CIS_volcano_plot: Trace volcano plot for computed CIS data.
In ISAnalytics: Analyze gene therapy vector insertion sites data identified from genomics next generation sequencing reads for clonal tracking studies

Description Usage Arguments Details Value Examples

\lifecycle

experimental Traces a volcano plot for IS frequency and CIS results.

CIS_volcano_plot(
  x,
  onco_db_file = system.file("extdata", "201806_uniprot-Proto-oncogene.tsv.xz", package
    = "ISAnalytics"),
  tumor_suppressors_db_file = system.file("extdata",
    "201806_uniprot-Tumor-suppressor.tsv.xz", package = "ISAnalytics"),
  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
)

`x`	Either a simple integration matrix or a data frame resulting from the call to CIS_grubbs with `add_standard_padjust = TRUE`
`onco_db_file`	Uniprot file for proto-oncogenes (see details)
`tumor_suppressors_db_file`	Uniprot file for tumor-suppressor genes
`species`	One between "human", "mouse" and "all"
`known_onco`	Data frame with known oncogenes. See details.
`suspicious_genes`	Data frame with clinical relevant suspicious genes. See details.
`significance_threshold`	The significance threshold
`annotation_threshold_ontots`	Value above which genes are annotated
`highlight_genes`	Either NULL or a character vector of genes to be highlighted in the plot even if they're not above the threshold
`title_prefix`	A string to be displayed in the title - usually the project name and other characterizing info
`return_df`	Return the data frame used to generate the plot? This can be useful if the user wants to manually modify the plot with ggplot2. If TRUE the function returns a list containing both the plot and the data frame.

Input data frame

Users can supply as x either a simple integration matrix or a data frame resulting from the call to CIS_grubbs with add_standard_padjust = TRUE. 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 ?filename function.

Known oncogenes

The default values are contained in a data frame exported by this package, it can be accessed by doing:

head(known_clinical_oncogenes())

## # A tibble: 5 x 2
##   GeneName KnownClonalExpansion
##   <chr>    <lgl>               
## 1 MECOM    TRUE                
## 2 CCND2    TRUE                
## 3 TAL1     TRUE                
## 4 LMO2     TRUE                
## 5 HMGA2    TRUE

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):

head(clinical_relevant_suspicious_genes())

## # A tibble: 6 x 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 plot or a list containing a plot and a data frame

op <- options(ISAnalytics.widgets = FALSE)

path_AF <- system.file("extdata", "ex_association_file.tsv",
    package = "ISAnalytics"
)
root_correct <- system.file("extdata", "fs.zip",
    package = "ISAnalytics"
)
root_correct <- unzip_file_system(root_correct, "fs")

matrices <- import_parallel_Vispa2Matrices_auto(
    association_file = path_AF, root = root_correct,
    quantification_type = c("seqCount", "fragmentEstimate"),
    matrix_type = "annotated", workers = 2, patterns = NULL,
    matching_opt = "ANY",
    dates_format = "dmy"
)

cis <- CIS_grubbs(matrices)
plot <- CIS_volcano_plot(cis)
options(op)