ISAnalytics 1.2.1
In this vignette we’re going to explain in detail how to use functions of the aggregate family, namely:
aggregate_metadata
aggregate_values_by_key
To install the package run the following code:
## For release version
if (!requireNamespace("BiocManager", quietly = TRUE)) {
install.packages("BiocManager")
}
BiocManager::install("ISAnalytics")
## For devel version
if (!requireNamespace("BiocManager", quietly = TRUE)) {
install.packages("BiocManager")
}
# The following initializes usage of Bioc devel
BiocManager::install(version = "devel")
BiocManager::install("ISAnalytics")
To install from GitHub:
# For release version
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"
)
# For devel version
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"
)
library(ISAnalytics)
#> Loading required package: magrittr
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.widgets" = FALSE)
# ENABLE HTML REPORTS
options("ISAnalytics.widgets" = TRUE)
We refer to information contained in the association file as “metadata”:
sometimes it’s useful to obtain collective information based on a certain
group of variables we’re interested in. The function aggregate_metadata
does just that: according to the grouping variables, meaning the names of
the columns in the association file to perform a group_by
operation with,
creates a summary. You can fully customize the summary by providing a
“function table” which tells the function which operation should be
applied to which column and what name to give to the output column.
A default is already supplied:
knitr::kable(default_meta_agg())
Column | Function | Args | Output_colname |
---|---|---|---|
FusionPrimerPCRDate | ~suppressWarnings(min(.x, na.rm = TRUE)) | NA | {.col}_min |
LinearPCRDate | ~suppressWarnings(min(.x, na.rm = TRUE)) | NA | {.col}_min |
VCN | ~suppressWarnings(mean(.x, na.rm = TRUE)) | NA | {.col}_avg |
ng DNA corrected | ~suppressWarnings(mean(.x, na.rm = TRUE)) | NA | {.col}_avg |
Kapa | ~suppressWarnings(mean(.x, na.rm = TRUE)) | NA | {.col}_avg |
ng DNA corrected | ~sum(.x, na.rm = TRUE) | NA | {.col}_sum |
ulForPool | ~sum(.x, na.rm = TRUE) | NA | {.col}_sum |
BARCODE_MUX | ~sum(.x, na.rm = TRUE) | NA | {.col}_sum |
TRIMMING_FINAL_LTRLC | ~sum(.x, na.rm = TRUE) | NA | {.col}_sum |
LV_MAPPED | ~sum(.x, na.rm = TRUE) | NA | {.col}_sum |
BWA_MAPPED_OVERALL | ~sum(.x, na.rm = TRUE) | NA | {.col}_sum |
ISS_MAPPED_PP | ~sum(.x, na.rm = TRUE) | NA | {.col}_sum |
You can either provide purrr-style lambdas (as given in the example above), or simply specify the name of the function and additional parameters as a list in a separated column. If you choose to provide your own table you should maintain the column names for the function to work properly.
Import the association file via import_assocition_file
. If you need more
information on import function please view the vignette
“How to use import functions”.
withr::with_options(list(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")
association_file <- import_association_file(path_AF, root_correct)
})
#> *** Association file import summary ***
#> ℹ For detailed report please set option 'ISAnalytics.widgets' to TRUE
#> * Parsing problems detected: TRUE
#> * Date parsing problems: TRUE
#> * Column problems detected: TRUE
#> * NAs found in important columns: TRUE
#> * File system alignment: no problems detected
#> *** Association file import summary ***
#> ℹ For detailed report please set option 'ISAnalytics.widgets' to TRUE
#> * Parsing problems detected: TRUE
#> * Date parsing problems: TRUE
#> * Column problems detected: TRUE
#> * NAs found in important columns: TRUE
#> * File system alignment: no problems detected
Perform aggregation:
aggregated_meta <- aggregate_metadata(
association_file,
grouping_keys = c(
"SubjectID",
"CellMarker",
"Tissue",
"TimePoint"
)
)
SubjectID | CellMarker | Tissue | TimePoint | FusionPrimerPCRDate_min | LinearPCRDate_min | VCN_avg | Kapa_avg | ulForPool_sum |
---|---|---|---|---|---|---|---|---|
VA2020-mix10 | NA | NA | 0000 | NA | NA | 0.70 | 26.54667 | 25.70 |
VA2020-mix11 | NA | NA | 0000 | NA | NA | 2.80 | 59.81000 | 12.29 |
VA2020-mix12 | NA | NA | 0000 | NA | NA | 4.30 | 79.80333 | 7.98 |
VA2020-mix13 | NA | NA | 0000 | NA | NA | 9.89 | 85.87000 | 7.46 |
VA2020-mix14 | NA | NA | 0000 | NA | NA | NaN | NaN | 0.00 |
ISAnalytics
contains useful functions to aggregate the values contained in
your imported matrices based on a key, aka a single column or a combination of
columns contained in the association file that are related to the samples.
