1 Introduction

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

  1. aggregate_metadata
  2. aggregate_values_by_key

1.1 How to install ISAnalytics

To install the package run the following code:

## For release version
if (!requireNamespace("BiocManager", quietly = TRUE)) {
      install.packages("BiocManager")
  }
BiocManager::install("ISAnalytics")

## For devel version
if (!requireNamespace("BiocManager", quietly = TRUE)) {
      install.packages("BiocManager")
  }
# The following initializes usage of Bioc devel
BiocManager::install(version = "devel")
BiocManager::install("ISAnalytics")

To install from GitHub:

# For release version
if (!require(devtools)) {
    install.packages("devtools")
}
devtools::install_github("calabrialab/ISAnalytics",
    ref = "RELEASE_3_12",
    dependencies = TRUE,
    build_vignettes = TRUE
)

## Safer option for vignette building issue
devtools::install_github("calabrialab/ISAnalytics",
    ref = "RELEASE_3_12"
)

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

## Safer option for vignette building issue
devtools::install_github("calabrialab/ISAnalytics",
    ref = "master"
)
library(ISAnalytics)
#> Loading required package: magrittr

1.2 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.widgets" = FALSE)

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

2 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, creates a summary which includes:

  • FusionPrimerPCRDate_min - The minimum date in the group for this variable
  • LinearPCRDate_min - The minimum date in the group for this variable
  • VCN_avg - The mean of “VCN” column for each group
  • DNAngUsed_avg - The mean of “DNAngUsed”column for each group
  • Kapa_avg - The mean of “Kapa” column for each group
  • DNAngUsed_sum - The sum of “DNAngUsed” column for each group
  • ulForPool_sum - The sum of “ulForPool” column for each group
  • AggregateMeta_sum - A string obtained by concatenation of all of the grouping variables separated by “_"

2.1 Typical workflow

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”.

withr::with_options(list(ISAnalytics.widgets = FALSE), {
    path_AF <- system.file("extdata", "ex_association_file.tsv",
        package = "ISAnalytics"
    )

    root_correct <- system.file("extdata", "fs.zip",
        package = "ISAnalytics"
    )
    root_correct <- unzip_file_system(root_correct, "fs")
    association_file <- import_association_file(path_AF, root_correct)
})
#> *** Association file import summary ***
#> i For detailed report please set option 'ISAnalytics.widgets' to TRUE
#> * Parsing problems detected: TRUE
#> * Date parsing problems: TRUE
#> * Column problems detected: TRUE
#> * NAs found in important columns: TRUE
#> * File system alignment: no problems detected

Perform aggregation:

aggregated_meta <- aggregate_metadata(association_file,
    grouping_keys = c(
        "SubjectID",
        "CellMarker",
        "Tissue", "TimePoint"
    ),
    import_stats = FALSE
)
AggregateMeta SubjectID CellMarker Tissue TimePoint FusionPrimerPCRDate_min LinearPCRDate_min VCN_avg DNAngUsed_avg Kapa_avg DNAngUsed_sum ulForPool_sum
VA2020-mix10_NA_NA_0000 VA2020-mix10 NA NA 0000 NA NA 0.70 100 26.54667 300 25.70
VA2020-mix11_NA_NA_0000 VA2020-mix11 NA NA 0000 NA NA 2.80 100 59.81000 300 12.29
VA2020-mix12_NA_NA_0000 VA2020-mix12 NA NA 0000 NA NA 4.30 100 79.80333 300 7.98
VA2020-mix13_NA_NA_0000 VA2020-mix13 NA NA 0000 NA NA 9.89 100 85.87000 300 7.46
VA2020-mix14_NA_NA_0000 VA2020-mix14 NA NA 0000 NA NA NaN NaN NaN 0 0.00

