@@ -72,8 +72,6 @@

 #' package = "ISAnalytics")}

 #' * \code{vignette("aggregate_function_usage",

 #' package = "ISAnalytics")}

-#' * \code{vignette("report_system",

-#' package = "ISAnalytics")}

 #' * \code{vignette("sharing_analyses",

 #' package = "ISAnalytics")}

 #'

@@ -748,10 +748,10 @@ sample_statistics <- function(x, metadata,

 #' ## Genomic annotation file

 #' This file is a data base, or more simply a .tsv file to import, with

 #' genes annotation for the specific genome. The annotations for the

-#' human genome (hg19) and murine genome (mm9 and mm10) are already

+#' human genome (hg19) and murine genome (mm9) are already

 #' included in this package: to use one of them just

-#' set the argument `genomic_annotation_file` to either `"hg19"`,

-#' `"mm9"` or `"mm10"`.

+#' set the argument `genomic_annotation_file` to either `"hg19"` or

+#' `"mm9"`.

 #' If for any reason the user is performing an analysis on another genome,

 #' this file needs to be changed respecting the USCS Genome Browser

 #' format, meaning the input file headers should include:

@@ -803,7 +803,7 @@ CIS_grubbs <- function(x,

     # Check other parameters

     stopifnot(is.character(genomic_annotation_file))

     genomic_annotation_file <- genomic_annotation_file[1]

-    if (genomic_annotation_file %in% c("hg19", "mm9", "mm10")) {

+    if (genomic_annotation_file %in% c("hg19", "mm9")) {

         gen_file <- paste0("refGenes_", genomic_annotation_file)

         utils::data(list = gen_file, envir = rlang::current_env())

         refgenes <- rlang::eval_tidy(rlang::sym(gen_file))

@@ -64,9 +64,6 @@

 #' @describeIn refGenes_hg19 Data frame for murine mm9 genome

 #' @usage data("refGenes_mm9")

 "refGenes_mm9"

-#' @describeIn refGenes_hg19 Data frame for murine mm10 genome

-#' @usage data("refGenes_mm10")

-"refGenes_mm10"

 #' Data frames for proto-oncogenes (human and mouse)

 #' amd tumor-suppressor genes from UniProt.

@@ -39,8 +39,6 @@ navbar:

         href: articles/collision_removal.html

       - text: How to use import functions

         href: articles/how_to_import_functions.html

-      - text: ISAnalytics report system

-        href: articles/report_system.html

       - text: Sharing analyses with ISAnalytics

         href: articles/sharing_analyses.html

     release-v:

@@ -131,6 +129,5 @@ reference:

   - proto_oncogenes

   - refGenes_hg19

   - refGenes_mm9

-  - refGenes_mm10

   - tumor_suppressors

deleted file mode 100644

Binary files a/data/refGenes_mm10.RData and /dev/null differ

@@ -46,10 +46,10 @@ this paper:

 This file is a data base, or more simply a .tsv file to import, with

 genes annotation for the specific genome. The annotations for the

-human genome (hg19) and murine genome (mm9 and mm10) are already

+human genome (hg19) and murine genome (mm9) are already

 included in this package: to use one of them just

-set the argument \code{genomic_annotation_file} to either \code{"hg19"},

-\code{"mm9"} or \code{"mm10"}.

+set the argument \code{genomic_annotation_file} to either \code{"hg19"} or

+\code{"mm9"}.

 If for any reason the user is performing an analysis on another genome,

 this file needs to be changed respecting the USCS Genome Browser

 format, meaning the input file headers should include:

@@ -81,35 +81,35 @@ For more details on how this files were generated use the help

 The default values are included in this package and

 it can be accessed by doing:\if{html}{\out{<div class="r">}}\preformatted{head(known_clinical_oncogenes())

-}\if{html}{\out{</div>}}\preformatted{## # A tibble: 5 × 2

-##   GeneName KnownClonalExpansion

-##   <chr>    <lgl>               

-## 1 MECOM    TRUE                

-## 2 CCND2    TRUE                

-## 3 TAL1     TRUE                

-## 4 LMO2     TRUE                

-## 5 HMGA2    TRUE

-}

+#> # A tibble: 5 × 2

+#>   GeneName KnownClonalExpansion

+#>   <chr>    <lgl>               

+#> 1 MECOM    TRUE                

+#> 2 CCND2    TRUE                

+#> 3 TAL1     TRUE                

+#> 4 LMO2     TRUE                

+#> 5 HMGA2    TRUE

+}\if{html}{\out{</div>}}

 If the user wants to change this parameter the input data frame must

 preserve the column structure. The same goes for the \code{suspicious_genes}

 parameter (DOIReference column is optional):\if{html}{\out{<div class="r">}}\preformatted{head(clinical_relevant_suspicious_genes())