Import your association file (see previous section) and then import your matrices:
withr::with_options(list(ISAnalytics.widgets = FALSE), {
matrices <- import_parallel_Vispa2Matrices_auto(
association_file = association_file, root = NULL,
quantification_type = c("fragmentEstimate", "seqCount"),
matrix_type = "annotated", workers = 2, patterns = NULL,
matching_opt = "ANY", multi_quant_matrix = FALSE
)
})
#> [1] "--- REPORT: FILES IMPORTED ---"
#> # A tibble: 8 x 5
#> ProjectID concatenatePoolIDSeqRun Quantification_type
#> <chr> <chr> <chr>
#> 1 CLOEXP POOL6-1 fragmentEstimate
#> 2 PROJECT1100 ABX-LR-PL5-POOL14-1 fragmentEstimate
#> 3 PROJECT1100 ABX-LR-PL6-POOL15-1 fragmentEstimate
#> 4 PROJECT1101 ABY-LR-PL4-POOL54-2 fragmentEstimate
#> 5 CLOEXP POOL6-1 seqCount
#> 6 PROJECT1100 ABX-LR-PL5-POOL14-1 seqCount
#> 7 PROJECT1100 ABX-LR-PL6-POOL15-1 seqCount
#> 8 PROJECT1101 ABY-LR-PL4-POOL54-2 seqCount
#> Files_chosen
#> <fs::path>
#> 1 /tmp/RtmpUgNaF5/fs/VA/CLOEXP/quantification/POOL6-1/VA-CLOEXP-POOL6-1_fragmen…
#> 2 /tmp/RtmpUgNaF5/fs/PROJECT1100/quantification/ABX-LR-PL5-POOL14-1/PROJECT1100…
#> 3 /tmp/RtmpUgNaF5/fs/PROJECT1100/quantification/ABX-LR-PL6-POOL15-1/PROJECT1100…
#> 4 /tmp/RtmpUgNaF5/fs/PROJECT1101/quantification/ABY-LR-PL4-POOL54-2/PROJECT1101…
#> 5 /tmp/RtmpUgNaF5/fs/VA/CLOEXP/quantification/POOL6-1/VA-CLOEXP-POOL6-1_seqCoun…
#> 6 /tmp/RtmpUgNaF5/fs/PROJECT1100/quantification/ABX-LR-PL5-POOL14-1/PROJECT1100…
#> 7 /tmp/RtmpUgNaF5/fs/PROJECT1100/quantification/ABX-LR-PL6-POOL15-1/PROJECT1100…
#> 8 /tmp/RtmpUgNaF5/fs/PROJECT1101/quantification/ABY-LR-PL4-POOL54-2/PROJECT1101…
#> Imported
#> <lgl>
#> 1 TRUE
#> 2 TRUE
#> 3 TRUE
#> 4 TRUE
#> 5 TRUE
#> 6 TRUE
#> 7 TRUE
#> 8 TRUE
#> [1] "--- SUMMARY OF FILES CHOSEN FOR IMPORT ---"
#> # A tibble: 8 x 4
#> ProjectID concatenatePoolIDSeqRun Quantification_type
#> <chr> <chr> <chr>
#> 1 CLOEXP POOL6-1 fragmentEstimate
#> 2 CLOEXP POOL6-1 seqCount
#> 3 PROJECT1100 ABX-LR-PL5-POOL14-1 fragmentEstimate
#> 4 PROJECT1100 ABX-LR-PL5-POOL14-1 seqCount
#> 5 PROJECT1100 ABX-LR-PL6-POOL15-1 fragmentEstimate
#> 6 PROJECT1100 ABX-LR-PL6-POOL15-1 seqCount
#> 7 PROJECT1101 ABY-LR-PL4-POOL54-2 fragmentEstimate
#> 8 PROJECT1101 ABY-LR-PL4-POOL54-2 seqCount
#> Files_chosen
#> <fs::path>
#> 1 /tmp/RtmpUgNaF5/fs/VA/CLOEXP/quantification/POOL6-1/VA-CLOEXP-POOL6-1_fragmen…
#> 2 /tmp/RtmpUgNaF5/fs/VA/CLOEXP/quantification/POOL6-1/VA-CLOEXP-POOL6-1_seqCoun…
#> 3 /tmp/RtmpUgNaF5/fs/PROJECT1100/quantification/ABX-LR-PL5-POOL14-1/PROJECT1100…
#> 4 /tmp/RtmpUgNaF5/fs/PROJECT1100/quantification/ABX-LR-PL5-POOL14-1/PROJECT1100…
#> 5 /tmp/RtmpUgNaF5/fs/PROJECT1100/quantification/ABX-LR-PL6-POOL15-1/PROJECT1100…