As you can see there is an additional parameter you can set, import_stats: if set to TRUE, the function will automatically look into the file system you provided as root when you imported the association file and will try to locate first the iss folder for each project and then all Vispa2 “stats” files.
Vispa2 stats contain useful information that is not included in the association file and is linked to the single Vispa2 run. In some cases it’s useful to perform aggregation on those info too. If you set the parameter to TRUE, besides the columns mentioned before you will also have:

  • BARCODE_MUX_sum - The sum of the “BARCODE_MUX” column for the group
  • TRIMMING_FINAL_LTRLC_sum - The sum of the “TRIMMING_FINAL_LTRLC” column for the group
  • LV_MAPPED_sum - The sum of the “LV_MAPPED” column for the group
  • BWA_MAPPED_OVERALL_sum - The sum of the “BWA_MAPPED_OVERALL” column for the group
  • ISS_MAPPED_PP_sum - The sum of the “ISS_MAPPED_PP” column for the group
withr::with_options(list(ISAnalytics.widgets = FALSE), {
    aggregated_meta <- aggregate_metadata(association_file,
        grouping_keys = c(
            "SubjectID", "CellMarker",
            "Tissue", "TimePoint"
        ),
        import_stats = TRUE
    )
})
#> [1] "--- REPORT IMPORT VISPA2STATS: FILES IMPORTED ---"
#> # A tibble: 4 x 4
#>   ProjectID  
#>   <chr>      
#> 1 CLOEXP     
#> 2 PROJECT1100
#> 3 PROJECT1100
#> 4 PROJECT1101
#>   stats_path                                                                    
#>   <fs::path>                                                                    
#> 1 C:/Users/biocbuild/bbs-3.12-bioc/tmpdir/RtmpMJAXdJ/fs/VA/CLOEXP/iss/POOL6-1   
#> 2 C:/Users/biocbuild/bbs-3.12-bioc/tmpdir/RtmpMJAXdJ/fs/PROJECT1100/iss/ABX-LR-~
#> 3 C:/Users/biocbuild/bbs-3.12-bioc/tmpdir/RtmpMJAXdJ/fs/PROJECT1100/iss/ABX-LR-~
#> 4 C:/Users/biocbuild/bbs-3.12-bioc/tmpdir/RtmpMJAXdJ/fs/PROJECT1101/iss/ABY-LR-~
#>   stats_files                                                                   
#>   <fs::path>                                                                    
#> 1 C:/Users/biocbuild/bbs-3.12-bioc/tmpdir/RtmpMJAXdJ/fs/VA/CLOEXP/iss/POOL6-1/s~
#> 2 C:/Users/biocbuild/bbs-3.12-bioc/tmpdir/RtmpMJAXdJ/fs/PROJECT1100/iss/ABX-LR-~
#> 3 C:/Users/biocbuild/bbs-3.12-bioc/tmpdir/RtmpMJAXdJ/fs/PROJECT1100/iss/ABX-LR-~
#> 4 C:/Users/biocbuild/bbs-3.12-bioc/tmpdir/RtmpMJAXdJ/fs/PROJECT1101/iss/ABY-LR-~
#>   Imported
#>   <lgl>   
#> 1 TRUE    
#> 2 TRUE    
#> 3 TRUE    
#> 4 TRUE
AggregateMeta SubjectID CellMarker Tissue TimePoint FusionPrimerPCRDate_min LinearPCRDate_min VCN_avg DNAngUsed_avg Kapa_avg DNAngUsed_sum ulForPool_sum BARCODE_MUX_sum TRIMMING_FINAL_LTRLC_sum LV_MAPPED_sum BWA_MAPPED_OVERALL_sum ISS_MAPPED_PP_sum
VA2020-mix10_NA_NA_0000 VA2020-mix10 NA NA 0000 NA NA 0.70 100 26.54667 300 25.70 19489792 19455728 8457580 10476016 9544692
VA2020-mix11_NA_NA_0000 VA2020-mix11 NA NA 0000 NA NA 2.80 100 59.81000 300 12.29 34294696 34199968 13941856 19007548 14986620
VA2020-mix12_NA_NA_0000 VA2020-mix12 NA NA 0000 NA NA 4.30 100 79.80333 300 7.98 23364272 23274392 11694244 10524788 7752560
VA2020-mix13_NA_NA_0000 VA2020-mix13 NA NA 0000 NA NA 9.89 100 85.87000 300 7.46 24345956 24249196 9936136 13188028 9321144
VA2020-mix14_NA_NA_0000 VA2020-mix14 NA NA 0000 NA NA NaN NaN NaN 0 0.00 1161066 1156914 484480 620925 438088