-}\if{html}{\out{</div>}}\preformatted{## # A tibble: 6 × 3

-##   GeneName ClinicalRelevance DOIReference                                

-##   <chr>    <lgl>             <chr>                                       

-## 1 DNMT3A   TRUE              https://doi.org/10.1182/blood-2018-01-829937

-## 2 TET2     TRUE              https://doi.org/10.1182/blood-2018-01-829937

-## 3 ASXL1    TRUE              https://doi.org/10.1182/blood-2018-01-829937

-## 4 JAK2     TRUE              https://doi.org/10.1182/blood-2018-01-829937

-## 5 CBL      TRUE              https://doi.org/10.1182/blood-2018-01-829937

-## 6 TP53     TRUE              https://doi.org/10.1182/blood-2018-01-829937

-}

+#> # A tibble: 6 × 3

+#>   GeneName ClinicalRelevance DOIReference                                

+#>   <chr>    <lgl>             <chr>                                       

+#> 1 DNMT3A   TRUE              https://doi.org/10.1182/blood-2018-01-829937

+#> 2 TET2     TRUE              https://doi.org/10.1182/blood-2018-01-829937

+#> 3 ASXL1    TRUE              https://doi.org/10.1182/blood-2018-01-829937

+#> 4 JAK2     TRUE              https://doi.org/10.1182/blood-2018-01-829937

+#> 5 CBL      TRUE              https://doi.org/10.1182/blood-2018-01-829937

+#> 6 TP53     TRUE              https://doi.org/10.1182/blood-2018-01-829937

+}\if{html}{\out{</div>}}

 }

 }

 \examples{

 data("integration_matrices", package = "ISAnalytics")

 cis_plot <- CIS_volcano_plot(integration_matrices,

-  title_prefix = "PJ01"

+    title_prefix = "PJ01"

 )

 cis_plot

 }

@@ -33,18 +33,18 @@ Plot of the estimated HSC population size for each patient.

 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")

+    x = integration_matrices,

+    association_file = association_file,

+    value_cols = c("seqCount", "fragmentEstimate")

 )

 aggreg_meta <- aggregate_metadata(

-  association_file = association_file

+    association_file = association_file

 )

 estimate <- HSC_population_size_estimate(

-  x = aggreg,

-  metadata = aggreg_meta,

-  stable_timepoints = c(90, 180, 360),

-  cell_type = "Other"

+    x = aggreg,

+    metadata = aggreg_meta,

+    stable_timepoints = c(90, 180, 360),

+    cell_type = "Other"

 )

 p <- HSC_population_plot(estimate, "PJ01")

 p

@@ -85,16 +85,16 @@ distinct non-zero time points.

 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")

+    x = integration_matrices,

+    association_file = association_file,

+    value_cols = c("seqCount", "fragmentEstimate")

 )

 aggreg_meta <- aggregate_metadata(association_file = association_file)

 estimate <- HSC_population_size_estimate(

-  x = aggreg,

-  metadata = aggreg_meta,

-  stable_timepoints = c(90, 180, 360),

-  cell_type = "Other"

+    x = aggreg,

+    metadata = aggreg_meta,

+    stable_timepoints = c(90, 180, 360),

+    cell_type = "Other"

 )

 }

 \concept{Population estimates}

@@ -105,8 +105,6 @@ and Annotation of Vector Integration Sites}

 package = "ISAnalytics")}

 \item \code{vignette("aggregate_function_usage",

 package = "ISAnalytics")}

-\item \code{vignette("report_system",

-package = "ISAnalytics")}

 \item \code{vignette("sharing_analyses",

 package = "ISAnalytics")}

 }

@@ -40,7 +40,7 @@ summary of info for each group. For more details on how to use this function:

 \examples{

 data("association_file", package = "ISAnalytics")

 aggreg_meta <- aggregate_metadata(

-  association_file = association_file

+    association_file = association_file

 )

 head(aggreg_meta)

 }

@@ -92,9 +92,9 @@ will be added to the final data frame.

