Package 'ISAnalytics'

Description

Groups metadata by the specified grouping keys and returns a summary of info for each group. For more details on how to use this function: vignette("workflow_start", package = "ISAnalytics")

Usage

aggregate_metadata(
  association_file,
  grouping_keys = c("SubjectID", "CellMarker", "Tissue", "TimePoint"),
  aggregating_functions = default_meta_agg(),
  import_stats = lifecycle::deprecated()
)

Arguments

Value

An aggregated data frame

See Also

Other Data cleaning and pre-processing: aggregate_values_by_key(), compute_near_integrations(), default_meta_agg(), outlier_filter(), outliers_by_pool_fragments(), purity_filter(), realign_after_collisions(), remove_collisions(), threshold_filter()

Examples

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

Description

Performs aggregation on values contained in the integration matrices based on the key and the specified lambda. For more details on how to use this function: vignette("workflow_start", package = "ISAnalytics")

Usage

aggregate_values_by_key(
  x,
  association_file,
  value_cols = "Value",
  key = c("SubjectID", "CellMarker", "Tissue", "TimePoint"),
  lambda = list(sum = ~sum(.x, na.rm = TRUE)),
  group = c(mandatory_IS_vars(), annotation_IS_vars()),
  join_af_by = "CompleteAmplificationID"
)

Arguments

Details

Setting the lambda parameter

The lambda parameter should always contain a named list of either functions or purrr-style lambdas. It is also possible to specify the namespace of the function in both ways, for example:

Using purrr-style lambdas allows to specify arguments for the functions, keeping in mind that the first parameter should always be .x:

It is also possible to use custom user-defined functions, keeping in mind that the symbol will be evaluated in the calling environment, for example if the function is called in the global environment and lambda contains "foo" as a function, "foo" will be evaluated in the global environment.

Constraints on aggregation functions

Functions passed in the lambda parameters must respect a few constraints to properly work and it's the user responsibility to ensure this.

• Functions have to accept as input a numeric or integer vector

• Function should return a single value or a list/data frame: if a list or a data frame is returned as a result, all the columns will be added to the final data frame.

Value

A list of data frames or a single data frame aggregated according to the specified arguments

See Also

Other Data cleaning and pre-processing: aggregate_metadata(), compute_near_integrations(), default_meta_agg(), outlier_filter(), outliers_by_pool_fragments(), purity_filter(), realign_after_collisions(), remove_collisions(), threshold_filter()

Examples

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")
)
head(aggreg)

Description

This helper function checks if each individual integration site, identified by the mandatory_IS_vars(), has been annotated with two or more distinct gene symbols.

Usage

annotation_issues(matrix)

Arguments

Value

Either NULL if no issues were detected or 1 or more data frames with genomic coordinates of the IS and the number of distinct genes associated

See Also

Other Import functions helpers: date_formats(), default_af_transform(), default_iss_file_prefixes(), matching_options(), quantification_types()

Examples

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

Description

This function is particularly useful when a sparse matrix structure is needed by a specific function (mainly from other packages).

Usage

as_sparse_matrix(
  x,
  single_value_col = "Value",
  fragmentEstimate = "fragmentEstimate",
  seqCount = "seqCount",
  barcodeCount = "barcodeCount",
  cellCount = "cellCount",
  ShsCount = "ShsCount",
  key = pcr_id_column()
)

Arguments

Value

Depending on input, 2 possible outputs:

• A single sparse matrix (data frame) if input is a single quantification matrix

• A list of sparse matrices divided by quantification if input is a single multi-quantification matrix or a list of matrices

See Also

Other Utilities: comparison_matrix(), enable_progress_bars(), export_ISA_settings(), generate_Vispa2_launch_AF(), generate_blank_association_file(), generate_default_folder_structure(), import_ISA_settings(), separate_quant_matrices(), transform_columns()

Examples

data("integration_matrices", package = "ISAnalytics")
sparse <- as_sparse_matrix(integration_matrices)

Description

This file is a simple example of association file. Use it as reference to properly fill out yours. To generate an empty association file to fill see the generate_blank_association_file() function.

Usage

data("association_file")

Format

An object of class data.table (inherits from data.frame) with 53 rows and 83 columns.

Details

The data was obtained manually by simulating real research data.

See Also

generate_blank_association_file

A character vector containing all the names of the currently supported outliers tests that can be called in the function outlier_filter.

Description

A character vector containing all the names of the currently supported outliers tests that can be called in the function outlier_filter.

Usage

available_outlier_tests()

Value

A character vector

Examples

available_outlier_tests()

Description

Contains all information associated with critical tags used in the dynamic vars system. To know more see vignette("workflow_start", package="ISAnalytics").

Usage

available_tags()

Value

A data frame

Examples

available_tags()

Description

A default table with info relative to different blood lineages associated with cell markers that can be supplied as a parameter to HSC_population_size_estimate

Usage

blood_lineages_default()

Value

A data frame

Examples

blood_lineages_default()

Description

For this functionality the suggested package circlize is required. Please note that this function is a simple wrapper of basic circlize functions, for an in-depth explanation on how the functions work and additional arguments please refer to the official documentation Circular Visualization in R

Usage

circos_genomic_density(
  data,
  gene_labels = NULL,
  label_col = NULL,
  cytoband_specie = "hg19",
  track_colors = "navyblue",
  grDevice = c("png", "pdf", "svg", "jpeg", "bmp", "tiff", "default"),
  file_path = getwd(),
  ...
)

Arguments

Details

Providing genomic labels

If genomic labels should be plotted alongside genomic density tracks, the user should provide them as a simple data frame in standard bed format, namely chr, start, end plus a column containing the labels. NOTE: if the user decides to plot on the default device (viewer in RStudio), he must ensure there is enough space for all elements to be plotted, otherwise an error message is thrown.

Value

NULL

See Also

Other Plotting functions: CIS_volcano_plot(), HSC_population_plot(), fisher_scatterplot(), integration_alluvial_plot(), sharing_heatmap(), sharing_venn(), top_abund_tableGrob(), top_cis_overtime_heatmap()

Examples

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")
)
by_subj <- aggreg |>
    dplyr::group_by(.data$SubjectID) |>
    dplyr::group_split()
circos_genomic_density(by_subj,
    track_colors = c("navyblue", "gold"),
    grDevice = "default", track.height = 0.1
)

Description

Statistical approach for the validation of common insertion sites significance based on the comparison of the integration frequency at the CIS gene with respect to other genes contained in the surrounding genomic regions. For more details please refer to this paper: https://ashpublications.org/blood/article/117/20/5332/21206/Lentiviral-vector-common-integration-sites-in

Usage

CIS_grubbs(
  x,
  genomic_annotation_file = "hg19",
  grubbs_flanking_gene_bp = 1e+05,
  threshold_alpha = 0.05,
  by = NULL,
  return_missing_as_df = TRUE,
  results_as_list = TRUE
)

Arguments

Details

Genomic annotation file

A data frame containing genes annotation for the specific genome. From version ⁠1.5.4⁠ the argument genomic_annotation_file accepts only data frames or package provided defaults. The user is responsible for importing the appropriate tabular files if customization is needed. The annotations for the 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" 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:

name2, chrom, strand, min_txStart, max_txEnd, minmax_TxLen, average_TxLen, name, min_cdsStart, max_cdsEnd, minmax_CdsLen, average_CdsLen

Value

A data frame

Required tags

The function will explicitly check for the presence of these tags:

• chromosome

• locus

• is_strand

• gene_symbol

• gene_strand

See Also

Other Analysis functions: HSC_population_size_estimate(), compute_abundance(), cumulative_is(), gene_frequency_fisher(), is_sharing(), iss_source(), sample_statistics(), top_integrations(), top_targeted_genes()

Examples

data("integration_matrices", package = "ISAnalytics")
cis <- CIS_grubbs(integration_matrices)
cis

Description

Computes common insertion sites and Grubbs test for each separate group and separating different time points among the same group. The logic applied is the same as the function CIS_grubbs().

Usage

CIS_grubbs_overtime(
  x,
  genomic_annotation_file = "hg19",
  grubbs_flanking_gene_bp = 1e+05,
  threshold_alpha = 0.05,
  group = "SubjectID",
  timepoint_col = "TimePoint",
  as_df = TRUE,
  return_missing_as_df = TRUE,
  max_workers = NULL
)

Arguments

Details

Genomic annotation file

A data frame containing genes annotation for the specific genome. From version ⁠1.5.4⁠ the argument genomic_annotation_file accepts only data frames or package provided defaults. The user is responsible for importing the appropriate tabular files if customization is needed. The annotations for the 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" 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:

name2, chrom, strand, min_txStart, max_txEnd, minmax_TxLen, average_TxLen, name, min_cdsStart, max_cdsEnd, minmax_CdsLen, average_CdsLen

Value

A list with results and optionally missing genes info

Examples

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")
)
cis_overtime <- CIS_grubbs_overtime(aggreg)
cis_overtime

Description

Traces a volcano plot for IS frequency and CIS results.

Usage

CIS_volcano_plot(
  x,
  onco_db_file = "proto_oncogenes",
  tumor_suppressors_db_file = "tumor_suppressors",
  species = "human",
  known_onco = known_clinical_oncogenes(),
  suspicious_genes = clinical_relevant_suspicious_genes(),
  significance_threshold = 0.05,
  annotation_threshold_ontots = 0.1,
  highlight_genes = NULL,
  title_prefix = NULL,
  return_df = FALSE
)

Arguments

Details

Input data frame

Users can supply as x either a simple integration matrix or a data frame resulting from the call to CIS_grubbs. In the first case an internal call to the function CIS_grubbs() is performed.