If you have the option ISAnalytics.widgets set to TRUE, this will produce a report in HTML format that tells you which stats files were imported. To avoid this, you can set the option to FALSE.

3 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.

3.1 Typical workflow

Import your association file (see previous section) and then import your matrices:

withr::with_options(list(ISAnalytics.widgets = FALSE), {
    matrices <- import_parallel_Vispa2Matrices_auto(
        association_file = association_file, root = NULL,
        quantification_type = c("fragmentEstimate", "seqCount"),
        matrix_type = "annotated", workers = 2, patterns = NULL,
        matching_opt = "ANY", multi_quant_matrix = FALSE
    )
})
#> [1] "--- REPORT: FILES IMPORTED ---"
#> # A tibble: 8 x 5
#>   ProjectID   concatenatePoolIDSeqRun Quantification_type
#>   <chr>       <chr>                   <chr>              
#> 1 CLOEXP      POOL6-1                 fragmentEstimate   
#> 2 PROJECT1100 ABX-LR-PL5-POOL14-1     fragmentEstimate   
#> 3 PROJECT1100 ABX-LR-PL6-POOL15-1     fragmentEstimate   
#> 4 PROJECT1101 ABY-LR-PL4-POOL54-2     fragmentEstimate   
#> 5 CLOEXP      POOL6-1                 seqCount           
#> 6 PROJECT1100 ABX-LR-PL5-POOL14-1     seqCount           
#> 7 PROJECT1100 ABX-LR-PL6-POOL15-1     seqCount           
#> 8 PROJECT1101 ABY-LR-PL4-POOL54-2     seqCount           
#>   Files_chosen                                                                  
#>   <fs::path>                                                                    
#> 1 C:/Users/biocbuild/bbs-3.12-bioc/tmpdir/RtmpMJAXdJ/fs/VA/CLOEXP/quantificatio~
#> 2 C:/Users/biocbuild/bbs-3.12-bioc/tmpdir/RtmpMJAXdJ/fs/PROJECT1100/quantificat~
#> 3 C:/Users/biocbuild/bbs-3.12-bioc/tmpdir/RtmpMJAXdJ/fs/PROJECT1100/quantificat~
#> 4 C:/Users/biocbuild/bbs-3.12-bioc/tmpdir/RtmpMJAXdJ/fs/PROJECT1101/quantificat~
#> 5 C:/Users/biocbuild/bbs-3.12-bioc/tmpdir/RtmpMJAXdJ/fs/VA/CLOEXP/quantificatio~
#> 6 C:/Users/biocbuild/bbs-3.12-bioc/tmpdir/RtmpMJAXdJ/fs/PROJECT1100/quantificat~
#> 7 C:/Users/biocbuild/bbs-3.12-bioc/tmpdir/RtmpMJAXdJ/fs/PROJECT1100/quantificat~
#> 8 C:/Users/biocbuild/bbs-3.12-bioc/tmpdir/RtmpMJAXdJ/fs/PROJECT1101/quantificat~
#>   Imported
#>   <lgl>   
#> 1 TRUE    
#> 2 TRUE    
#> 3 TRUE    
#> 4 TRUE    
#> 5 TRUE    
#> 6 TRUE    
#> 7 TRUE    
#> 8 TRUE    
#> [1] "--- SUMMARY OF FILES CHOSEN FOR IMPORT ---"
#> # A tibble: 8 x 4
#>   ProjectID   concatenatePoolIDSeqRun Quantification_type
#>   <chr>       <chr>                   <chr>              
#> 1 CLOEXP      POOL6-1                 fragmentEstimate   
#> 2 CLOEXP      POOL6-1                 seqCount           
#> 3 PROJECT1100 ABX-LR-PL5-POOL14-1     fragmentEstimate   