 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")

+    x = integration_matrices,

+    association_file = association_file,

+    value_cols = c("seqCount", "fragmentEstimate")

 )

 head(aggreg)

 }

@@ -73,16 +73,16 @@ otherwise an error message is thrown.

 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")

+    x = integration_matrices,

+    association_file = association_file,

+    value_cols = c("seqCount", "fragmentEstimate")

 )

 by_subj <- aggreg \%>\%

-  dplyr::group_by(.data$SubjectID) \%>\%

-  dplyr::group_split()

+    dplyr::group_by(.data$SubjectID) \%>\%

+    dplyr::group_split()

 circos_genomic_density(by_subj,

-  track_colors = c("navyblue", "gold"),

-  grDevice = "default", track.height = 0.1

+    track_colors = c("navyblue", "gold"),

+    grDevice = "default", track.height = 0.1

 )

 }

 }

@@ -47,16 +47,16 @@ of reference.

 fs_path <- system.file("extdata", "fs.zip", package = "ISAnalytics")

 fs <- unzip_file_system(fs_path, "fs")

 af_path <- system.file("extdata", "asso.file.tsv.gz",

-  package = "ISAnalytics"

+    package = "ISAnalytics"

 )

 af <- import_association_file(af_path,

-  root = fs,

-  import_iss = FALSE,

-  report_path = NULL

+    root = fs,

+    import_iss = FALSE,

+    report_path = NULL

 )

 matrices <- import_parallel_Vispa2Matrices(af,

-  c("seqCount", "fragmentEstimate"),

-  mode = "AUTO", report_path = NULL, multi_quant_matrix = FALSE

+    c("seqCount", "fragmentEstimate"),

+    mode = "AUTO", report_path = NULL, multi_quant_matrix = FALSE

 )

 multi_quant <- comparison_matrix(matrices)

 head(multi_quant)

@@ -49,9 +49,9 @@ column) will be produced.

 \examples{

 data("integration_matrices", package = "ISAnalytics")

 abund <- compute_abundance(

-  x = integration_matrices,

-  columns = "fragmentEstimate",

-  key = "CompleteAmplificationID"

+    x = integration_matrices,

+    columns = "fragmentEstimate",

+    key = "CompleteAmplificationID"

 )

 head(abund)

 }

@@ -83,7 +83,7 @@ all quantification matrices.

 \examples{

 data("integration_matrices", package = "ISAnalytics")

 rec <- compute_near_integrations(

-  x = integration_matrices, map_as_file = FALSE

+    x = integration_matrices, map_as_file = FALSE

 )

 head(rec)

 }

@@ -77,9 +77,9 @@ the chosen column to avoid undesired results.

 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")

+    x = integration_matrices,

+    association_file = association_file,

+    value_cols = c("seqCount", "fragmentEstimate")

 )

 cumulative_count <- cumulative_count_union(aggreg)

 cumulative_count

@@ -43,9 +43,9 @@ time point "t+1".

 data("integration_matrices", package = "ISAnalytics")

 data("association_file", package = "ISAnalytics")

 aggreg <- aggregate_values_by_key(

-  x = rlang::current_env()$integration_matrices,

-  association_file = rlang::current_env()$association_file,

-  value_cols = c("seqCount", "fragmentEstimate")

+    x = rlang::current_env()$integration_matrices,

+    association_file = rlang::current_env()$association_file,

+    value_cols = c("seqCount", "fragmentEstimate")

 )

 cumulated_is <- cumulative_is(aggreg)

 cumulated_is

@@ -45,16 +45,16 @@ file has been aligned with the file system

 fs_path <- system.file("extdata", "fs.zip", package = "ISAnalytics")

 fs <- unzip_file_system(fs_path, "fs")

 af_path <- system.file("extdata", "asso.file.tsv.gz",

-  package = "ISAnalytics"

+    package = "ISAnalytics"

 )

 af <- import_association_file(af_path,

-  root = fs,

-  import_iss = FALSE,

-  report_path = NULL

+    root = fs,

+    import_iss = FALSE,

+    report_path = NULL

 )

 stats_files <- import_Vispa2_stats(af,

-  join_with_af = FALSE,

-  report_path = NULL

+    join_with_af = FALSE,

+    report_path = NULL

 )

 head(stats_files)