Oncogene and tumor suppressor genes files

These files are included in the package for user convenience and are simply UniProt files with gene annotations for human and mouse. For more details on how this files were generated use the help ?tumor_suppressors, ?proto_oncogenes

Known oncogenes

The default values are included in this package and it can be accessed by doing:

If the user wants to change this parameter the input data frame must preserve the column structure. The same goes for the suspicious_genes parameter (DOIReference column is optional):

Value

A plot or a list containing a plot and a data frame

Required tags

The function will explicitly check for the presence of these tags:

• gene_symbol

See Also

Other Plotting functions: HSC_population_plot(), circos_genomic_density(), fisher_scatterplot(), integration_alluvial_plot(), sharing_heatmap(), sharing_venn(), top_abund_tableGrob(), top_cis_overtime_heatmap()

Examples

data("integration_matrices", package = "ISAnalytics")
cis_plot <- CIS_volcano_plot(integration_matrices,
    title_prefix = "PJ01"
)
cis_plot

Description

Clinical relevant suspicious genes (for mouse and human).

Usage

clinical_relevant_suspicious_genes()

Value

A data frame

See Also

Other Plotting function helpers: known_clinical_oncogenes()

Examples

clinical_relevant_suspicious_genes()

Description

Takes a list of integration matrices referring to different quantification types and merges them into a single data frame with multiple value columns, each renamed according to their quantification type of reference.

Usage

comparison_matrix(
  x,
  fragmentEstimate = "fragmentEstimate",
  seqCount = "seqCount",
  barcodeCount = "barcodeCount",
  cellCount = "cellCount",
  ShsCount = "ShsCount",
  value_col_name = "Value"
)

Arguments

Value

A single data frame

See Also

quantification_types

Other Utilities: as_sparse_matrix(), enable_progress_bars(), export_ISA_settings(), generate_Vispa2_launch_AF(), generate_blank_association_file(), generate_default_folder_structure(), import_ISA_settings(), separate_quant_matrices(), transform_columns()

Examples

sc <- tibble::tribble(
    ~chr, ~integration_locus, ~strand, ~CompleteAmplificationID, ~Value,
    "1", 45324, "+", "ID1", 543,
    "2", 52423, "-", "ID1", 42,
    "6", 54623, "-", "ID2", 67,
    "X", 12314, "+", "ID3", 8
)
fe <- tibble::tribble(
    ~chr, ~integration_locus, ~strand, ~CompleteAmplificationID, ~Value,
    "1", 45324, "+", "ID1", 56.76,
    "2", 52423, "-", "ID1", 78.32,
    "6", 54623, "-", "ID2", 123.45,
    "X", 12314, "+", "ID3", 5.34
)
comparison_matrix(list(
    fragmentEstimate = fe,
    seqCount = sc
))

Description

Abundance is obtained for every integration event by calculating the ratio between the single value and the total value for the given group.

Usage

compute_abundance(
  x,
  columns = c("fragmentEstimate_sum"),
  percentage = TRUE,
  key = c("SubjectID", "CellMarker", "Tissue", "TimePoint"),
  keep_totals = FALSE
)

Arguments

Details

Abundance will be computed upon the user selected columns in the columns parameter. For each column a corresponding relative abundance column (and optionally a percentage abundance column) will be produced.

Value

Either a single data frame with computed abundance values or a list of 2 data frames (abundance_df, quant_totals)

Required tags

The function will explicitly check for the presence of these tags:

• All columns declared in mandatory_IS_vars()

See Also

Other Analysis functions: CIS_grubbs(), HSC_population_size_estimate(), cumulative_is(), gene_frequency_fisher(), is_sharing(), iss_source(), sample_statistics(), top_integrations(), top_targeted_genes()

Examples

data("integration_matrices", package = "ISAnalytics")
abund <- compute_abundance(
    x = integration_matrices,
    columns = "fragmentEstimate",
    key = "CompleteAmplificationID"
)
head(abund)

Description

This function scans the input integration matrix to detect eventual integration sites that are too "near" to each other and merges them into single integration sites adjusting their values if needed.

Usage

compute_near_integrations(
  x,
  threshold = 4,
  is_identity_tags = c("chromosome", "is_strand"),
  keep_criteria = c("max_value", "keep_first"),
  value_columns = c("seqCount", "fragmentEstimate"),
  max_value_column = "seqCount",
  sample_id_column = pcr_id_column(),
  additional_agg_lambda = list(.default = default_rec_agg_lambdas()),
  max_workers = 4,
  map_as_file = TRUE,
  file_path = default_report_path(),
  strand_specific = lifecycle::deprecated()
)

Arguments

Details

The concept of "near"

An integration event is uniquely identified by all fields specified in the mandatory_IS_vars() look-up table. It can happen to find IS that are formally distinct (different combination of values in the fields), but that should not considered distinct in practice, since they represent the same integration event - this may be due to artefacts at the putative locus of the IS in the merging of multiple sequencing libraries.

We say that an integration event IS1 is near to another integration event IS2 if the absolute difference of their loci is strictly lower than the set threshold.

The IS identity

There is also another aspect to be considered. Since the algorithm is based on a sliding window mechanism, on which groups of IS should we set and slide the window?

By default, we have 3 fields in the mandatory_IS_vars(): chr, integration_locus, strand, and we assume that all the fields contribute to the identity of the IS. This means that IS1 and IS2 can be compared only if they have the same chromosome and the same strand. However, if we would like to exclude the strand of the integration from our considerations then IS1 and IS2 can be selected from all the events that fall on the same chromosome. A practical example:

IS1 = ⁠(chr = "1", strand = "+", integration_locus = 14568)⁠

IS2 = ⁠(chr = "1", strand = "-", integration_locus = 14567)⁠

if is_identity_tags = c("chromosome", "is_strand") IS1 and IS2 are considered distinct because they differ in strand, therefore no correction will be applied to loci of either of the 2. If is_identity_tags = c("chromosome") then IS1 and IS2 are considered near, because the strand is irrelevant, hence one of the 2 IS will change locus.

Aggregating near IS

IS that fall in the same interval are evaluated according to the criterion selected - if recalibration is necessary, rows with the same sample ID are aggregated in a single row with a quantification value that is the sum of all the merged rows.

If the input integration matrix contains annotation columns, that is additional columns that are not

• part of the mandatory IS vars (see mandatory_IS_vars())

• part of the annotation IS vars (see annotation_IS_vars())

• the sample identifier column

• the quantification column

it is possible to specify how they should be aggregated. Defaults are provided for each column type (character, integer, numeric...), but custom functions can be specified as a named list, where names are column names in x and values are functions to be applied. NOTE: functions must be purrr-style lambdas and they must perform some kind of aggregating operation, aka they must take a vector as input and return a single value. The type of the output should match the type of the target column. If you specify custom lambdas, provide defaults in the special element .defaults. Example:

list(
  numeric_col = ~ sum(.x),
  char_col = ~ paste0(.x, collapse = ", "),
  .defaults = default_rec_agg_lambdas()
)

Value

An integration matrix with same or less number of rows

Required tags

The function will explicitly check for the presence of these tags:

• chromosome

• locus

• is_strand

• gene_symbol

Note

We do recommend to use this function in combination with comparison_matrix to automatically perform re-calibration on all quantification matrices.

See Also

Other Data cleaning and pre-processing: aggregate_metadata(), aggregate_values_by_key(), default_meta_agg(), outlier_filter(), outliers_by_pool_fragments(), purity_filter(), realign_after_collisions(), remove_collisions(), threshold_filter()

Examples

data("integration_matrices", package = "ISAnalytics")
rec <- compute_near_integrations(
    x = integration_matrices, map_as_file = FALSE
)
head(rec)

Description

Given an input integration matrix that can be grouped over time, this function adds integrations in groups assuming that if an integration is observed at time point "t" then it is also observed in time point "t+1".

Usage

cumulative_is(
  x,
  key = c("SubjectID", "CellMarker", "Tissue", "TimePoint"),
  timepoint_col = "TimePoint",
  include_tp_zero = FALSE,
  counts = TRUE,
  keep_og_is = FALSE,
  expand = TRUE
)

Arguments

Value

A data frame

Required tags

The function will explicitly check for the presence of these tags:

• All columns declared in mandatory_IS_vars()

• Checks if the matrix is annotated by assessing presence of annotation_IS_vars()

See Also

Other Analysis functions: CIS_grubbs(), HSC_population_size_estimate(), compute_abundance(), gene_frequency_fisher(), is_sharing(), iss_source(), sample_statistics(), top_integrations(), top_targeted_genes()

Examples

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")
)
cumulated_is <- cumulative_is(aggreg)
cumulated_is

Description

All options correspond to lubridate functions, see more in the dedicated package documentation.

Usage

date_formats()

Value

A character vector

See Also

import_association_file, import_parallel_Vispa2Matrices_auto

Other Import functions helpers: annotation_issues(), default_af_transform(), default_iss_file_prefixes(), matching_options(), quantification_types()

Examples

date_formats()

Description

A list of default transformations to apply to the association file columns after importing it via import_association_file()

Usage

default_af_transform(convert_tp)

Arguments

Value

A named list of lambdas

See Also

Other Import functions helpers: annotation_issues(), date_formats(), default_iss_file_prefixes(), matching_options(), quantification_types()

Examples

default_af_transform(TRUE)

Description

Note that each element is a regular expression.