#> 6 /tmp/RtmpUgNaF5/fs/PROJECT1100/quantification/ABX-LR-PL6-POOL15-1/PROJECT1100…
#> 7 /tmp/RtmpUgNaF5/fs/PROJECT1101/quantification/ABY-LR-PL4-POOL54-2/PROJECT1101…
#> 8 /tmp/RtmpUgNaF5/fs/PROJECT1101/quantification/ABY-LR-PL4-POOL54-2/PROJECT1101…
#> [1] "--- INTEGRATION MATRICES FOUND REPORT ---"
#> # A tibble: 8 x 5
#> ProjectID concatenatePoolIDSeqRun Anomalies Quantification_type
#> <chr> <chr> <lgl> <chr>
#> 1 CLOEXP POOL6-1 FALSE fragmentEstimate
#> 2 CLOEXP POOL6-1 FALSE seqCount
#> 3 PROJECT1100 ABX-LR-PL5-POOL14-1 FALSE fragmentEstimate
#> 4 PROJECT1100 ABX-LR-PL5-POOL14-1 FALSE seqCount
#> 5 PROJECT1100 ABX-LR-PL6-POOL15-1 FALSE fragmentEstimate
#> 6 PROJECT1100 ABX-LR-PL6-POOL15-1 FALSE seqCount
#> 7 PROJECT1101 ABY-LR-PL4-POOL54-2 FALSE fragmentEstimate
#> 8 PROJECT1101 ABY-LR-PL4-POOL54-2 FALSE seqCount
#> Files_found
#> <fs::path>
#> 1 /tmp/RtmpUgNaF5/fs/VA/CLOEXP/quantification/POOL6-1/VA-CLOEXP-POOL6-1_fragmen…
#> 2 /tmp/RtmpUgNaF5/fs/VA/CLOEXP/quantification/POOL6-1/VA-CLOEXP-POOL6-1_seqCoun…
#> 3 /tmp/RtmpUgNaF5/fs/PROJECT1100/quantification/ABX-LR-PL5-POOL14-1/PROJECT1100…
#> 4 /tmp/RtmpUgNaF5/fs/PROJECT1100/quantification/ABX-LR-PL5-POOL14-1/PROJECT1100…
#> 5 /tmp/RtmpUgNaF5/fs/PROJECT1100/quantification/ABX-LR-PL6-POOL15-1/PROJECT1100…
#> 6 /tmp/RtmpUgNaF5/fs/PROJECT1100/quantification/ABX-LR-PL6-POOL15-1/PROJECT1100…
#> 7 /tmp/RtmpUgNaF5/fs/PROJECT1101/quantification/ABY-LR-PL4-POOL54-2/PROJECT1101…
#> 8 /tmp/RtmpUgNaF5/fs/PROJECT1101/quantification/ABY-LR-PL4-POOL54-2/PROJECT1101…
The function aggregate_values_by_key
can perform the aggregation both on the
list of matrices and a single matrix.
# Takes the whole list and produces a list in output
aggregated_matrices <- aggregate_values_by_key(matrices, association_file)
# Takes a single matrix and produces a single matrix as output
aggregated_matrices_single <- aggregate_values_by_key(
matrices$seqCount,
association_file
)
chr | integration_locus | strand | GeneName | GeneStrand | SubjectID | CellMarker | Tissue | TimePoint | Value_sum |
---|---|---|---|---|---|---|---|---|---|
1 | 4519734 | + | LINC01777 | + | VA2020-mix10 | NA | NA | 0000 | 8 |
1 | 8426336 | - | RERE | - | VA2020-mix10 | NA | NA | 0000 | 4 |
1 | 8884086 | - | RERE | - | VA2020-mix11 | NA | NA | 0000 | 4 |
1 | 11322005 | - | MTOR | - | VA2020-mix10 | NA | NA | 0000 | 4 |
1 | 11772847 | + | DRAXIN | + | VA2020-mix11 | NA | NA | 0000 | 52 |
1 | 22453532 | + | WNT4 | - | VA2020-mix10 | NA | NA | 0000 | 8 |
The function has several different parameters that have default values that can be changed according to user preference.