 }

@@ -69,16 +69,16 @@ For more details see the "How to use import functions" vignette:

 fs_path <- system.file("extdata", "fs.zip", package = "ISAnalytics")

 fs <- unzip_file_system(fs_path, "fs")

 af_path <- system.file("extdata", "asso.file.tsv.gz",

-  package = "ISAnalytics"

+    package = "ISAnalytics"

 )

 af <- import_association_file(af_path,

-  root = fs,

-  import_iss = FALSE,

-  report_path = NULL

+    root = fs,

+    import_iss = FALSE,

+    report_path = NULL

 )

 matrices <- import_parallel_Vispa2Matrices(af,

-  c("seqCount", "fragmentEstimate"),

-  mode = "AUTO", report_path = NULL

+    c("seqCount", "fragmentEstimate"),

+    mode = "AUTO", report_path = NULL

 )

 head(matrices)

 }

@@ -39,11 +39,11 @@ For more details see the "How to use import functions" vignette:

 fs_path <- system.file("extdata", "fs.zip", package = "ISAnalytics")

 fs <- unzip_file_system(fs_path, "fs")

 matrix_path <- fs::path(

-  fs,

-  "PJ01",

-  "quantification",

-  "POOL01-1",

-  "PJ01_POOL01-1_seqCount_matrix.no0.annotated.tsv.gz"

+    fs,

+    "PJ01",

+    "quantification",

+    "POOL01-1",

+    "PJ01_POOL01-1_seqCount_matrix.no0.annotated.tsv.gz"

 )

 matrix <- import_single_Vispa2Matrix(matrix_path)

 head(matrix)

@@ -72,21 +72,21 @@ as transparent in the strata.

 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")

+    x = integration_matrices,

+    association_file = association_file,

+    value_cols = c("seqCount", "fragmentEstimate")

 )

 abund <- compute_abundance(x = aggreg)

 alluvial_plots <- integration_alluvial_plot(abund,

-  alluvia_plot_y_threshold = 0.5

+    alluvia_plot_y_threshold = 0.5

 )

 ex_plot <- alluvial_plots[[1]]$plot +

-  ggplot2::labs(

-    title = "IS distribution over time",

-    subtitle = "Patient 1, MNC BM",

-    y = "Abundance (\%)",

-    x = "Time point (days after GT)"

-  )

+    ggplot2::labs(

+        title = "IS distribution over time",

+        subtitle = "Patient 1, MNC BM",

+        y = "Abundance (\%)",

+        x = "Time point (days after GT)"

+    )

 print(ex_plot)

 }

 \seealso{

@@ -81,9 +81,9 @@ function \code{\link{sharing_heatmap}} or via the function

 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")

+    x = integration_matrices,

+    association_file = association_file,

+    value_cols = c("seqCount", "fragmentEstimate")

 )

 sharing <- is_sharing(aggreg)

 sharing

@@ -34,8 +34,8 @@ Filter out outliers in metadata.

 \examples{

 data("association_file", package = "ISAnalytics")

 filtered_af <- outlier_filter(association_file,

-  key = "BARCODE_MUX",

-  report_path = NULL

+    key = "BARCODE_MUX",

+    report_path = NULL

 )

 head(filtered_af)

 }

@@ -94,7 +94,7 @@ in sequence.

 \examples{

 data("association_file", package = "ISAnalytics")

 flagged <- outliers_by_pool_fragments(association_file,

-  report_path = NULL

+    report_path = NULL

 )

 head(flagged)

 }

@@ -35,17 +35,17 @@ For more details on how to use collision removal functionality:

 data("integration_matrices", package = "ISAnalytics")

 data("association_file", package = "ISAnalytics")

 separated <- separate_quant_matrices(

-  integration_matrices

+    integration_matrices

 )

 no_coll <- remove_collisions(

-  x = separated$seqCount,

-  association_file = association_file,

-  quant_cols = c(seqCount = "Value"),

-  report_path = NULL

+    x = separated$seqCount,

+    association_file = association_file,

+    quant_cols = c(seqCount = "Value"),

+    report_path = NULL

 )

 realigned <- realign_after_collisions(

-  sc_matrix = no_coll,

-  other_matrices = list(fragmentEstimate = separated$fragmentEstimate)

+    sc_matrix = no_coll,

+    other_matrices = list(fragmentEstimate = separated$fragmentEstimate)

 )

 realigned

 }

@@ -4,21 +4,16 @@

 \name{refGenes_hg19}

 \alias{refGenes_hg19}

 \alias{refGenes_mm9}

-\alias{refGenes_mm10}

 \title{Gene annotation files for hg19, mm9 and mm10.}

 \format{

 An object of class \code{tbl_df} (inherits from \code{tbl}, \code{data.frame}) with 27275 rows and 12 columns.