Usage

default_iss_file_prefixes()

Value

A character vector of regexes

See Also

Other Import functions helpers: annotation_issues(), date_formats(), default_af_transform(), matching_options(), quantification_types()

Examples

default_iss_file_prefixes()

Description

A default columns-function specifications for aggregate_metadata

Usage

default_meta_agg()

Details

This data frame contains four columns:

• Column: holds the name of the column in the association file that should be processed

• Function: contains either the name of a function (e.g. mean) or a purrr-style lambda (e.g. ~ mean(.x, na.rm = TRUE)). This function will be applied to the corresponding column specified in Column

• Args: optional additional arguments to pass to the corresponding function. This is relevant ONLY if the corresponding Function is a simple function and not a purrr-style lambda.

• Output_colname: a glue specification that will be used to determine a unique output column name. See glue for more details.

Value

A data frame

See Also

Other Data cleaning and pre-processing: aggregate_metadata(), aggregate_values_by_key(), compute_near_integrations(), outlier_filter(), outliers_by_pool_fragments(), purity_filter(), realign_after_collisions(), remove_collisions(), threshold_filter()

Examples

default_meta_agg()

Description

Defaults for column aggregations in compute_near_integrations().

Usage

default_rec_agg_lambdas()

Value

A named list of lambdas

Examples

default_rec_agg_lambdas()

Description

Default folder for saving ISAnalytics reports. Supplied as default argument for several functions.

Usage

default_report_path()

Value

A path

Examples

default_report_path()

Description

A set of pre-defined functions for sample_statistics.

Usage

default_stats()

Value

A named list of functions/purrr-style lambdas

Examples

default_stats()

Description

This is a simple wrapper around functions from the package progressr. To customize the appearance of the progress bar, please refer to progressr documentation.

Usage

enable_progress_bars()

Value

NULL

See Also

Other Utilities: as_sparse_matrix(), comparison_matrix(), export_ISA_settings(), generate_Vispa2_launch_AF(), generate_blank_association_file(), generate_default_folder_structure(), import_ISA_settings(), separate_quant_matrices(), transform_columns()

Examples

enable_progress_bars()
progressr::handlers(global = FALSE) # Deactivate

Description

This function allows exporting the currently set dynamic vars in json format so it can be quickly imported later. Dynamic variables need to be properly set via the setter functions before calling the function. For more details, refer to the dedicated vignette vignette("workflow_start", package="ISAnalytics").

Usage

export_ISA_settings(folder, setting_profile_name)

Arguments

Value

NULL

See Also

Other Utilities: as_sparse_matrix(), comparison_matrix(), enable_progress_bars(), generate_Vispa2_launch_AF(), generate_blank_association_file(), generate_default_folder_structure(), import_ISA_settings(), separate_quant_matrices(), transform_columns()

Examples

tmp_folder <- tempdir()
export_ISA_settings(tmp_folder, "DEFAULT")

Description

Plots results of Fisher's exact test on gene frequency obtained via gene_frequency_fisher() as a scatterplot.

Usage

fisher_scatterplot(
  fisher_df,
  p_value_col = "Fisher_p_value_fdr",
  annot_threshold = 0.05,
  annot_color = "red",
  gene_sym_col = "GeneName",
  do_not_highlight = NULL,
  keep_not_highlighted = TRUE
)

Arguments

Details

Specifying genes to avoid highlighting

In some cases, users might want to avoid highlighting certain genes even if their p-value is below the threshold. To do so, use the argument do_not_highlight: character vectors are appropriate for specific genes that are to be excluded, expressions or lambdas allow a finer control. For example we can supply:

with this expression, genes that have a p-value < threshold and start with "MIR" or have an average_TxLen_1 lower than 300 are excluded from the highlighted points. NOTE: keep in mind that expressions are evaluated inside a dplyr::filter context.

Similarly, lambdas are passed to the filtering function but only operate on the column containing the gene symbol.

Value

A plot

See Also

Other Plotting functions: CIS_volcano_plot(), HSC_population_plot(), circos_genomic_density(), integration_alluvial_plot(), sharing_heatmap(), sharing_venn(), top_abund_tableGrob(), top_cis_overtime_heatmap()

Examples

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")
)
cis <- CIS_grubbs(aggreg, by = "SubjectID")
fisher <- gene_frequency_fisher(cis$cis$PT001, cis$cis$PT002,
    min_is_per_gene = 2
)
fisher_scatterplot(fisher)

Description

Provided 2 data frames with calculations for CIS, via CIS_grubbs(), computes Fisher's exact test. Results can be plotted via fisher_scatterplot().

Usage

gene_frequency_fisher(
  cis_x,
  cis_y,
  min_is_per_gene = 3,
  gene_set_method = c("intersection", "union"),
  onco_db_file = "proto_oncogenes",
  tumor_suppressors_db_file = "tumor_suppressors",
  species = "human",
  known_onco = known_clinical_oncogenes(),
  suspicious_genes = clinical_relevant_suspicious_genes(),
  significance_threshold = 0.05,
  remove_unbalanced_0 = TRUE
)

Arguments

Details

Oncogene and tumor suppressor genes files

These files are included in the package for user convenience and are simply UniProt files with gene annotations for human and mouse. For more details on how this files were generated use the help ?tumor_suppressors, ?proto_oncogenes

Known oncogenes

The default values are included in this package and it can be accessed by doing:

If the user wants to change this parameter the input data frame must preserve the column structure. The same goes for the suspicious_genes parameter (DOIReference column is optional):

Value

A data frame

Required tags

The function will explicitly check for the presence of these tags:

• gene_symbol

See Also

Other Analysis functions: CIS_grubbs(), HSC_population_size_estimate(), compute_abundance(), cumulative_is(), is_sharing(), iss_source(), sample_statistics(), top_integrations(), top_targeted_genes()

Examples

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")
)
cis <- CIS_grubbs(aggreg, by = "SubjectID")
fisher <- gene_frequency_fisher(cis$cis$PT001, cis$cis$PT002,
    min_is_per_gene = 2
)
fisher

Description

Produces a blank association file to start using both VISPA2 and ISAnalytics

Usage

generate_blank_association_file(path)

Arguments

Value

NULL

See Also

Other Utilities: as_sparse_matrix(), comparison_matrix(), enable_progress_bars(), export_ISA_settings(), generate_Vispa2_launch_AF(), generate_default_folder_structure(), import_ISA_settings(), separate_quant_matrices(), transform_columns()

Examples

temp <- tempfile()
generate_blank_association_file(temp)

Description

The function produces a folder structure in the file system at the provided path that respects VISPA2 standards, with package-included data.

Usage

generate_default_folder_structure(
  type = "correct",
  dir = tempdir(),
  af = "default",
  matrices = "default"
)

Arguments

Value

A named list containing the path to the association file and the path to the top level folder(s) of the structure

Required tags

The function will explicitly check for the presence of these tags:

• project_id

• tag_seq

• vispa_concatenate

See Also

Other Utilities: as_sparse_matrix(), comparison_matrix(), enable_progress_bars(), export_ISA_settings(), generate_Vispa2_launch_AF(), generate_blank_association_file(), import_ISA_settings(), separate_quant_matrices(), transform_columns()

Examples

fs_path <- generate_default_folder_structure(type = "correct")
fs_path

Description

The function selects the appropriate columns and prepares a file for the launch of VISPA2 pipeline for each project/pool pair specified.

Usage

generate_Vispa2_launch_AF(association_file, project, pool, path)

Arguments

Details

Note: the function is vectorized, meaning you can specify more than one project and more than one pool as vectors of characters, but you must ensure that:

• Both project and pool vectors have the same length

• You correclty type names in corresponding positions, for example c("PJ01", "PJ01") - c("POOL01", "POOL02"). If you type a pool in the position of a corresponding project that doesn't match no file will be produced since that pool doesn't exist in the corresponding project.

Value

NULL

Required tags

The function will explicitly check for the presence of these tags:

• cell_marker

• fusion_id

• pcr_repl_id

• pool_id

• project_id

• subject

• tag_id

• tissue

• tp_days

• vector_id

The names of the pools in the pool argument is checked against the column corresponding to the pool_id tag.

See Also

Other Utilities: as_sparse_matrix(), comparison_matrix(), enable_progress_bars(), export_ISA_settings(), generate_blank_association_file(), generate_default_folder_structure(), import_ISA_settings(), separate_quant_matrices(), transform_columns()

Examples

temp <- tempdir()
data("association_file", package = "ISAnalytics")
generate_Vispa2_launch_AF(association_file, "PJ01", "POOL01", temp)

Description

Plot of the estimated HSC population size for each patient.

Usage

HSC_population_plot(
  estimates,
  project_name,
  timepoints = "Consecutive",
  models = "Mth Chao (LB)"
)

Arguments

Value

A plot

See Also

Other Plotting functions: CIS_volcano_plot(), circos_genomic_density(), fisher_scatterplot(), integration_alluvial_plot(), sharing_heatmap(), sharing_venn(), top_abund_tableGrob(), top_cis_overtime_heatmap()

Examples

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")
)
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"
)
p <- HSC_population_plot(estimate$est, "PJ01")
p

Description

Hematopoietic stem cells population size estimate with capture-recapture models.