#> 4 PROJECT1100 ABX-LR-PL5-POOL14-1     seqCount           
#> 5 PROJECT1100 ABX-LR-PL6-POOL15-1     fragmentEstimate   
#> 6 PROJECT1100 ABX-LR-PL6-POOL15-1     seqCount           
#> 7 PROJECT1101 ABY-LR-PL4-POOL54-2     fragmentEstimate   
#> 8 PROJECT1101 ABY-LR-PL4-POOL54-2     seqCount           
#>   Files_chosen                                                                  
#>   <fs::path>                                                                    
#> 1 C:/Users/biocbuild/bbs-3.12-bioc/tmpdir/RtmpMJAXdJ/fs/VA/CLOEXP/quantificatio~
#> 2 C:/Users/biocbuild/bbs-3.12-bioc/tmpdir/RtmpMJAXdJ/fs/VA/CLOEXP/quantificatio~
#> 3 C:/Users/biocbuild/bbs-3.12-bioc/tmpdir/RtmpMJAXdJ/fs/PROJECT1100/quantificat~
#> 4 C:/Users/biocbuild/bbs-3.12-bioc/tmpdir/RtmpMJAXdJ/fs/PROJECT1100/quantificat~
#> 5 C:/Users/biocbuild/bbs-3.12-bioc/tmpdir/RtmpMJAXdJ/fs/PROJECT1100/quantificat~
#> 6 C:/Users/biocbuild/bbs-3.12-bioc/tmpdir/RtmpMJAXdJ/fs/PROJECT1100/quantificat~
#> 7 C:/Users/biocbuild/bbs-3.12-bioc/tmpdir/RtmpMJAXdJ/fs/PROJECT1101/quantificat~
#> 8 C:/Users/biocbuild/bbs-3.12-bioc/tmpdir/RtmpMJAXdJ/fs/PROJECT1101/quantificat~
#> [1] "--- INTEGRATION MATRICES FOUND REPORT ---"
#> # A tibble: 8 x 5
#>   ProjectID   concatenatePoolIDSeqRun Anomalies Quantification_type
#>   <chr>       <chr>                   <lgl>     <chr>              
#> 1 CLOEXP      POOL6-1                 FALSE     fragmentEstimate   
#> 2 CLOEXP      POOL6-1                 FALSE     seqCount           
#> 3 PROJECT1100 ABX-LR-PL5-POOL14-1     FALSE     fragmentEstimate   
#> 4 PROJECT1100 ABX-LR-PL5-POOL14-1     FALSE     seqCount           
#> 5 PROJECT1100 ABX-LR-PL6-POOL15-1     FALSE     fragmentEstimate   
#> 6 PROJECT1100 ABX-LR-PL6-POOL15-1     FALSE     seqCount           
#> 7 PROJECT1101 ABY-LR-PL4-POOL54-2     FALSE     fragmentEstimate   
#> 8 PROJECT1101 ABY-LR-PL4-POOL54-2     FALSE     seqCount           
#>   Files_found                                                                   
#>   <fs::path>                                                                    
#> 1 C:/Users/biocbuild/bbs-3.12-bioc/tmpdir/RtmpMJAXdJ/fs/VA/CLOEXP/quantificatio~
#> 2 C:/Users/biocbuild/bbs-3.12-bioc/tmpdir/RtmpMJAXdJ/fs/VA/CLOEXP/quantificatio~
#> 3 C:/Users/biocbuild/bbs-3.12-bioc/tmpdir/RtmpMJAXdJ/fs/PROJECT1100/quantificat~
#> 4 C:/Users/biocbuild/bbs-3.12-bioc/tmpdir/RtmpMJAXdJ/fs/PROJECT1100/quantificat~
#> 5 C:/Users/biocbuild/bbs-3.12-bioc/tmpdir/RtmpMJAXdJ/fs/PROJECT1100/quantificat~
#> 6 C:/Users/biocbuild/bbs-3.12-bioc/tmpdir/RtmpMJAXdJ/fs/PROJECT1100/quantificat~
#> 7 C:/Users/biocbuild/bbs-3.12-bioc/tmpdir/RtmpMJAXdJ/fs/PROJECT1101/quantificat~
#> 8 C:/Users/biocbuild/bbs-3.12-bioc/tmpdir/RtmpMJAXdJ/fs/PROJECT1101/quantificat~