 An object of class \code{tbl_df} (inherits from \code{tbl}, \code{data.frame}) with 24487 rows and 12 columns.

-

-An object of class \code{tbl_df} (inherits from \code{tbl}, \code{data.frame}) with 24627 rows and 11 columns.

 }

 \usage{

 data("refGenes_hg19")

 data("refGenes_mm9")

-

-data("refGenes_mm10")

 }

 \description{

 This file was obtained following this steps:

@@ -46,8 +41,6 @@ ORDER BY kg.chrom ASC , kg.txStart ASC

 \section{Functions}{

 \itemize{

 \item \code{refGenes_mm9}: Data frame for murine mm9 genome

-

-\item \code{refGenes_mm10}: Data frame for murine mm10 genome

 }}

 \keyword{datasets}

@@ -54,9 +54,9 @@ For more details refer to the vignette "Collision removal functionality":

 data("integration_matrices", package = "ISAnalytics")

 data("association_file", package = "ISAnalytics")

 no_coll <- remove_collisions(

-  x = integration_matrices,

-  association_file = association_file,

-  report_path = NULL

+    x = integration_matrices,

+    association_file = association_file,

+    report_path = NULL

 )

 head(no_coll)

 }

@@ -64,9 +64,9 @@ sample_key = c("SubjectID", "CellMarker","Tissue", "TimePoint"))

 data("integration_matrices", package = "ISAnalytics")

 data("association_file", package = "ISAnalytics")

 stats <- sample_statistics(

-  x = integration_matrices,

-  metadata = association_file,

-  value_columns = c("seqCount", "fragmentEstimate")

+    x = integration_matrices,

+    metadata = association_file,

+    value_columns = c("seqCount", "fragmentEstimate")

 )

 stats

 }

@@ -48,7 +48,7 @@ quantification matrices as a named list of tibbles.

 \examples{

 data("integration_matrices", package = "ISAnalytics")

 separated <- separate_quant_matrices(

-  integration_matrices

+    integration_matrices

 )

 separated

 }

@@ -53,13 +53,13 @@ Displays the IS sharing calculated via \link{is_sharing} as heatmaps.

 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")

+    x = integration_matrices,

+    association_file = association_file,

+    value_cols = c("seqCount", "fragmentEstimate")

 )

 sharing <- is_sharing(aggreg,

-  minimal = FALSE,

-  include_self_comp = TRUE

+    minimal = FALSE,

+    include_self_comp = TRUE

 )

 sharing_heatmaps <- sharing_heatmap(sharing_df = sharing)

 sharing_heatmaps$absolute

@@ -40,9 +40,9 @@ for more detail on this.

 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")

+    x = integration_matrices,

+    association_file = association_file,

+    value_cols = c("seqCount", "fragmentEstimate")

 )

 sharing <- is_sharing(aggreg, n_comp = 3, table_for_venn = TRUE)

 venn_tbls <- sharing_venn(sharing, row_range = 1:3, euler = FALSE)

@@ -78,7 +78,8 @@ It is also possible to filter different data frames with different

 sets of conditions. Besides having the possibility of defining the

 other parameters as simple vector, which has the same results as

 operating on an unnamed list, the user can define the parameters as

-named lists containing vectors. For example:\if{html}{\out{<div class="r">}}\preformatted{example_df <- tibble::tibble(a = c(20, 30, 40),

+named lists containing vectors. For example:\if{html}{\out{<div class="r">}}\preformatted{

+example_df <- tibble::tibble(a = c(20, 30, 40),

                              b = c(40, 50, 60),

                              c = c("a", "b", "c"),

                              d = c(3L, 4L, 5L))

@@ -86,30 +87,31 @@ example_list <- list(first = example_df,

                      second = example_df,

                      third = example_df)

 print(example_list)