Usage

HSC_population_size_estimate(
  x,
  metadata,
  stable_timepoints = NULL,
  aggregation_key = c("SubjectID", "CellMarker", "Tissue", "TimePoint"),
  blood_lineages = blood_lineages_default(),
  timepoint_column = "TimePoint",
  seqCount_column = "seqCount_sum",
  fragmentEstimate_column = "fragmentEstimate_sum",
  seqCount_threshold = 3,
  fragmentEstimate_threshold = 3,
  nIS_threshold = 5,
  cell_type = "MYELOID",
  tissue_type = "PB",
  max_workers = 4
)

Arguments

Value

A data frame with the results of the estimates

Input formats

Both x and metadata should be supplied to the function in aggregated format (ideally through the use of aggregate_metadata and aggregate_values_by_key). Note that the aggregation_key, aka the vector of column names used for aggregation, must contain at least the columns associated with the tags subject, cell_marker, tissue and a time point column (the user can specify the name of the column in the argument timepoint_column).

Specifying more than one group

Groups for the estimates are computed as a pair of cell type and tissue. If the user wishes to compute estimates for more than one combination of cell type and tissue, it is possible to specify them as character vectors to the fields cell_type and tissue_type respectively, noting that:

• Vectors must have the same length or one of the 2 has to be of length 1

• It is a responsibility of the user to check whether the combination exists in the dataset provided.

Example: Note that estimates are computed individually for each group.

On time points

If stable_timepoints is a vector with length > 1, the function will look for the first available stable time point and slice the data from that time point onward. If NULL is supplied instead, it means there are no stable time points available. Note that 0 time points are ALWAYS discarded. Also, to be included in the analysis, a group must have at least 2 distinct non-zero time points. NOTE: the vector passed has to contain all individual time points, not just the minimum and maximum

Setting a threshold for fragment estimate

If fragment estimate is present in the input matrix, the filtering logic changes slightly: rows in the original matrix are kept if the sequence count value is greater or equal than the seqCount_threshold AND the fragment estimate value is greater or equal to the fragmentEstimate_threshold IF PRESENT (non-zero value). This means that for rows that miss fragment estimate, the filtering logic will be applied only on sequence count. If the user wishes not to use the combined filtering with fragment estimate, simply set fragmentEstimate_threshold = 0.

Required tags

The function will explicitly check for the presence of these tags:

• subject

• tissue

• cell_marker

See Also

Other Analysis functions: CIS_grubbs(), compute_abundance(), cumulative_is(), gene_frequency_fisher(), is_sharing(), iss_source(), sample_statistics(), top_integrations(), top_targeted_genes()

Examples

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")
)
aggreg_meta <- aggregate_metadata(association_file = association_file)
estimate <- HSC_population_size_estimate(
    x = aggreg,
    metadata = aggreg_meta,
    fragmentEstimate_column = NULL,
    stable_timepoints = c(90, 180, 360),
    cell_type = "Other"
)

Description

Imports the association file and optionally performs a check on the file system starting from the root to assess the alignment between the two.

Usage

import_association_file(
  path,
  root = NULL,
  dates_format = "ymd",
  separator = "\t",
  filter_for = NULL,
  import_iss = FALSE,
  convert_tp = TRUE,
  report_path = default_report_path(),
  transformations = default_af_transform(convert_tp),
  tp_padding = lifecycle::deprecated(),
  ...
)

Arguments

Details

Transformations

Lambdas provided in input in the transformations argument, must be transformations, aka functions that take in input a vector and return a vector of the same length as the input.

If the transformation list contains column names that are not present in the data frame, they are simply ignored.

File system alignment

If the root argument is set to NULL no file system alignment is performed. This allows to import the basic file but it won't be possible to perform automated matrix and stats import. For more details see the "How to use import functions" vignette: vignette("workflow_start", package = "ISAnalytics")

Time point conversion

The time point conversion is based on the following logic, given TPD is the column containing the time point expressed in days and TPM and TPY are respectively the time points expressed as month and years

• If TPD is NA –> NA (for both months and years)

• TPM = 0, TPY = 0 if and only if TPD = 0

For conversion in months:

• TPM = ceiling(TPD/30) if TPD < 30 otherwise TPM = round(TPD/30)

For conversion in years:

• TPY = ceiling(TPD/360)

Value

The data frame containing metadata

Required tags

The function will explicitly check for the presence of these tags:

• project_id

• pool_id

• tag_seq

• subject

• tissue

• tp_days

• cell_marker

• pcr_replicate

• vispa_concatenate

• pcr_repl_id

• proj_folder

The function will use all the available specifications contained in association_file_columns(TRUE) to read and parse the file. If the specifications contain columns with a type "date", the function will parse the generic date with the format in the dates_format argument.

See Also

transform_columns

date_formats

Other Import functions: import_Vispa2_stats(), import_parallel_Vispa2Matrices(), import_single_Vispa2Matrix()

Examples

fs_path <- generate_default_folder_structure(type = "correct")
af <- import_association_file(fs_path$af,
    root = fs_path$root,
    report_path = NULL
)
head(af)

Description

The function allows the import of an existing dynamic vars profile in json format. This is a quick and convenient way to set up the workflow, alternative to specifying lookup tables manually through the corresponding setter functions. For more details, refer to the dedicated vignette vignette("workflow_start", package="ISAnalytics").

Usage

import_ISA_settings(path)

Arguments

Value

NULL

See Also

Other Utilities: as_sparse_matrix(), comparison_matrix(), enable_progress_bars(), export_ISA_settings(), generate_Vispa2_launch_AF(), generate_blank_association_file(), generate_default_folder_structure(), separate_quant_matrices(), transform_columns()

Examples

tmp_folder <- tempdir()
export_ISA_settings(tmp_folder, "DEFAULT")
import_ISA_settings(fs::path(tmp_folder, "DEFAULT_ISAsettings.json"))
reset_dyn_vars_config()

Description

The function offers a convenient way of importing multiple integration matrices in an automated or semi-automated way. For more details see the "How to use import functions" vignette: vignette("workflow_start", package = "ISAnalytics")

Usage

import_parallel_Vispa2Matrices(
  association_file,
  quantification_type = c("seqCount", "fragmentEstimate"),
  matrix_type = c("annotated", "not_annotated"),
  workers = 2,
  multi_quant_matrix = TRUE,
  report_path = default_report_path(),
  patterns = NULL,
  matching_opt = matching_options(),
  mode = "AUTO",
  ...
)

Arguments

Value

Either a multi-quantification matrix or a list of integration matrices

Required tags

The function will explicitly check for the presence of these tags:

• project_id

• vispa_concatenate

See Also

Other Import functions: import_Vispa2_stats(), import_association_file(), import_single_Vispa2Matrix()

Examples

fs_path <- generate_default_folder_structure(type = "correct")
af <- import_association_file(fs_path$af,
    root = fs_path$root,
    report_path = NULL
)
matrices <- import_parallel_Vispa2Matrices(af,
    c("seqCount", "fragmentEstimate"),
    mode = "AUTO", report_path = NULL
)
head(matrices)

Description

This function allows to read and import an integration matrix (ideally produced by VISPA2) and converts it to a tidy format.

Usage

import_single_Vispa2Matrix(
  path,
  separator = "\t",
  additional_cols = NULL,
  transformations = NULL,
  sample_names_to = pcr_id_column(),
  values_to = "Value",
  to_exclude = lifecycle::deprecated(),
  keep_excluded = lifecycle::deprecated()
)

Arguments

Details

Additional columns

Additional columns are annotation columns present in the integration matrix to import that are not

• part of the mandatory IS vars (see mandatory_IS_vars())

• part of the annotation IS vars (see annotation_IS_vars())

• the sample identifier column

• the quantification column

When specified they tell the function how to treat those columns in the import phase, by providing a named character vector, where names correspond to the additional column names and values are a choice of the following:

• "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

• "_" to drop the column

For more details see the "How to use import functions" vignette: vignette("workflow_start", package = "ISAnalytics")

Transformations

Lambdas provided in input in the transformations argument, must be transformations, aka functions that take in input a vector and return a vector of the same length as the input.

If the transformation list contains column names that are not present in the data frame, they are simply ignored.

Value

A data frame object in tidy format

Required tags

The function will explicitly check for the presence of these tags:

• All columns declared in mandatory_IS_vars()

See Also

transform_columns

Other Import functions: import_Vispa2_stats(), import_association_file(), import_parallel_Vispa2Matrices()

Examples

fs_path <- generate_default_folder_structure(type = "correct")
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)
head(matrix)

Description

Imports all the Vispa2 stats files for each pool provided the association file has been aligned with the file system (see import_association_file).

Usage

import_Vispa2_stats(
  association_file,
  file_prefixes = default_iss_file_prefixes(),
  join_with_af = TRUE,
  pool_col = "concatenatePoolIDSeqRun",
  report_path = default_report_path()
)

Arguments

Value

A data frame

Required tags

The function will explicitly check for the presence of these tags:

• project_id

• tag_seq

• vispa_concatenate

• pcr_repl_id

See Also

Other Import functions: import_association_file(), import_parallel_Vispa2Matrices(), import_single_Vispa2Matrix()

Examples

fs_path <- generate_default_folder_structure(type = "correct")
af <- import_association_file(fs_path$af,
    root = fs_path$root,
    import_iss = FALSE,
    report_path = NULL
)
stats_files <- import_Vispa2_stats(af,
    join_with_af = FALSE,
    report_path = NULL
)
head(stats_files)

Description

Given one or multiple tags, prints the associated description and functions where the tag is explicitly used.

