ISAnalytics is an R package developed to analyze gene therapy vector insertion sites data identified from genomics next generation sequencing reads for clonal tracking studies.
In gene therapy, stem cells are modified using viral vectors to deliver the therapeutic transgene and replace functional properties since the genetic modification is stable and inherited in all cell progeny. The retrieval and mapping of the sequences flanking the virus-host DNA junctions allows the identification of insertion sites (IS), essential for monitoring the evolution of genetically modified cells in vivo. A comprehensive toolkit for the analysis of IS is required to foster clonal trackign studies and supporting the assessment of safety and long term efficacy in vivo. This package is aimed at (1) supporting automation of IS workflow, (2) performing base and advance analysis for IS tracking (clonal abundance, clonal expansions and statistics for insertional mutagenesis, etc.), (3) providing basic biology insights of transduced stem cells in vivo.
You can visit the package website to view documentation, vignettes and more.
RELEASE version:
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("ISAnalytics")
DEVEL version:
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
# The following initializes usage of Bioc devel
BiocManager::install(version='devel')
BiocManager::install("ISAnalytics")
RELEASE:
if (!require(devtools)) {
install.packages("devtools")
}
devtools::install_github("calabrialab/ISAnalytics",
ref = "RELEASE_3_12",
dependencies = TRUE,
build_vignettes = TRUE)
## Safer option for vignette building issue
devtools::install_github("calabrialab/ISAnalytics",
ref = "RELEASE_3_12")
DEVEL:
if (!require(devtools)) {
install.packages("devtools")
}
devtools::install_github("calabrialab/ISAnalytics",
ref = "master",
dependencies = TRUE,
build_vignettes = TRUE)
## Safer option for vignette building issue
devtools::install_github("calabrialab/ISAnalytics",
ref = "master")
vignette("How to use import functions", package = "ISAnalytics")
vignette("Collision removal functionality", package = "ISAnalytics")
vignette("Working with aggregate functions", package = "ISAnalytics")
compute_near_integrations
compute_abundance
, comparison_matrix
separate_quant_matrices
, othersCIS_volcano_plot
compute_near_integrations
: when provided
recalibration map export path as a folder now the function works
correctly and produces an automatically generated file nameaggregate_metadata
: now paths to folder that
contains Vispa2 stats is looked up correctly. Also, VISPA2 stats
columns are aggregated if found in the input data frame
independently from the parameter import_stats
.compute_abundance
can now take as input aggregated matrices and
has additional parameters to offer more flexibility to the user.
Major updates and improvements also on documentation and
reproducible examples.import_single_Vispa2Matrix
: import
is now preferentially carried out using data.table::fread
greatly
speeding up the process - where not possible readr::read_delim
is
used insteadimport_association_file
: greatly
improved parsing precision (each column has a dedicated type),
import report now signals parsing problems and their location and
signals also problems in parsing dates. Report also includes
potential problems in column names and signals missing data in
important columns. Added also the possibility to give various file
formats in input including *.xls(x)
formats.top_integrations
can now take additional parameters to
compute top n genes for each specified groupCIS_volcano_plot
due to poor
precision (easier to add faceting manually) and added parameters to
return the data frame that generated the plot as an additional
result. Also, it is now possible to specify a vector of gene names
to highlight even if they’re not above the annotation threshold.remove_collisions
CIS_grubbs
and cumulative_count_union
CIS_volcano_plot
sample_statistics
aggregate_values_by_key
has a simplified interface and supports
multi-quantification matricesimport_parallel_Vispa2Matrices_interactive
and
import_parallel_Vispa2Matrices_auto
now have an option to return a
multi-quantification matrix directly after import instead of a listthreshold_filter
, top_integrations
compute_abundance
comparison_matrix
that ignored custom column namesISanalytics is officially on bioconductor!
comparison_matrix
and
separate_quant_matrices
as_sparse_matrix
compute_near_integrations
remove_collisions
import_single_Vispa2Matrix
to remove non significant
0 valuesISADataFrame
: now the package only uses standard
tibblesFor help please contact the maintainer of the package or open an issue on GitHub.
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