-}\if{html}{\out{</div>}}\preformatted{## $first

-## # A tibble: 3 × 4

-##       a     b c         d

-##   <dbl> <dbl> <chr> <int>

-## 1    20    40 a         3

-## 2    30    50 b         4

-## 3    40    60 c         5

-## 

-## $second

-## # A tibble: 3 × 4

-##       a     b c         d

-##   <dbl> <dbl> <chr> <int>

-## 1    20    40 a         3

-## 2    30    50 b         4

-## 3    40    60 c         5

-## 

-## $third

-## # A tibble: 3 × 4

-##       a     b c         d

-##   <dbl> <dbl> <chr> <int>

-## 1    20    40 a         3

-## 2    30    50 b         4

-## 3    40    60 c         5

-}\if{html}{\out{<div class="r">}}\preformatted{filtered <- threshold_filter(example_list,

+#> $first

+#> # A tibble: 3 × 4

+#>       a     b c         d

+#>   <dbl> <dbl> <chr> <int>

+#> 1    20    40 a         3

+#> 2    30    50 b         4

+#> 3    40    60 c         5

+#> 

+#> $second

+#> # A tibble: 3 × 4

+#>       a     b c         d

+#>   <dbl> <dbl> <chr> <int>

+#> 1    20    40 a         3

+#> 2    30    50 b         4

+#> 3    40    60 c         5

+#> 

+#> $third

+#> # A tibble: 3 × 4

+#>       a     b c         d

+#>   <dbl> <dbl> <chr> <int>

+#> 1    20    40 a         3

+#> 2    30    50 b         4

+#> 3    40    60 c         5

+

+filtered <- threshold_filter(example_list,

 threshold = list(first = c(20, 60),

 third = c(25)),

 cols_to_compare = list(first = c("a", "b"),

@@ -117,27 +119,27 @@ third = c("a")),

 comparators = list(first = c(">", "<"),

 third = c(">=")))

 print(filtered)

-}\if{html}{\out{</div>}}\preformatted{## $first

-## # A tibble: 1 × 4

-##       a     b c         d

-##   <dbl> <dbl> <chr> <int>

-## 1    30    50 b         4

-## 

-## $second

-## # A tibble: 3 × 4

-##       a     b c         d

-##   <dbl> <dbl> <chr> <int>

-## 1    20    40 a         3

-## 2    30    50 b         4

-## 3    40    60 c         5

-## 

-## $third

-## # A tibble: 2 × 4

-##       a     b c         d

-##   <dbl> <dbl> <chr> <int>

-## 1    30    50 b         4

-## 2    40    60 c         5

-}

+#> $first

+#> # A tibble: 1 × 4

+#>       a     b c         d

+#>   <dbl> <dbl> <chr> <int>

+#> 1    30    50 b         4

+#> 

+#> $second

+#> # A tibble: 3 × 4

+#>       a     b c         d

+#>   <dbl> <dbl> <chr> <int>

+#> 1    20    40 a         3

+#> 2    30    50 b         4

+#> 3    40    60 c         5

+#> 

+#> $third

+#> # A tibble: 2 × 4

+#>       a     b c         d

+#>   <dbl> <dbl> <chr> <int>

+#> 1    30    50 b         4

+#> 2    40    60 c         5

+}\if{html}{\out{</div>}}

 The above signature will roughly be translated as:

 \itemize{

@@ -176,30 +178,30 @@ be filtered

 }

 \examples{

 example_df <- tibble::tibble(

-  a = c(20, 30, 40),

-  b = c(40, 50, 60),

-  c = c("a", "b", "c"),

-  d = c(3L, 4L, 5L)

+    a = c(20, 30, 40),

+    b = c(40, 50, 60),

+    c = c("a", "b", "c"),

+    d = c(3L, 4L, 5L)

 )

 example_list <- list(

-  first = example_df,

-  second = example_df,

-  third = example_df

+    first = example_df,

+    second = example_df,

+    third = example_df

 )

 filtered <- threshold_filter(example_list,

-  threshold = list(

-    first = c(20, 60),

-    third = c(25)

-  ),

-  cols_to_compare = list(

-    first = c("a", "b"),

-    third = c("a")

-  ),

-  comparators = list(

-    first = c(">", "<"),

-    third = c(">=")

-  )

+    threshold = list(

+        first = c(20, 60),

+        third = c(25)