Usage

inspect_tags(tags)

Arguments

Value

NULL

See Also

Other dynamic vars: mandatory_IS_vars(), pcr_id_column(), reset_mandatory_IS_vars(), set_mandatory_IS_vars(), set_matrix_file_suffixes()

Examples

inspect_tags(c("chromosome", "project_id", "x"))

Description

Alluvial plots allow the visualization of integration sites distribution in different points in time in the same group. This functionality requires the suggested package ggalluvial.

Usage

integration_alluvial_plot(
  x,
  group = c("SubjectID", "CellMarker", "Tissue"),
  plot_x = "TimePoint",
  plot_y = "fragmentEstimate_sum_PercAbundance",
  alluvia = mandatory_IS_vars(),
  alluvia_plot_y_threshold = 1,
  top_abundant_tbl = TRUE,
  empty_space_color = "grey90",
  ...
)

Arguments

Details

Input data frame

The input data frame must contain all the columns specified in the arguments group, plot_x, plot_y and alluvia. The standard input for this function is the data frame obtained via the compute_abundance function.

Plotting threshold on y

The plotting threshold on the quantification on the y axis has the function to highlight only relevant information on the plot and reduce computation time. The default value is 1, that acts on the default column plotted on the y axis which contains a percentage value. This translates in natural language roughly as "highlight with colors only those integrations (alluvia) that at least in 1 point in time have an abundance value >= 1 %". The remaining integrations will be plotted as a unique layer in the column, colored as specified by the argument empty_space_color.

Customizing the plot

The returned plots are ggplot2 objects and can therefore further modified as any other ggplot2 object. For example, if the user decides to change the fill scale it is sufficient to do

NOTE: if you requested the computation of the top ten abundant tables and you want the colors to match you should re-compute them

A note on strata ordering

Strata in each column are ordered first by time of appearance and secondly in decreasing order of abundance (value of y). It means, for example, that if the plot has 2 or more columns, in the second column, on top, will appear first appear IS that appeared in the previous columns and then all other IS, ordered in decreasing order of abundance.

Value

For each group a list with the associated plot and optionally the summary tableGrob

See Also

Other Plotting functions: CIS_volcano_plot(), HSC_population_plot(), circos_genomic_density(), fisher_scatterplot(), sharing_heatmap(), sharing_venn(), top_abund_tableGrob(), top_cis_overtime_heatmap()

Examples

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")
)
abund <- compute_abundance(x = aggreg)
alluvial_plots <- integration_alluvial_plot(abund,
    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)"
    )
print(ex_plot)

Description

The data was obtained manually by simulating real research data.

Usage

data("integration_matrices")

Format

Data frame with 1689 rows and 8 columns

chr

The chromosome number (as character)

integration_locus

Number of the base at which the viral insertion occurred

strand

Strand of the integration

GeneName

Symbol of the closest gene

GeneStrand

Strand of the closest gene

CompleteAmplificationID

Unique sample identifier

seqCount

Value of the sequence count quantification

fragmentEstimate

Value of the fragment estimate quantification

Description

Computes the amount of integration sites shared between the groups identified in the input data.

Usage

is_sharing(
  ...,
  group_key = c("SubjectID", "CellMarker", "Tissue", "TimePoint"),
  group_keys = NULL,
  n_comp = 2,
  is_count = TRUE,
  relative_is_sharing = TRUE,
  minimal = TRUE,
  include_self_comp = FALSE,
  keep_genomic_coord = FALSE,
  table_for_venn = FALSE
)

Arguments

Details

An integration site is always identified by the combination of fields in mandatory_IS_vars(), thus these columns must be present in the input(s).

The function accepts multiple inputs for different scenarios, please refer to the vignette vignette("workflow_start", package = "ISAnalytics") for a more in-depth explanation.

Output

The function outputs a single data frame containing all requested comparisons and optionally individual group counts, genomic coordinates of the shared integration sites and truth tables for plotting venn diagrams.

Plotting sharing

The sharing data obtained can be easily plotted in a heatmap via the function sharing_heatmap or via the function sharing_venn

Value

A data frame

Required tags

The function will explicitly check for the presence of these tags:

• All columns declared in mandatory_IS_vars()

See Also

Other Analysis functions: CIS_grubbs(), HSC_population_size_estimate(), compute_abundance(), cumulative_is(), gene_frequency_fisher(), iss_source(), sample_statistics(), top_integrations(), top_targeted_genes()

Examples

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")
)
sharing <- is_sharing(aggreg)
sharing

Description

The function computes the sharing between a reference group of interest for each time point and a selection of groups of interest. In this way it is possible to observe the percentage of shared integration sites between reference and each group and identify in which time point a certain IS was observed for the first time.

Usage

iss_source(
  reference,
  selection,
  ref_group_key = c("SubjectID", "CellMarker", "Tissue", "TimePoint"),
  selection_group_key = c("SubjectID", "CellMarker", "Tissue", "TimePoint"),
  timepoint_column = "TimePoint",
  by_subject = TRUE,
  subject_column = "SubjectID"
)

Arguments

Value

A list of data frames or a data frame

See Also

Other Analysis functions: CIS_grubbs(), HSC_population_size_estimate(), compute_abundance(), cumulative_is(), gene_frequency_fisher(), is_sharing(), sample_statistics(), top_integrations(), top_targeted_genes()

Examples

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")
)
df1 <- aggreg |>
    dplyr::filter(.data$Tissue == "BM")
df2 <- aggreg |>
    dplyr::filter(.data$Tissue == "PB")
source <- iss_source(df1, df2)
source
ggplot2::ggplot(source$PT001, ggplot2::aes(
    x = as.factor(g2_TimePoint),
    y = sharing_perc, fill = g1
)) +
    ggplot2::geom_col() +
    ggplot2::labs(
        x = "Time point", y = "Shared IS % with MNC BM",
        title = "Source of is MNC BM vs MNC PB"
    )

Description

Known clinical oncogenes (for mouse and human).

Usage

known_clinical_oncogenes()

Value

A data frame

See Also

Other Plotting function helpers: clinical_relevant_suspicious_genes()

Examples

known_clinical_oncogenes()

Description

Fetches the look-up tables for different categories of dynamic vars. For more details, refer to the dedicated vignette vignette("workflow_start", package="ISAnalytics").

• mandatory_IS_vars returns the look-up table of variables that are used to uniquely identify integration events

• annotation_IS_vars() returns the look-up table of variables that contain genomic annotations

• association_file_columns() returns the look-up table of variables that contains information on how metadata is structured

• iss_stats_specs() returns the look-up table of variables that contains information on the format of pool statistics files produced automatically by VISPA2

• matrix_file_suffixes() returns the look-up table of variables that contains all default file names for each quantification type and it is used by automated import functions

Usage

mandatory_IS_vars(include_types = FALSE)

annotation_IS_vars(include_types = FALSE)

association_file_columns(include_types = FALSE)

iss_stats_specs(include_types = FALSE)

matrix_file_suffixes()

Arguments

Value

A character vector or a data frame

See Also

Other dynamic vars: inspect_tags(), pcr_id_column(), reset_mandatory_IS_vars(), set_mandatory_IS_vars(), set_matrix_file_suffixes()

Examples

# Names only
mandatory_IS_vars()

# Names and types
mandatory_IS_vars(TRUE)

# Names only
annotation_IS_vars()

# Names and types
annotation_IS_vars(TRUE)

# Names only
association_file_columns()

# Names and types
association_file_columns(TRUE)

# Names only
iss_stats_specs()

# Names and types
iss_stats_specs(TRUE)

# Names only
matrix_file_suffixes()

Description

These are all the possible values for the matching_opt parameter in import_parallel_vispa2Matrices_auto.

Usage

matching_options()

Details

The values "ANY", "ALL" and "OPTIONAL", represent how the patterns should be matched, more specifically

• ANY = look only for files that match AT LEAST one of the patterns specified

• ALL = look only for files that match ALL of the patterns specified

• OPTIONAL = look preferentially for files that match, in order, all patterns or any pattern and if no match is found return what is found (keep in mind that duplicates are discarded in automatic mode)

Value

A vector of characters for matching_opt

See Also

import_parallel_Vispa2Matrices_auto

Other Import functions helpers: annotation_issues(), date_formats(), default_af_transform(), default_iss_file_prefixes(), quantification_types()

Examples

opts <- matching_options()

Description

Launch the shiny application NGSdataExplorer.

Usage

NGSdataExplorer()

Value

Nothing

Examples

## Not run: 
NGSdataExplorer()

## End(Not run)

Description

Filter out outliers in metadata by using appropriate outlier tests.

Usage

outlier_filter(
  metadata,
  pcr_id_col = pcr_id_column(),
  outlier_test = c(outliers_by_pool_fragments),
  outlier_test_outputs = NULL,
  combination_logic = c("AND"),
  negate = FALSE,
  report_path = default_report_path(),
  ...
)

Arguments

Details

Modular structure

The outlier filtering functions are structured in a modular fashion. There are 2 kind of functions:

• Outlier tests - Functions that perform some kind of calculation based on inputs and flags metadata

• Outlier filter - A function that takes one or more outlier tests, combines all the flags with a given logic and filters out rows that are flagged as outliers

This function acts as the filter. It can either take one or more outlier tests as functions and call them through the argument outlier_test, or it can take directly outputs produced by individual tests in the argument outlier_test_outputs - if both are provided the second one has priority. The second method offers a bit more freedom, since single tests can be run independently and intermediate results saved and examined more in detail. If more than one test is to be performed, the argument combination_logic tells the function how to combine the flags: you can specify 1 logical operator or more than 1, provided it is compatible with the number of tests.