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

# Takes the whole list and produces a list in output
aggregated_matrices <- aggregate_values_by_key(matrices, association_file)

# Takes a single matrix and produces a single matrix as output
aggregated_matrices_single <- aggregate_values_by_key(
    matrices$seqCount,
    association_file
)
chr integration_locus strand GeneName GeneStrand SubjectID CellMarker Tissue TimePoint Value_sum
1 4519734 + LINC01777 + VA2020-mix10 NA NA 0000 8
1 8426336 - RERE - VA2020-mix10 NA NA 0000 4
1 8884086 - RERE - VA2020-mix11 NA NA 0000 4
1 11322005 - MTOR - VA2020-mix10 NA NA 0000 4
1 11772847 + DRAXIN + VA2020-mix11 NA NA 0000 52
1 22453532 + WNT4 - VA2020-mix10 NA NA 0000 8

3.1.1 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 = matrices$seqCount,
    association_file = association_file,
    key = c("SubjectID", "ProjectID")
)
chr integration_locus strand GeneName GeneStrand SubjectID ProjectID Value_sum
1 4519734 + LINC01777 + VA2020-mix10 CLOEXP 2
1 4519734 + LINC01777 + VA2020-mix10 PROJECT1100 4
1 4519734 + LINC01777 + VA2020-mix10 PROJECT1101 2
1 8426336 - RERE - VA2020-mix10 CLOEXP 1
1 8426336 - RERE - VA2020-mix10 PROJECT1100 2
1 8426336 - RERE - VA2020-mix10 PROJECT1101 1
  1. Changing the lambda value
    The lambda parameter indicates the function(s) to be applied to the values for aggregation. lambda must be a named list of either functions or purrr-style lambdas: if you would like to specify additional parameters to the function the second option is recommended. The only important note on functions is that they should perform some kind of aggregation on numeric values: this means in practical terms they need to accept a vector of numeric/integer values as input and produce a SINGLE value as output. Valid options for this purpose might be: sum, mean, median, min, max and so on.
agg2 <- aggregate_values_by_key(
    x = matrices$seqCount,
    association_file = association_file,
    key = "SubjectID",
    lambda = list(mean = ~ mean(.x, na.rm = TRUE))
)
chr integration_locus strand GeneName GeneStrand SubjectID Value_mean
1 4519734 + LINC01777 + VA2020-mix10 2
1 8426336 - RERE - VA2020-mix10 1
1 8884086 - RERE - VA2020-mix11 1
1 11322005 - MTOR - VA2020-mix10 1
1 11772847 + DRAXIN + VA2020-mix11 13
1 22453532 + WNT4 - VA2020-mix10 2

