ISAnalytics 1.8.3
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.
ISAnalytics can be installed quickly in different ways:
devtoolsThere are always 2 versions of the package active:
RELEASE is the latest stable versionDEVEL is the development version, it is the most up-to-date version where
all new features are introducedRELEASE 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_16",
dependencies = TRUE,
build_vignettes = TRUE)DEVEL:
if (!require(devtools)) {
install.packages("devtools")
}
devtools::install_github("calabrialab/ISAnalytics",
ref = "master",
dependencies = TRUE,
build_vignettes = TRUE)ISAnalytics has a verbose option that allows some functions to print
additional information to the console while they’re executing.
To disable this feature do:
# DISABLE
options("ISAnalytics.verbose" = FALSE)
# ENABLE
options("ISAnalytics.verbose" = TRUE)
Some functions also produce report in a user-friendly HTML format, to set this feature:
# DISABLE HTML REPORTS
options("ISAnalytics.reports" = FALSE)
# ENABLE HTML REPORTS
options("ISAnalytics.reports" = TRUE)In the newer version of ISAnalytics, we introduced a “dynamic variables system”,
to allow more flexibility in terms of input formats. Before starting with the
analysis workflow, you can specify how your inputs are structured so that
the package can process them. For more information on how to do this
take a look at vignette("workflow_start", package = "ISAnalytics").
The first steps of the analysis workflow involve the import and parsing of data and metadata files from disk.
import_association_file() and/or
import_Vispa2_stats()import_single_Vispa2Matrix() or
import_parallel_Vispa2Matrices()Refer to the vignette
vignette("workflow_start", package = "ISAnalytics") for
more details.
ISAnalytics offers several different functions for cleaning and pre-processing your data.
compute_near_integrations()outlier_filter()remove_collisions(), see also the dedicated vignette
vignette("workflow_start", package = "ISAnalytics")purity_filter()aggregate_values_by_key(), aggregate_metadata(), see also the
dedicated vignette
vignette("workflow_start", package = "ISAnalytics")You can answer very different biological questions by using the provided functions with appropriate inputs.
sample_statistics()compute_abundance(), integration_alluvial_plot()top_integrations()top_targeted_genes()CIS_grubbs(),
CIS_volcano_plot()gene_frequency_fisher(), fisher_scatterplot(), circos_genomic_density()is_sharing(), iss_source(), sharing_heatmap(),
sharing_venn()HSC_population_size_estimate(),
HSC_population_plot()For more, please refer to the full function reference.
ISAnalytics is designed to be flexible concerning input formats, thus it is suited to process various kinds of data provided the correct dynamic configuration is set.
We demonstrate this with an example that uses barcodes data. The matrix is publicly available here (Ferrari Samuele Jacob Aurelien, 2020), metadata was provided to us by the authors and it is available in the package additional files.
library(ISAnalytics)
# Set appropriate data and metadata specs ----
metadata_specs <- tibble::tribble(
~names, ~types, ~transform, ~flag, ~tag,
"ProjectID", "char", NULL, "required", "project_id",
"SubjectID", "char", NULL, "required", "subject",
"Tissue", "char", NULL, "required", "tissue",
"TimePoint", "int", NULL, "required", "tp_days",
"CellMarker", "char", NULL, "required", "cell_marker",
"ID", "char", NULL, "required", "pcr_repl_id",
"SourceFileName", "char", NULL, "optional", NA_character_,
"Link", "char", NULL, "optional", NA_character_
)
set_af_columns_def(metadata_specs)
#> Warning: Warning: important tags missing
#> ℹ Some tags are required for proper execution of some functions. If these tags are not provided, execution of dependent functions might fail. Review your inputs carefully.
#> ℹ Missing tags: pool_id, fusion_id, tag_seq, vector_id, tag_id, pcr_replicate, vispa_concatenate, proj_folder
#> ℹ To see where these are involved type `inspect_tags(c('pool_id','fusion_id','tag_seq','vector_id','tag_id','pcr_replicate','vispa_concatenate','proj_folder'))`
#> Association file columns specs successfully changed
mandatory_specs <- tibble::tribble(
~names, ~types, ~transform, ~flag, ~tag,
"BarcodeSeq", "char", NULL, "required", NA_character_
)
set_mandatory_IS_vars(mandatory_specs)
#> Warning: Warning: important tags missing
#> ℹ Some tags are required for proper execution of some functions. If these tags are not provided, execution of dependent functions might fail. Review your inputs carefully.