Writing custom outlier tests

You have the freedom to provide your own functions as outlier tests. For this purpose, functions provided must respect this guidelines:

• Must take as input the whole metadata df

• Must return a df containing AT LEAST the pcr_id_col and a logical column "to_remove" that contains the flag

• The pcr_id_col must contain all the values originally present in the metadata df

Value

A data frame of metadata which has less or the same amount of rows

See Also

Other Data cleaning and pre-processing: aggregate_metadata(), aggregate_values_by_key(), compute_near_integrations(), default_meta_agg(), outliers_by_pool_fragments(), purity_filter(), realign_after_collisions(), remove_collisions(), threshold_filter()

Examples

data("association_file", package = "ISAnalytics")
filtered_af <- outlier_filter(association_file,
    key = "BARCODE_MUX",
    report_path = NULL
)
head(filtered_af)

Description

Identify and flag outliers based on expected number of raw reads per pool.

Usage

outliers_by_pool_fragments(
  metadata,
  key = "BARCODE_MUX",
  outlier_p_value_threshold = 0.01,
  normality_test = FALSE,
  normality_p_value_threshold = 0.05,
  transform_log2 = TRUE,
  per_pool_test = TRUE,
  pool_col = "PoolID",
  min_samples_per_pool = 5,
  flag_logic = "AND",
  keep_calc_cols = TRUE,
  report_path = default_report_path()
)

Arguments

Details

Modular structure

The outlier filtering functions are structured in a modular fashion. There are 2 kind of functions:

• Outlier tests - Functions that perform some kind of calculation based on inputs and flags metadata

• Outlier filter - A function that takes one or more outlier tests, combines all the flags with a given logic and filters out rows that are flagged as outliers

This function is an outlier test, and calculates for each column in the key

• The zscore of the values

• The tstudent of the values

• The the associated p-value (tdist)

Optionally the test can be performed for each pool and a normality test can be run prior the actual calculations. Samples are flagged if this condition is respected:

• tdist < outlier_p_value_threshold & zscore < 0

If the key contains more than one column an additional flag logic can be specified for combining the results. Example: let's suppose the key contains the names of two columns, X and Y key = c("X", "Y") if we specify the the argument flag_logic = "AND" then the reads will be flagged based on this global condition: (tdist_X < outlier_p_value_threshold & zscore_X < 0) AND (tdist_Y < outlier_p_value_threshold & zscore_Y < 0)

The user can specify one or more logical operators that will be applied in sequence.

Value

A data frame of metadata with the column to_remove

See Also

Other Data cleaning and pre-processing: aggregate_metadata(), aggregate_values_by_key(), compute_near_integrations(), default_meta_agg(), outlier_filter(), purity_filter(), realign_after_collisions(), remove_collisions(), threshold_filter()

Examples

data("association_file", package = "ISAnalytics")
flagged <- outliers_by_pool_fragments(association_file,
    report_path = NULL
)
head(flagged)

Description

The function is a shortcut to retrieve the currently set pcr id column name from the association file column tags look-up table. This column is needed every time a joining operation with metadata needs to be performed

Usage

pcr_id_column()

Value

The name of the column

See Also

Other dynamic vars: inspect_tags(), mandatory_IS_vars(), reset_mandatory_IS_vars(), set_mandatory_IS_vars(), set_matrix_file_suffixes()

Examples

pcr_id_column()

Description

The file is simply a result of a research with the keywords "proto-oncogenes" and "tumor suppressor" for the target genomes on UniProt database.

Usage

data("proto_oncogenes")

data("tumor_suppressors")

Format

An object of class tbl_df (inherits from tbl, data.frame) with 569 rows and 13 columns.

An object of class tbl_df (inherits from tbl, data.frame) with 523 rows and 13 columns.

Functions

• tumor_suppressors: Data frame for tumor suppressor genes

Description

Filter that targets possible contamination between cell lines based on a numeric quantification (likely abundance or sequence count).

Usage

purity_filter(
  x,
  lineages = blood_lineages_default(),
  aggregation_key = c("SubjectID", "CellMarker", "Tissue", "TimePoint"),
  group_key = c("CellMarker", "Tissue"),
  selected_groups = NULL,
  join_on = "CellMarker",
  min_value = 3,
  impurity_threshold = 10,
  by_timepoint = TRUE,
  timepoint_column = "TimePoint",
  value_column = "seqCount_sum"
)

Arguments

Details

Setting input arguments

The input matrix can be re-aggregated with the provided group_key argument. This key contains the names of the columns to group on (besides the columns holding genomic coordinates of the integration sites) and must be contained in at least one of x or lineages data frames. If the key is not found only in x, then a join operation with the lineages data frame is performed on the common column(s) join_on.

Group selection

It is possible for the user to specify on which groups the logic of the filter should be applied to. For example: if we have group_key = c("HematoLineage") and we set selected_groups = c("CD34", "Myeloid","Lymphoid") it means that a single integration will be evaluated for the filter only for groups that have the values of "CD34", "Myeloid" and "Lymphoid" in the "HematoLineage" column. If the same integration is present in other groups it is kept as it is. selected_groups can be set to NULL if we want the logic to apply to every group present in the data frame, it can be set as a simple character vector as the example above if the group key has length 1 (and there is no need to filter on time point). If the group key is longer than 1 then the filter is applied only on the first element of the key.

If a more refined selection on groups is needed, a data frame can be provided instead:

group_key = c("CellMarker", "Tissue")
selected_groups = tibble::tribble(
~ CellMarker, ~ Tissue,
"CD34", "BM",
"CD14", "BM",
"CD14", "PB"
)

Columns in the data frame should be the same as group key (plus, eventually, the time point column). In this example only those groups identified by the rows in the provided data frame are processed.

Value

A data frame

See Also

Other Data cleaning and pre-processing: aggregate_metadata(), aggregate_values_by_key(), compute_near_integrations(), default_meta_agg(), outlier_filter(), outliers_by_pool_fragments(), realign_after_collisions(), remove_collisions(), threshold_filter()

Examples

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")
)
filtered_by_purity <- purity_filter(
    x = aggreg,
    value_column = "seqCount_sum"
)
head(filtered_by_purity)

Description

These are all the possible values for the quantification_type parameter in import_parallel_vispa2Matrices_interactive and import_parallel_vispa2Matrices_auto.

Usage

quantification_types()

Details

The possible values are:

• fragmentEstimate

• seqCount

• barcodeCount

• cellCount

• ShsCount

Value

A vector of characters for quantification types

See Also

import_parallel_Vispa2Matrices_interactive, import_parallel_Vispa2Matrices_auto

Other Import functions helpers: annotation_issues(), date_formats(), default_af_transform(), default_iss_file_prefixes(), matching_options()

Examples

quant_types <- quantification_types()

Description

This function should be used to keep data consistent among the same analysis: if for some reason you removed the collisions by passing only the sequence count matrix to remove_collisions(), you should call this function afterwards, providing a list of other quantification matrices. NOTE: if you provided a list of several quantification types to remove_collisions() before, there is no need to call this function.

Usage

realign_after_collisions(
  sc_matrix,
  other_matrices,
  sample_column = pcr_id_column()
)

Arguments

Details

For more details on how to use collision removal functionality: vignette("workflow_start", package = "ISAnalytics")

Value

A named list with re-aligned matrices

See Also

remove_collisions

Other Data cleaning and pre-processing: aggregate_metadata(), aggregate_values_by_key(), compute_near_integrations(), default_meta_agg(), outlier_filter(), outliers_by_pool_fragments(), purity_filter(), remove_collisions(), threshold_filter()

Examples

data("integration_matrices", package = "ISAnalytics")
data("association_file", package = "ISAnalytics")
separated <- separate_quant_matrices(
    integration_matrices
)
no_coll <- remove_collisions(
    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)
)
realigned

Description

Selection of column names from the association file to be considered for Vispa2 launch. NOTE: the TagID column appears only once but needs to be repeated twice for generating the launch file. Use the appropriate function to generate the file automatically.

Usage

reduced_AF_columns()

Value

A character vector

Examples

reduced_AF_columns()

Description

Required columns for refGene file.

