00… MNC_…    114      114      186      271         310 100    61.3
    #>  6 PT00… PT00… MNC_…     35      114      186      103         211  30.7  18.8
    #>  7 PT00… PT00… MNC_…      2      114      137      271         281   1.75  1.46
    #>  8 PT00… PT00… MNC_…      2      114      137      103         292   1.75  1.46
    #>  9 PT00… PT00… MNC_…      9       28      186      271         310  32.1   4.84
    #> 10 PT00… PT00… MNC_…     28       28      186      103         211 100    15.1
    #> # ℹ 22 more rows
    #> # ℹ 3 more variables: on_g3 <dbl>, on_union <dbl>, truth_tbl_venn <list>

The argument `table_for_venn = TRUE` will add a new column `truth_tbl_venn` that contains corresponding truth tables for each row.

    sharing_plots1 <- sharing_venn(sharing_5, row_range = 1, euler = TRUE)
    sharing_plots2 <- sharing_venn(sharing_5, row_range = 1, euler = FALSE)

Say that we’re interested in plotting just the first row of our sharing data frame. Then we can call the function `sharing_venn` and specify in the `row_range` argument the index 1. Note that this function requires the package eulerr to work. The argument `euler` indicates if the function should produce euler or venn diagrams instead.

Once obtained the lists of euler/venn objects we can plot them by simply calling the function `plot()`:

    plot(sharing_plots1[[1]])

    plot(sharing_plots2[[1]])

There are several options that can be set, for this please refer to eulerr docs.

# Reproducibility

`R` session information.

    #> ─ Session info ───────────────────────────────────────────────────────────────────────────────────────────────────────
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    #>  version  R version 4.5.2 (2025-10-31)
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    #>  collate  C
    #>  ctype    en_US.UTF-8
    #>  tz       Etc/UTC
    #>  date     2025-11-11
    #>  pandoc   3.6.3 @ /usr/local/bin/ (via rmarkdown)
    #>  quarto   1.8.24 @ /usr/local/bin/quarto
    #>
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# Bibliography

This vignette was generated using BiocStyle (Oleś, 2025) with knitr (Xie, 2025) and rmarkdown (Allaire, Xie, Dervieux, McPherson, Luraschi, Ushey, Atkins, Wickham, Cheng, Chang, and Iannone, 2025) running behind the scenes.

Citations made with RefManageR (McLean, 2017).

[1] J. Allaire, Y. Xie, C. Dervieux, et al. rmarkdown: Dynamic Documents for R. R package version 2.30. 2025. URL: https://github.com/rstudio/rmarkdown.

[2] S. B. Giulio Spinozzi Andrea Calabria. “VISPA2: a scalable pipeline for high-throughput identification and annotation of vector integration sites”. In: BMC Bioinformatics (Nov. 25, 2017). DOI: 10.1186/s12859-017-1937-9.

[3] M. W. McLean. “RefManageR: Import and Manage BibTeX and BibLaTeX References in R”. In: The Journal of Open Source Software (2017). DOI: 10.21105/joss.00338.

[4] A. Oleś. BiocStyle: Standard styles for vignettes and other Bioconductor documents. R package version 2.38.0. 2025. URL: https://github.com/Bioconductor/BiocStyle.

[5] Y. Xie. knitr: A General-Purpose Package for Dynamic Report Generation in R. R package version 1.50. 2025. URL: https://yihui.org/knitr/.