#> ℹ Missing tags: chromosome, locus, is_strand
#> ℹ To see where these are involved type `inspect_tags(c('chromosome','locus','is_strand'))`
#> Mandatory IS vars successfully changed
# Files ----
data_folder <- tempdir()
utils::unzip(zipfile = system.file("testdata",
"testdata.zip",
package = "ISAnalytics"),
exdir = data_folder, overwrite = TRUE)
meta_file <- "barcodes_example_af.tsv.xz"
matrix_file <- "GSE144340_Matrix_542.tsv.xz"
# Data import ----
af <- import_association_file(fs::path(data_folder, meta_file),
report_path = NULL
)
af
#> # A tibble: 15 × 10
#> ProjectID SubjectID Tissue TimePoint CellMarker ID SourceFileName Link
#> <chr> <chr> <chr> <int> <chr> <chr> <chr> <chr>
#> 1 PMID32601433 A0 BM 21 Whole BM_A0 GSE144340_Mat… http…
#> 2 PMID32601433 A0 PB 21 Whole PB21… GSE144340_Mat… http…
#> 3 PMID32601433 A1 BM 21 Whole BM_A1 GSE144340_Mat… http…
#> 4 PMID32601433 A1 PB 21 Whole PB21… GSE144340_Mat… http…
#> 5 PMID32601433 A2 PB 21 Whole PB21… GSE144340_Mat… http…
#> 6 PMID32601433 A3 PB 21 Whole PB21… GSE144340_Mat… http…
#> 7 PMID32601433 A4 BM 21 Whole BM_A4 GSE144340_Mat… http…
#> 8 PMID32601433 A4 PB 21 Whole PB21… GSE144340_Mat… http…
#> 9 PMID32601433 C0 PB 21 Whole PB21… GSE144340_Mat… http…
#> 10 PMID32601433 C1 BM 21 Whole BM_C1 GSE144340_Mat… http…
#> 11 PMID32601433 C1 PB 21 Whole PB21… GSE144340_Mat… http…
#> 12 PMID32601433 C2 BM 21 Whole BM_C2 GSE144340_Mat… http…
#> 13 PMID32601433 C2 PB 21 Whole PB21… GSE144340_Mat… http…
#> 14 PMID32601433 C3 BM 21 Whole BM_C3 GSE144340_Mat… http…
#> 15 PMID32601433 C3 PB 21 Whole PB21… GSE144340_Mat… http…
#> # ℹ 2 more variables: TimepointMonths <chr>, TimepointYears <chr>
matrix <- import_single_Vispa2Matrix(fs::path(data_folder, matrix_file),
sample_names_to = "ID"
)
#> Warning: compression format not supported by fread
#> ℹ File will be read using readr
#> Reading file...
#> ℹ Mode: classic
#> Reshaping...
#> *** File info ***
#> • --- Annotated: FALSE
#> • --- Dimensions: 31757 x 16
#> • --- Read mode: classic
#> • --- Sample count: 15
matrix
#> # A tibble: 33,776 × 3
#> BarcodeSeq ID Value
#> <chr> <chr> <dbl>
#> 1 AAAAAAAACACGGAGAACGACG PB21_C3 2
#> 2 AAAAAAAACGCGAACAACTACG PB21_C3 1
#> 3 AAAAAAAACTCAAAAAAGAAAT BM_C3 1
#> 4 AAAAAAAATTTACACAAAGAAA BM_A4 1
#> 5 AAAAAAAATTTTTAAACGTACC BM_A0 1
#> 6 AAAAAACATATCTATAGTTACC BM_A0 1
#> 7 AAAAAAGACGACGATAGGCACG PB21_C1 1
#> 8 AAAAAAGACGTTTATAGGTGTA PB21_A2 1
#> 9 AAAAAAGACTGCGACAAAAGGG BM_A4 1
#> 10 AAAAAAGACTTTGATAACCACG PB21_C3 1
#> # ℹ 33,766 more rows
# Descriptive stats ----
desc_stats <- sample_statistics(matrix, af,
sample_key = pcr_id_column(),
value_columns = "Value"
)$metadata |>
dplyr::rename(distinct_barcodes = "nIS")
desc_stats
#> # A tibble: 15 × 29
#> ProjectID SubjectID Tissue TimePoint CellMarker ID SourceFileName Link
#> <chr> <chr> <chr> <int> <chr> <chr> <chr> <chr>
#> 1 PMID32601433 A0 BM 21 Whole BM_A0 GSE144340_Mat… http…
#> 2 PMID32601433 A0 PB 21 Whole PB21… GSE144340_Mat… http…
#> 3 PMID32601433 A1 BM 21 Whole BM_A1 GSE144340_Mat… http…
#> 4 PMID32601433 A1 PB 21 Whole PB21… GSE144340_Mat… http…