Usage

refGene_table_cols()

Value

Character vector of column names

Examples

refGene_table_cols()

Description

This file was obtained following this steps:

1. Download from http://hgdownload.soe.ucsc.edu/goldenPath/hg19/database/ the refGene.sql, knownGene.sql, knownToRefSeq.sql, kgXref.sql tables

2. Import everything it in mysql

3. Generate views for annotation:

SELECT kg.`chrom`, min(kg.cdsStart) as CDS_minStart,
max(kg.`cdsEnd`) as CDS_maxEnd, k2a.geneSymbol,
kg.`strand` as GeneStrand, min(kg.txStart) as TSS_minStart,
max(kg.txEnd) as TSS_maxStart,
kg.proteinID as ProteinID, k2a.protAcc as ProteinAcc, k2a.spDisplayID
FROM `knownGene` AS kg JOIN kgXref AS k2a
ON BINARY kg.name = k2a.kgID COLLATE latin1_bin
-- latin1_swedish_ci
-- WHERE k2a.spDisplayID IS NOT NULL and (k2a.`geneSymbol` LIKE 'Tcra%' or
k2a.`geneSymbol` LIKE 'TCRA%')
WHERE (k2a.spDisplayID IS NOT NULL or k2a.spDisplayID NOT LIKE '')
and k2a.`geneSymbol` LIKE 'Tcra%'
group by kg.`chrom`, k2a.geneSymbol
ORDER BY kg.chrom ASC , kg.txStart ASC

Usage

data("refGenes_hg19")

data("refGenes_mm9")

Format

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

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

Functions

• refGenes_mm9: Data frame for murine mm9 genome

Description

A collision is an integration (aka a unique combination of the provided mandatory_IS_vars()) which is observed in more than one independent sample. The function tries to decide to which independent sample should an integration event be assigned to, and if no decision can be taken, the integration is completely removed from the data frame. For more details refer to the vignette "Collision removal functionality": vignette("workflow_start", package = "ISAnalytics")

Usage

remove_collisions(
  x,
  association_file,
  independent_sample_id = c("ProjectID", "SubjectID"),
  date_col = "SequencingDate",
  reads_ratio = 10,
  quant_cols = c(seqCount = "seqCount", fragmentEstimate = "fragmentEstimate"),
  report_path = default_report_path(),
  max_workers = NULL
)

Arguments

Value

Either a multi-quantification matrix or a list of data frames

Required tags

The function will explicitly check for the presence of these tags:

• project_id

• pool_id

• pcr_replicate

See Also

Other Data cleaning and pre-processing: aggregate_metadata(), aggregate_values_by_key(), compute_near_integrations(), default_meta_agg(), outlier_filter(), outliers_by_pool_fragments(), purity_filter(), realign_after_collisions(), threshold_filter()

Examples

data("integration_matrices", package = "ISAnalytics")
data("association_file", package = "ISAnalytics")
no_coll <- remove_collisions(
    x = integration_matrices,
    association_file = association_file,
    report_path = NULL
)
head(no_coll)

Description

Reverts all changes to dynamic vars to the default values. For more details, refer to the dedicated vignette vignette("workflow_start", package="ISAnalytics").

• reset_mandatory_IS_vars() re-sets the look-up table for mandatory IS vars.

• reset_annotation_IS_vars() re-sets the look-up table for genomic annotation IS vars.

• reset_af_columns_def() re-sets the look-up table for association file columns vars

• reset_iss_stats_specs() re-sets the look-up table for VISPA2 pool statistics vars

• reset_matrix_file_suffixes() re-sets the matrix file suffixes look-up table

• reset_dyn_vars_config() re-sets all look-up tables

Usage

reset_mandatory_IS_vars()

reset_annotation_IS_vars()

reset_af_columns_def()

reset_iss_stats_specs()

reset_matrix_file_suffixes()

reset_dyn_vars_config()

Value

NULL

See Also

Other dynamic vars: inspect_tags(), mandatory_IS_vars(), pcr_id_column(), set_mandatory_IS_vars(), set_matrix_file_suffixes()

Examples

reset_mandatory_IS_vars()

reset_annotation_IS_vars()

reset_af_columns_def()

reset_iss_stats_specs()

reset_matrix_file_suffixes()

reset_dyn_vars_config()

Description

The function operates on a data frame by grouping the content by the sample key and computing every function specified on every column in the value_columns parameter. After that the metadata data frame is updated by including the computed results as columns for the corresponding key. For this reason it's required that both x and metadata have the same sample key, and it's particularly important if the user is working with previously aggregated data. For example:

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")
)
aggreg_meta <- aggregate_metadata(association_file = association_file)

sample_stats <- sample_statistics(x = aggreg,
metadata = aggreg_meta,
value_columns = c("seqCount", "fragmentEstimate"),
sample_key = c("SubjectID", "CellMarker","Tissue", "TimePoint"))

Usage

sample_statistics(
  x,
  metadata,
  sample_key = "CompleteAmplificationID",
  value_columns = "Value",
  functions = default_stats(),
  add_integrations_count = TRUE
)

Arguments

Value

A list with modified x and metadata data frames

Required tags

The function will explicitly check for the presence of these tags:

• All columns declared in mandatory_IS_vars()

These are checked only if add_integrations_count = TRUE.

See Also

Other Analysis functions: CIS_grubbs(), HSC_population_size_estimate(), compute_abundance(), cumulative_is(), gene_frequency_fisher(), is_sharing(), iss_source(), top_integrations(), top_targeted_genes()

Examples

data("integration_matrices", package = "ISAnalytics")
data("association_file", package = "ISAnalytics")
stats <- sample_statistics(
    x = integration_matrices,
    metadata = association_file,
    value_columns = c("seqCount", "fragmentEstimate")
)
stats

Description

The function separates a single multi-quantification integration matrix, obtained via comparison_matrix, into single quantification matrices as a named list of tibbles.

Usage

separate_quant_matrices(
  x,
  fragmentEstimate = "fragmentEstimate",
  seqCount = "seqCount",
  barcodeCount = "barcodeCount",
  cellCount = "cellCount",
  ShsCount = "ShsCount",
  key = c(mandatory_IS_vars(), annotation_IS_vars(), "CompleteAmplificationID")
)

Arguments

Value

A named list of data frames, where names are quantification types

See Also

quantification_types

Other Utilities: as_sparse_matrix(), comparison_matrix(), enable_progress_bars(), export_ISA_settings(), generate_Vispa2_launch_AF(), generate_blank_association_file(), generate_default_folder_structure(), import_ISA_settings(), transform_columns()

Examples

data("integration_matrices", package = "ISAnalytics")
separated <- separate_quant_matrices(
    integration_matrices
)

Description

This set of function allows users to specify custom look-up tables for dynamic variables. For more details, refer to the dedicated vignette vignette("workflow_start", package="ISAnalytics").

• set_mandatory_IS_vars() sets the look-up table for mandatory IS vars.

• set_annotation_IS_vars() sets the look-up table for genomic annotation IS vars.

• set_af_columns_def() sets the look-up table for association file columns vars

• set_iss_stats_specs() sets the look-up table for VISPA2 pool statistics vars

Usage

set_mandatory_IS_vars(specs)

set_annotation_IS_vars(specs)

set_af_columns_def(specs)

set_iss_stats_specs(specs)

Arguments

Details

The user can supply specifications in the form of a named vector or a data frame.

Named vector

When using a named vector, names should be the names of the columns, values should be the type associated with each column in the form of a string. The vector gets automatically converted into a data frame with the right format (default values for the columns transform and flag are NULL and required respectively). Use of this method is however discouraged: data frame inputs are preferred since they offer more control.

Look-up table structure

The look-up table for dynamic vars should always follow this structure:

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

Column types:

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

Value

NULL

See Also

Other dynamic vars: inspect_tags(), mandatory_IS_vars(), pcr_id_column(), reset_mandatory_IS_vars(), set_matrix_file_suffixes()

Examples

tmp_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_
)
set_mandatory_IS_vars(tmp_mand_vars)
print(mandatory_IS_vars(TRUE))
reset_mandatory_IS_vars()

tmp_annot_vars <- tibble::tribble(
    ~names, ~types, ~transform, ~flag, ~tag,
    "gene", "char", NULL, "required",
    "gene_symbol",
    "gene_strand", "char", NULL, "required", "gene_strand"
)
print(annotation_IS_vars(TRUE))
reset_annotation_IS_vars()

temp_af_cols <- tibble::tribble(
    ~names, ~types, ~transform, ~flag, ~tag,
    "project", "char", NULL, "required",
    "project_id",
    "pcr_id", "char", NULL, "required", "pcr_repl_id",
    "subject", "char", NULL, "required", "subject"
)
set_af_columns_def(temp_af_cols)
print(association_file_columns(TRUE))
reset_af_columns_def()

tmp_iss_vars <- tibble::tribble(
    ~names, ~types, ~transform, ~flag, ~tag,
    "pool", "char", NULL, "required",
    "vispa_concatenate",
    "tag", "char", NULL, "required", "tag_seq",
    "barcode", "int", NULL, "required", NA_character_
)
set_iss_stats_specs(tmp_iss_vars)
iss_stats_specs(TRUE)
reset_iss_stats_specs()

Description

The function automatically produces and sets a look-up table of matrix file suffixes based on user input.

Usage

set_matrix_file_suffixes(
  quantification_suffix = list(seqCount = "seqCount", fragmentEstimate =
    "fragmentEstimate", barcodeCount = "barcodeCount", cellCount = "cellCount", ShsCount
    = "ShsCount"),
  annotation_suffix = list(annotated = ".no0.annotated", not_annotated = ""),
  file_ext = "tsv.gz",
  glue_file_spec = "{quantification_suffix}_matrix{annotation_suffix}.{file_ext}"
)

Arguments

Value

NULL

See Also

Other dynamic vars: inspect_tags(), mandatory_IS_vars(), pcr_id_column(), reset_mandatory_IS_vars(), set_mandatory_IS_vars()

Examples

set_matrix_file_suffixes(
    quantification_suffix = list(
        seqCount = "sc",
        fragmentEstimate = "fe",
        barcodeCount = "barcodeCount",
        cellCount = "cellCount",
        ShsCount = "ShsCount"
    ),
    annotation_suffix = list(annotated = "annot", not_annotated = "")
)
matrix_file_suffixes()
reset_matrix_file_suffixes()

Description

Displays the IS sharing calculated via is_sharing as heatmaps.