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 = matrices$seqCount,
    association_file = association_file,
    key = "SubjectID",
    lambda = list(describe = psych::describe)
)

agg3
#> # A tibble: 1,272 x 7
#>    chr   integration_locus strand GeneName GeneStrand SubjectID Value_describe$~
#>    <chr>             <int> <chr>  <chr>    <chr>      <chr>                <dbl>
#>  1 1               4519734 +      LINC017~ +          VA2020-m~                1
#>  2 1               8426336 -      RERE     -          VA2020-m~                1
#>  3 1               8884086 -      RERE     -          VA2020-m~                1
#>  4 1              11322005 -      MTOR     -          VA2020-m~                1
#>  5 1              11772847 +      DRAXIN   +          VA2020-m~                1
#>  6 1              22453532 +      WNT4     -          VA2020-m~                1
#>  7 1              25929388 -      MAN1C1   +          VA2020-m~                1
#>  8 1              26950561 -      RPS6KA1  +          VA2020-m~                1
#>  9 1              29116798 -      YTHDF2   +          VA2020-m~                1
#> 10 1              30825358 -      LINC016~ -          VA2020-m~                1
#> # ... with 1,262 more rows
  1. 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”.
## Obtaining multi-quantification matrix
comp <- comparison_matrix(matrices)

agg4 <- aggregate_values_by_key(
    x = comp,
    association_file = association_file,
    key = "SubjectID",
    lambda = list(sum = sum, mean = mean),
    value_cols = c("seqCount", "fragmentEstimate")
)
chr integration_locus strand GeneName GeneStrand SubjectID seqCount_sum seqCount_mean fragmentEstimate_sum fragmentEstimate_mean
1 4519734 + LINC01777 + VA2020-mix10 8 2 8.010711 2.002678
1 8426336 - RERE - VA2020-mix10 4 1 4.002137 1.000534
1 8884086 - RERE - VA2020-mix11 4 1 4.004477 1.001119
1 11322005 - MTOR - VA2020-mix10 4 1 4.002002 1.000501
1 11772847 + DRAXIN + VA2020-mix11 52 13 4.000695 1.000174
1 22453532 + WNT4 - VA2020-mix10 8 2 4.005724 1.001431
  1. 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 = matrices$seqCount,
    association_file = association_file,
    key = "SubjectID",
    lambda = list(sum = sum, mean = mean),
    group = c(mandatory_IS_vars())
)
chr integration_locus strand SubjectID Value_sum Value_mean
1 4519734 + VA2020-mix10 8 2
1 8426336 - VA2020-mix10 4 1
1 8884086 - VA2020-mix11 4 1
1 11322005 - VA2020-mix10 4 1
1 11772847 + VA2020-mix11 52 13
1 22453532 + VA2020-mix10 8 2

4 Reproducibility

The ISAnalytics package (calabrialab, 2021) was made possible thanks to:

This package was developed using biocthis.

R session information.