key
valuec("SubjectID", "CellMarker", "Tissue", "TimePoint")
(same default key as the aggregate_metadata
function).agg1 <- aggregate_values_by_key(
x = matrices$seqCount,
association_file = association_file,
key = c("SubjectID", "ProjectID")
)
chr | integration_locus | strand | GeneName | GeneStrand | SubjectID | ProjectID | Value_sum |
---|---|---|---|---|---|---|---|
1 | 4519734 | + | LINC01777 | + | VA2020-mix10 | CLOEXP | 2 |
1 | 4519734 | + | LINC01777 | + | VA2020-mix10 | PROJECT1100 | 4 |
1 | 4519734 | + | LINC01777 | + | VA2020-mix10 | PROJECT1101 | 2 |
1 | 8426336 | - | RERE | - | VA2020-mix10 | CLOEXP | 1 |
1 | 8426336 | - | RERE | - | VA2020-mix10 | PROJECT1100 | 2 |
1 | 8426336 | - | RERE | - | VA2020-mix10 | PROJECT1101 | 1 |
lambda
valuelambda
parameter indicates the function(s) to be applied to the
values for aggregation.
lambda
must be a named list of either functions or purrr-style lambdas:
if you would like to specify additional parameters to the function
the second option is recommended.
The only important note on functions is that they should perform some kind of
aggregation on numeric values: this means in practical terms they need
to accept a vector of numeric/integer values as input and produce a
SINGLE value as output. Valid options for this purpose might be: sum
, mean
,
median
, min
, max
and so on.agg2 <- aggregate_values_by_key(
x = matrices$seqCount,
association_file = association_file,
key = "SubjectID",
lambda = list(mean = ~ mean(.x, na.rm = TRUE))
)
chr | integration_locus | strand | GeneName | GeneStrand | SubjectID | Value_mean |
---|---|---|---|---|---|---|
1 | 4519734 | + | LINC01777 | + | VA2020-mix10 | 2 |
1 | 8426336 | - | RERE | - | VA2020-mix10 | 1 |
1 | 8884086 | - | RERE | - | VA2020-mix11 | 1 |
1 | 11322005 | - | MTOR | - | VA2020-mix10 | 1 |
1 | 11772847 | + | DRAXIN | + | VA2020-mix11 | 13 |
1 | 22453532 | + | WNT4 | - | VA2020-mix10 | 2 |
Note that, when specifying purrr-style lambdas (formulas), the first
parameter needs to be set to .x
, other parameters can be set as usual.
You can also use in lambda
functions that produce data frames or lists.
In this case all variables from the produced data frame will be included
in the final data frame. For example:
agg3 <- aggregate_values_by_key(
x = matrices$seqCount,
association_file = association_file,
key = "SubjectID",
lambda = list(describe = psych::describe)
)
agg3
#> # A tibble: 1,272 x 7
#> chr integration_locus strand GeneName GeneStrand SubjectID Value_describe$…
#> <chr> <int> <chr> <chr> <chr> <chr> <dbl>
#> 1 1 4519734 + LINC017… + VA2020-m… 1
#> 2 1 8426336 - RERE - VA2020-m… 1
#> 3 1 8884086 - RERE - VA2020-m… 1
#> 4 1 11322005 - MTOR - VA2020-m… 1
#> 5 1 11772847 + DRAXIN + VA2020-m… 1
#> 6 1 22453532 + WNT4 - VA2020-m… 1
#> 7 1 25929388 - MAN1C1 + VA2020-m… 1
#> 8 1 26950561 - RPS6KA1 + VA2020-m… 1
#> 9 1 29116798 - YTHDF2 + VA2020-m… 1
#> 10 1 30825358 - LINC016… - VA2020-m… 1
#> # … with 1,262 more rows
value_cols
valuevalue_cols
parameter tells the function on which numeric columns
of x the functions should be applied.