Usage

sharing_heatmap(
  sharing_df,
  show_on_x = "g1",
  show_on_y = "g2",
  absolute_sharing_col = "shared",
  title_annot = NULL,
  plot_relative_sharing = TRUE,
  rel_sharing_col = c("on_g1", "on_union"),
  show_perc_symbol_rel = TRUE,
  interactive = FALSE
)

Arguments

Value

A list of plots or widgets

See Also

is_sharing

Other Plotting functions: CIS_volcano_plot(), HSC_population_plot(), circos_genomic_density(), fisher_scatterplot(), integration_alluvial_plot(), sharing_venn(), top_abund_tableGrob(), top_cis_overtime_heatmap()

Examples

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")
)
sharing <- is_sharing(aggreg,
    minimal = FALSE,
    include_self_comp = TRUE
)
sharing_heatmaps <- sharing_heatmap(sharing_df = sharing)
sharing_heatmaps$absolute
sharing_heatmaps$on_g1
sharing_heatmaps$on_union

Description

This function processes a sharing data frame obtained via is_sharing() with the option table_for_venn = TRUE to obtain a list of objects that can be plotted as venn or euler diagrams.

Usage

sharing_venn(sharing_df, row_range = NULL, euler = TRUE)

Arguments

Details

The functions requires the package eulerr. Each row of the input data frame is representable as a venn/euler diagram. The function allows to specify a range of row indexes to obtain a list of plottable objects all at once, leave it to NULL to process all rows.

To actually plot the data it is sufficient to call the function plot() and specify optional customization arguments. See eulerr docs for more detail on this.

Value

A list of data frames

See Also

Other Plotting functions: CIS_volcano_plot(), HSC_population_plot(), circos_genomic_density(), fisher_scatterplot(), integration_alluvial_plot(), sharing_heatmap(), top_abund_tableGrob(), top_cis_overtime_heatmap()

Examples

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")
)
sharing <- is_sharing(aggreg, n_comp = 3, table_for_venn = TRUE)
venn_tbls <- sharing_venn(sharing, row_range = 1:3, euler = FALSE)
venn_tbls
plot(venn_tbls[[1]])

Description

Produce summary tableGrobs as R graphics. For this functionality the suggested package gridExtra is required. To visualize the resulting object:

gridExtra::grid.arrange(tableGrob)

Usage

top_abund_tableGrob(
  df,
  id_cols = mandatory_IS_vars(),
  quant_col = "fragmentEstimate_sum_PercAbundance",
  by = "TimePoint",
  alluvial_plot = NULL,
  top_n = 10,
  tbl_cols = "GeneName",
  include_id_cols = FALSE,
  digits = 2,
  perc_symbol = TRUE,
  transform_by = NULL
)

Arguments

Value

A tableGrob object

See Also

Other Plotting functions: CIS_volcano_plot(), HSC_population_plot(), circos_genomic_density(), fisher_scatterplot(), integration_alluvial_plot(), sharing_heatmap(), sharing_venn(), top_cis_overtime_heatmap()

Examples

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")
)
abund <- compute_abundance(x = aggreg)
grob <- top_abund_tableGrob(abund)
gridExtra::grid.arrange(grob)

# with transform
grob <- top_abund_tableGrob(abund, transform_by = ~ as.numeric(.x))

Description

This function computes the visualization of the results of the function CIS_grubbs_overtime() in the form of heatmaps for the top N selected genes over time.

Usage

top_cis_overtime_heatmap(
  x,
  n_genes = 20,
  timepoint_col = "TimePoint",
  group_col = "group",
  onco_db_file = "proto_oncogenes",
  tumor_suppressors_db_file = "tumor_suppressors",
  species = "human",
  known_onco = known_clinical_oncogenes(),
  suspicious_genes = clinical_relevant_suspicious_genes(),
  significance_threshold = 0.05,
  plot_values = c("minus_log_p", "p"),
  p_value_correction = c("fdr", "bonferroni"),
  prune_tp_treshold = 20,
  gene_selection_param = c("trimmed", "n", "mean", "sd", "median", "mad", "min", "max"),
  fill_0_selection = TRUE,
  fill_NA_in_heatmap = FALSE,
  heatmap_color_palette = "default",
  title_generator = NULL,
  save_as_files = FALSE,
  files_format = c("pdf", "png", "tiff", "bmp", "jpg"),
  folder_path = NULL,
  ...
)

Arguments

Details

Oncogene and tumor suppressor genes files

These files are included in the package for user convenience and are simply UniProt files with gene annotations for human and mouse. For more details on how this files were generated use the help ?tumor_suppressors, ?proto_oncogenes

Known oncogenes

The default values are included in this package and it can be accessed by doing:

If the user wants to change this parameter the input data frame must preserve the column structure. The same goes for the suspicious_genes parameter (DOIReference column is optional):

Top N gene selection

Since the genes present in different time point slices are likely different, the decision process to select the final top N genes to represent in the heatmap follows this logic:

• Each time point slice is arranged either in ascending order (if we want to plot the p-value) or in descending order (if we want to plot the scaled p-value) and the top n genes are selected

• A series of statistics are computed over the union set of genes on ALL time points (min, max, mean, ...)

• A decision is taken by considering the ordered gene_selection_param (order depends once again if the values are scaled or not), and the first N genes are selected for plotting.

Filling NA values prior calculations

It is possible to fill NA values (aka missing combinations of GENE/TP) with 0s prior computing the descriptive statistics on which gene selection is based. Please keep in mind that this has an impact on the final result, since for computing metrics such as the mean, NA values are usually removed, decreasing the overall number of values considered - this does not hold when NA values are substituted with 0s.

The statistics

Statistics are computed for each gene over all time points of each group. More in detail, n: counts the number of instances (rows) in which the genes appears, aka it counts the time points in which the gene is present. NOTE: if fill_0_selection option is set to TRUE this value will be equal for all genes! All other statistics as per the argument gene_selection_param map to the corresponding R functions with the exception of trimmed which is a simple call to the mean function with the argument trimmed = 0.1.

Aesthetics

It is possible to customise the appearence of the plot through different parameters.

• fill_NA_in_heatmap tells the function whether missing combinations of GENE/TP should be plotted as NA or filled with a value (1 if p-value, 0 if scaled p-value)

• A title generator function can be provided to dynamically create a title for the plots: the function can accept two positional arguments for the group identifier and the number of selected genes respectively. If one or none of the arguments are of interest, they can be absorbed with ....

• heatmap_color_palette can be used to specify a function from which colors are sampled (refers to the colors of values only)

• To change the colors associated with annotations instead, use the argument annotation_colors of pheatmap::pheatmap() - it must be set to a list with this format:

list(
  KnownGeneClass = c("OncoGene" = color_spec,
                     "Other" = color_spec,
                     "TumSuppressor" = color_spec),
  ClinicalRelevance = c("TRUE" = color_spec,
                        "FALSE" = color_spec),
  CriticalForInsMut = c("TRUE" = color_spec,
                        "FALSE" = color_spec)
)

Value

Either a list of graphical objects or a list of paths where plots were saved

See Also

Other Plotting functions: CIS_volcano_plot(), HSC_population_plot(), circos_genomic_density(), fisher_scatterplot(), integration_alluvial_plot(), sharing_heatmap(), sharing_venn(), top_abund_tableGrob()

Examples

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")
)
cis_overtime <- CIS_grubbs_overtime(aggreg)
hmaps <- top_cis_overtime_heatmap(cis_overtime$cis,
    fill_NA_in_heatmap = TRUE
)

# To re-plot:
# grid::grid.newpage()
# grid::grid.draw(hmaps$PT001$gtable)

Description

The input data frame will be sorted by the highest values in the columns specified and the top n rows will be returned as output. The user can choose to keep additional columns in the output by passing a vector of column names or passing 2 "shortcuts":

• keep = "everything" keeps all columns in the original data frame

• keep = "nothing" only keeps the mandatory columns (mandatory_IS_vars()) plus the columns in the columns parameter.

Usage

top_integrations(
  x,
  n = 20,
  columns = "fragmentEstimate_sum_RelAbundance",
  keep = "everything",
  key = NULL
)

Arguments

Value

Either a data frame with at most n rows or a data frames with at most n*(number of groups) rows.

Required tags

The function will explicitly check for the presence of these tags:

• All columns declared in mandatory_IS_vars()

See Also

Other Analysis functions: CIS_grubbs(), HSC_population_size_estimate(), compute_abundance(), cumulative_is(), gene_frequency_fisher(), is_sharing(), iss_source(), sample_statistics(), top_targeted_genes()

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")
)
top <- top_integrations(smpl,
    n = 3,
    columns = c("Value", "Value2"),
    keep = "nothing"
)
top_key <- top_integrations(smpl,
    n = 3,
    columns = "Value",
    keep = "Value2",
    key = "CompleteAmplificationID"
)

Description

Produces a summary of the number of integration events per gene, orders the table in decreasing order and slices the first n rows - either on all the data frame or by group.

Usage

top_targeted_genes(
  x,
  n = 20,
  key = c("SubjectID", "CellMarker", "Tissue", "TimePoint"),
  consider_chr = TRUE,
  consider_gene_strand = TRUE,
  as_df = TRUE
)