#> - Session info -------------------------------------------------------------------------------------------------------
#>  setting  value                       
#>  version  R version 4.0.5 (2021-03-31)
#>  os       Windows Server 2012 R2 x64  
#>  system   x86_64, mingw32             
#>  ui       RTerm                       
#>  language (EN)                        
#>  collate  C                           
#>  ctype    English_United States.1252  
#>  tz       America/New_York            
#>  date     2021-04-08                  
#> 
#> - Packages -----------------------------------------------------------------------------------------------------------
#>  package       * version date       lib source        
#>  assertthat      0.2.1   2019-03-21 [2] CRAN (R 4.0.4)
#>  BiocManager     1.30.12 2021-03-28 [2] CRAN (R 4.0.4)
#>  BiocParallel    1.24.1  2021-04-08 [2] Bioconductor  
#>  BiocStyle     * 2.18.1  2021-04-08 [2] Bioconductor  
#>  bookdown        0.21    2020-10-13 [2] CRAN (R 4.0.5)
#>  bslib           0.2.4   2021-01-25 [2] CRAN (R 4.0.4)
#>  cellranger      1.1.0   2016-07-27 [2] CRAN (R 4.0.5)
#>  cli             2.4.0   2021-04-05 [2] CRAN (R 4.0.5)
#>  colorspace      2.0-0   2020-11-11 [2] CRAN (R 4.0.5)
#>  crayon          1.4.1   2021-02-08 [2] CRAN (R 4.0.4)
#>  data.table      1.14.0  2021-02-21 [2] CRAN (R 4.0.5)
#>  DBI             1.1.1   2021-01-15 [2] CRAN (R 4.0.4)
#>  debugme         1.1.0   2017-10-22 [2] CRAN (R 4.0.5)
#>  digest          0.6.27  2020-10-24 [2] CRAN (R 4.0.4)
#>  dplyr           1.0.5   2021-03-05 [2] CRAN (R 4.0.4)
#>  ellipsis        0.3.1   2020-05-15 [2] CRAN (R 4.0.4)
#>  evaluate        0.14    2019-05-28 [2] CRAN (R 4.0.4)
#>  fansi           0.4.2   2021-01-15 [2] CRAN (R 4.0.4)
#>  forcats         0.5.1   2021-01-27 [2] CRAN (R 4.0.5)
#>  fs              1.5.0   2020-07-31 [2] CRAN (R 4.0.4)
#>  generics        0.1.0   2020-10-31 [2] CRAN (R 4.0.4)
#>  ggplot2         3.3.3   2020-12-30 [2] CRAN (R 4.0.5)
#>  ggrepel         0.9.1   2021-01-15 [2] CRAN (R 4.0.5)
#>  glue            1.4.2   2020-08-27 [2] CRAN (R 4.0.4)
#>  gtable          0.3.0   2019-03-25 [2] CRAN (R 4.0.5)
#>  highr           0.8     2019-03-20 [2] CRAN (R 4.0.4)
#>  hms             1.0.0   2021-01-13 [2] CRAN (R 4.0.5)
#>  htmltools       0.5.1.1 2021-01-22 [2] CRAN (R 4.0.4)
#>  htmlwidgets     1.5.3   2020-12-10 [2] CRAN (R 4.0.4)
#>  httr            1.4.2   2020-07-20 [2] CRAN (R 4.0.4)
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5 Bibliography

This vignette was generated using BiocStyle (Oles, Morgan, and Huber, 2021) with knitr (Xie, 2021) and rmarkdown (Allaire, Xie, McPherson, Luraschi, et al., 2021) running behind the scenes.

Citations made with knitcitations (Boettiger, 2021).

[1] J. Allaire, Y. Xie, J. McPherson, J. Luraschi, et al. rmarkdown: Dynamic Documents for R. R package version 2.7. 2021. <URL: https://github.com/rstudio/rmarkdown>.

[2] C. Boettiger. knitcitations: Citations for ‘Knitr’ Markdown Files. R package version 1.0.12. 2021. <URL: https://CRAN.R-project.org/package=knitcitations>.

[3] G. Csárdi, R. core, H. Wickham, W. Chang, et al. sessioninfo: R Session Information. R package version 1.1.1. 2018. <URL: https://CRAN.R-project.org/package=sessioninfo>.

[4] A. Oles, M. Morgan, and W. Huber. BiocStyle: Standard styles for vignettes and other Bioconductor documents. R package version 2.18.1. 2021. <URL: https://github.com/Bioconductor/BiocStyle>.

[5] R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. Vienna, Austria, 2021. <URL: https://www.R-project.org/>.

[6] H. Wickham. “testthat: Get Started with Testing”. In: The R Journal 3 (2011), pp. 5-10. <URL: https://journal.r-project.org/archive/2011-1/RJournal_2011-1_Wickham.pdf>.

[7] Y. Xie. knitr: A General-Purpose Package for Dynamic Report Generation in R. R package version 1.31. 2021. <URL: https://yihui.org/knitr/>.

[8] calabrialab. Analyze gene therapy vector insertion sites data identified from genomics next generation sequencing reads for clonal tracking studies. https://github.com/calabrialab/ISAnalytics - R package version 1.0.11. 2021. DOI: 10.18129/B9.bioc.ISAnalytics. <URL: http://www.bioconductor.org/packages/ISAnalytics>.