NOte that every function contained in lambda
will be applied to every
column in value_cols
: resulting columns will be named as
“original name_function applied”.## Obtaining multi-quantification matrix
comp <- comparison_matrix(matrices)
agg4 <- aggregate_values_by_key(
x = comp,
association_file = association_file,
key = "SubjectID",
lambda = list(sum = sum, mean = mean),
value_cols = c("seqCount", "fragmentEstimate")
)
chr | integration_locus | strand | GeneName | GeneStrand | SubjectID | seqCount_sum | seqCount_mean | fragmentEstimate_sum | fragmentEstimate_mean |
---|---|---|---|---|---|---|---|---|---|
1 | 4519734 | + | LINC01777 | + | VA2020-mix10 | 8 | 2 | 8.010711 | 2.002678 |
1 | 8426336 | - | RERE | - | VA2020-mix10 | 4 | 1 | 4.002137 | 1.000534 |
1 | 8884086 | - | RERE | - | VA2020-mix11 | 4 | 1 | 4.004477 | 1.001119 |
1 | 11322005 | - | MTOR | - | VA2020-mix10 | 4 | 1 | 4.002002 | 1.000501 |
1 | 11772847 | + | DRAXIN | + | VA2020-mix11 | 52 | 13 | 4.000695 | 1.000174 |
1 | 22453532 | + | WNT4 | - | VA2020-mix10 | 8 | 2 | 4.005724 | 1.001431 |
group
valuegroup
parameter should contain all other variables to include in the
grouping besides key
. By default this contains c("chr", "integration_locus", "strand", "GeneName", "GeneStrand")
. You can change this grouping as you see
fit, if you don’t want to add any other variable to the key, just set it to
NULL.agg5 <- aggregate_values_by_key(
x = matrices$seqCount,
association_file = association_file,
key = "SubjectID",
lambda = list(sum = sum, mean = mean),
group = c(mandatory_IS_vars())
)
chr | integration_locus | strand | SubjectID | Value_sum | Value_mean |
---|---|---|---|---|---|
1 | 4519734 | + | VA2020-mix10 | 8 | 2 |
1 | 8426336 | - | VA2020-mix10 | 4 | 1 |
1 | 8884086 | - | VA2020-mix11 | 4 | 1 |
1 | 11322005 | - | VA2020-mix10 | 4 | 1 |
1 | 11772847 | + | VA2020-mix11 | 52 | 13 |
1 | 22453532 | + | VA2020-mix10 | 8 | 2 |
The ISAnalytics package (calabrialab, 2021) was made possible thanks to:
This package was developed using biocthis.
R
session information.
#> ─ Session info ───────────────────────────────────────────────────────────────────────────────────────────────────────
#> setting value
#> version R version 4.1.0 (2021-05-18)
#> os Ubuntu 20.04.2 LTS
#> system x86_64, linux-gnu
#> ui X11
#> language (EN)
#> collate C
#> ctype en_US.UTF-8
#> tz America/New_York
#> date 2021-06-10
#>
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This vignette was generated using BiocStyle (Oleś, 2021) with knitr (Xie, 2021) and rmarkdown (Allaire, Xie, McPherson, Luraschi, et al., 2021) running behind the scenes.
Citations made with knitcitations (Boettiger, 2021).
[1] J. Allaire, Y. Xie, J. McPherson, J. Luraschi, et al. rmarkdown: Dynamic Documents for R. R package version 2.8. 2021. <URL: https://github.com/rstudio/rmarkdown>.
[2] C. Boettiger. knitcitations: Citations for ‘Knitr’ Markdown Files. R package version 1.0.12. 2021. <URL: https://CRAN.R-project.org/package=knitcitations>.
[3] G. Csárdi, R. core, H. Wickham, W. Chang, et al. sessioninfo: R Session Information. R package version 1.1.1. 2018. <URL: https://CRAN.R-project.org/package=sessioninfo>.
[4] A. Oleś. BiocStyle: Standard styles for vignettes and other Bioconductor documents. R package version 2.20.0. 2021. <URL: https://github.com/Bioconductor/BiocStyle>.
[5] R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. Vienna, Austria, 2021. <URL: https://www.R-project.org/>.
[6] H. Wickham. “testthat: Get Started with Testing”. In: The R Journal 3 (2011), pp. 5-10. <URL: https://journal.r-project.org/archive/2011-1/RJournal_2011-1_Wickham.pdf>.
[7] Y. Xie. knitr: A General-Purpose Package for Dynamic Report Generation in R. R package version 1.33. 2021. <URL: https://yihui.org/knitr/>.
[8] calabrialab. Analyze gene therapy vector insertion sites data identified from genomics next generation sequencing reads for clonal tracking studies. https://github.com/calabrialab/ISAnalytics - R package version 1.2.1. 2021. DOI: 10.18129/B9.bioc.ISAnalytics. <URL: http://www.bioconductor.org/packages/ISAnalytics>.