#> 5 PMID32601433 A2 PB 21 Whole PB21… GSE144340_Mat… http…
#> 6 PMID32601433 A3 PB 21 Whole PB21… GSE144340_Mat… http…
#> 7 PMID32601433 A4 BM 21 Whole BM_A4 GSE144340_Mat… http…
#> 8 PMID32601433 A4 PB 21 Whole PB21… GSE144340_Mat… http…
#> 9 PMID32601433 C0 PB 21 Whole PB21… GSE144340_Mat… http…
#> 10 PMID32601433 C1 BM 21 Whole BM_C1 GSE144340_Mat… http…
#> 11 PMID32601433 C1 PB 21 Whole PB21… GSE144340_Mat… http…
#> 12 PMID32601433 C2 BM 21 Whole BM_C2 GSE144340_Mat… http…
#> 13 PMID32601433 C2 PB 21 Whole PB21… GSE144340_Mat… http…
#> 14 PMID32601433 C3 BM 21 Whole BM_C3 GSE144340_Mat… http…
#> 15 PMID32601433 C3 PB 21 Whole PB21… GSE144340_Mat… http…
#> # ℹ 21 more variables: TimepointMonths <chr>, TimepointYears <chr>,
#> # Value_sum <dbl>, Value_count <int>, Value_shannon <dbl>,
#> # Value_simpson <dbl>, Value_invsimpson <dbl>, Value_describe_vars <dbl>,
#> # Value_describe_n <dbl>, Value_describe_mean <dbl>, Value_describe_sd <dbl>,
#> # Value_describe_median <dbl>, Value_describe_trimmed <dbl>,
#> # Value_describe_mad <dbl>, Value_describe_min <dbl>,
#> # Value_describe_max <dbl>, Value_describe_range <dbl>, …
# Aggregation and new stats ----
agg_key <- c("SubjectID")
agg <- aggregate_values_by_key(matrix, af,
key = agg_key,
group = "BarcodeSeq",
join_af_by = pcr_id_column()
)
agg
#> # A tibble: 33,267 × 3
#> BarcodeSeq SubjectID Value_sum
#> <chr> <chr> <dbl>
#> 1 AAAAAAAACACGGAGAACGACG C3 2
#> 2 AAAAAAAACGCGAACAACTACG C3 1
#> 3 AAAAAAAACTCAAAAAAGAAAT C3 1
#> 4 AAAAAAAATTTACACAAAGAAA A4 1
#> 5 AAAAAAAATTTTTAAACGTACC A0 1
#> 6 AAAAAACATATCTATAGTTACC A0 1
#> 7 AAAAAAGACGACGATAGGCACG C1 1
#> 8 AAAAAAGACGTTTATAGGTGTA A2 1
#> 9 AAAAAAGACTGCGACAAAAGGG A4 1
#> 10 AAAAAAGACTTTGATAACCACG C3 1
#> # ℹ 33,257 more rows
agg_meta_functions <- tibble::tribble(
~Column, ~Function, ~Args, ~Output_colname,
"TimePoint", ~ mean(.x, na.rm = TRUE), NA, "{.col}_avg",
"CellMarker", ~ length(unique(.x)), NA, "distinct_cell_marker_count",
"ID", ~ length(unique(.x)), NA, "distinct_id_count"
)
agg_meta <- aggregate_metadata(
af,
aggregating_functions = agg_meta_functions,
grouping_keys = agg_key
)
agg_meta
#> # A tibble: 9 × 4
#> SubjectID TimePoint_avg distinct_cell_marker_count distinct_id_count
#> <chr> <dbl> <int> <int>
#> 1 A0 21 1 2
#> 2 A1 21 1 2
#> 3 A2 21 1 1
#> 4 A3 21 1 1
#> 5 A4 21 1 2
#> 6 C0 21 1 1
#> 7 C1 21 1 2
#> 8 C2 21 1 2
#> 9 C3 21 1 2
agg_stats <- sample_statistics(agg, agg_meta,
sample_key = agg_key,
value_columns = "Value_sum"
)$metadata |>
dplyr::rename(distinct_barcodes = "nIS")
agg_stats
#> # A tibble: 9 × 23
#> SubjectID TimePoint_avg distinct_cell_marker…¹ distinct_id_count Value_sum_sum
#> <chr> <dbl> <int> <int> <dbl>
#> 1 A0 21 1 2 326467
#> 2 A1 21 1 2 378987
#> 3 A2 21 1 1 124676
#> 4 A3 21 1 1 180497
#> 5 A4 21 1 2 473256
#> 6 C0 21 1 1 59966
#> 7 C1 21 1 2 399316
#> 8 C2 21 1 2 492590
#> 9 C3 21 1 2 341166
#> # ℹ abbreviated name: ¹distinct_cell_marker_count
#> # ℹ 18 more variables: Value_sum_count <int>, Value_sum_shannon <dbl>,