+    ),

+    cols_to_compare = list(

+        first = c("a", "b"),

+        third = c("a")

+    ),

+    comparators = list(

+        first = c(">", "<"),

+        third = c(">=")

+    )

 )

 filtered

 }

@@ -64,9 +64,9 @@ is required. To visualize the resulting object:\preformatted{gridExtra::grid.arr

 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")

+    x = integration_matrices,

+    association_file = association_file,

+    value_cols = c("seqCount", "fragmentEstimate")

 )

 abund <- compute_abundance(x = aggreg)

 grob <- top_abund_tableGrob(abund)

@@ -50,24 +50,24 @@ by passing a vector of column names or passing 2 "shortcuts":

 }

 \examples{

 smpl <- tibble::tibble(

-  chr = c("1", "2", "3", "4", "5", "6"),

-  integration_locus = c(14536, 14544, 14512, 14236, 14522, 14566),

-  strand = c("+", "+", "-", "+", "-", "+"),

-  CompleteAmplificationID = c("ID1", "ID2", "ID1", "ID1", "ID3", "ID2"),

-  Value = c(3, 10, 40, 2, 15, 150),

-  Value2 = c(456, 87, 87, 9, 64, 96),

-  Value3 = c("a", "b", "c", "d", "e", "f")

+    chr = c("1", "2", "3", "4", "5", "6"),

+    integration_locus = c(14536, 14544, 14512, 14236, 14522, 14566),

+    strand = c("+", "+", "-", "+", "-", "+"),

+    CompleteAmplificationID = c("ID1", "ID2", "ID1", "ID1", "ID3", "ID2"),

+    Value = c(3, 10, 40, 2, 15, 150),

+    Value2 = c(456, 87, 87, 9, 64, 96),

+    Value3 = c("a", "b", "c", "d", "e", "f")

 )

 top <- top_integrations(smpl,

-  n = 3,

-  columns = c("Value", "Value2"),

-  keep = "nothing"

+    n = 3,

+    columns = c("Value", "Value2"),

+    keep = "nothing"

 )

 top_key <- top_integrations(smpl,

-  n = 3,

-  columns = "Value",

-  keep = "Value2",

-  key = "CompleteAmplificationID"

+    n = 3,

+    columns = "Value",

+    keep = "Value2",

+    key = "CompleteAmplificationID"

 )

 }

 \seealso{

@@ -78,7 +78,7 @@ A default is already supplied:

 ```{r echo=FALSE}

 library(ISAnalytics)

-knitr::kable(default_meta_agg()) 

+print(default_meta_agg(), width = Inf)

 ```

 You can either provide purrr-style lambdas (as given in the example above),

@@ -102,10 +102,7 @@ aggregated_meta <- aggregate_metadata(association_file = association_file)

 ```

 ```{r echo=FALSE}

-DT::datatable(aggregated_meta, 

-              rownames = FALSE,

-              options = list(dom = "t", 

-                             scrollX = 120))

+print(aggregated_meta)

 ```

 # Aggregation of values by key

@@ -131,10 +128,7 @@ aggreg <- aggregate_values_by_key(

 ```

 ```{r echo=FALSE}

-DT::datatable(head(aggreg),

-              rownames = FALSE,

-              options = list(dom = "t",

-                             scrollX = 120))

+print(aggreg, width = Inf)

 ```

 The function `aggregate_values_by_key` can perform the aggregation both on the 

@@ -164,10 +158,7 @@ agg1 <- aggregate_values_by_key(

 ```

 ```{r echo=FALSE}

-DT::datatable(head(agg1),

-              rownames = FALSE,

-              options = list(dom = "t",

-                             scrollX = 120))

+print(agg1, width = Inf)

 ```

 2. **Changing the `lambda` value**  

@@ -193,10 +184,7 @@ agg2 <- aggregate_values_by_key(

 ```

 ```{r echo=FALSE}

-DT::datatable(head(agg2),

-              rownames = FALSE,

-              options = list(dom = "t",

-                             scrollX = 120))

+print(agg2, width = Inf)

 ```

 Note that, when specifying purrr-style lambdas (formulas), the first 

@@ -217,10 +205,7 @@ agg3 <- aggregate_values_by_key(

 ```

 ```{r echo=FALSE}

-DT::datatable(head(agg3),

-              rownames = FALSE,

-              options = list(dom = "t",

-                             scrollX = 120))