#> # Value_sum_simpson <dbl>, Value_sum_invsimpson <dbl>,
#> # Value_sum_describe_vars <dbl>, Value_sum_describe_n <dbl>,
#> # Value_sum_describe_mean <dbl>, Value_sum_describe_sd <dbl>,
#> # Value_sum_describe_median <dbl>, Value_sum_describe_trimmed <dbl>,
#> # Value_sum_describe_mad <dbl>, Value_sum_describe_min <dbl>, …
# Abundance ----
abundance <- compute_abundance(agg, columns = "Value_sum", key = agg_key)
abundance
#> # A tibble: 33,267 × 5
#> BarcodeSeq SubjectID Value_sum Value_sum_RelAbundance Value_sum_PercAbunda…¹
#> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 AAAAAAAACA… C3 2 0.00000586 0.000586
#> 2 AAAAAAAACG… C3 1 0.00000293 0.000293
#> 3 AAAAAAAACT… C3 1 0.00000293 0.000293
#> 4 AAAAAAAATT… A4 1 0.00000211 0.000211
#> 5 AAAAAAAATT… A0 1 0.00000306 0.000306
#> 6 AAAAAACATA… A0 1 0.00000306 0.000306
#> 7 AAAAAAGACG… C1 1 0.00000250 0.000250
#> 8 AAAAAAGACG… A2 1 0.00000802 0.000802
#> 9 AAAAAAGACT… A4 1 0.00000211 0.000211
#> 10 AAAAAAGACT… C3 1 0.00000293 0.000293
#> # ℹ 33,257 more rows
#> # ℹ abbreviated name: ¹Value_sum_PercAbundance
reset_dyn_vars_config()
#> Mandatory IS vars reset to default
#> Annotation IS vars reset to default
#> Association file columns specs reset to default
#> ISS stats specs reset to default
#> Matrix suffixes specs reset to defaultThe package provides a simple Shiny interface for data exploration and plotting. To start the interface use:
NGSdataExplorer()The application main page will show a loading screen for a file. It is possible to load files also from the R environment, for example, before opening the app, we can load the included association file:
data("association_file")Once in the application we can choose "association_file" from the
R environment loading option screen and click on “Import data”. Once
data is imported, we can click on the “Explore” tab in the upper navbar:
here we will see 2 tabs, one allows interactive exploration of data in tabular
form, in the other tab we can plot data. It is possible to customize several
different parameters for the plot and finally save it to file with the
dedicated button at the end of the page.
The Shiny interface is still currently under active development and new features will be added in the near future.
Several implemented functions produce static HTML reports that can be saved on disk, or tabular files. Reports contain the relevant information on how the function was called, inputs and outputs statistics, and session info for reproducibility.
ISAnalytics has it’s dedicated package website where you can browse the documentation and vignettes easily, in addition to keeping up to date with all relevant updates. Visit the website at https://calabrialab.github.io/ISAnalytics/
If you have any issues the documentation can’t solve, get in touch by opening an issue on GitHub or contacting the maintainers
[1] B. S. Ferrari Samuele Jacob Aurelien. “Efficient gene editing of human long-term hematopoietic stem cells validated by clonal tracking”. In: Nat Biotechnol 38, 1298–1308 (Nov. 2020). DOI: https://doi.org/10.1038/s41587-020-0551-y.