create_qc_report: Create a MeTaQuaC QC report based on Biocrates data (as...
In bihealth/metaquac: Metabolomics Targeted Quality Control
Description
Usage
Arguments
Value
Examples
View source: R/report_creation.R
Description
Create a MeTaQuaC QC report based on Biocrates data (as exported by MetIDQ).
Usage
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create_qc_report(
data_files,
kit = c("Biocrates Bile Acids Kit", "Biocrates AbsoluteIDQ p180 Kit",
"Biocrates AbsoluteIDQ p400 HR Kit", "Biocrates AbsoluteIDQ Stero17 Kit",
"Biocrates MxP Quant 500 Kit", "Generic Data")[5],
measurement_type = c("LC", "FIA")[1],
generic_data_types = c(CONCENTRATION = "Concentration", AREA = "Area", INTENSITY =
NULL, ISTD_AREA = NULL, ISTD_INTENSITY = NULL, STATUS = NULL),
generic_index_first_compound = NULL,
title = ifelse(kit %in% KITS_BIOCRATES, "Biocrates QC Report",
"Generic Data QC Report"),
author = unname(Sys.info()["user"]),
report_output_name = paste0(format(Sys.time(), "%Y%m%d_%H%M%S"), "_qc_report_",
measurement_type),
report_output_dir = "reports",
report_output_clean = TRUE,
pool_indicator = "Sample.Identification",
profiling_variables = NULL,
study_variables = NULL,
replicate_variables = NULL,
preproc_keep_status = c("Valid", "Semi Quant.", "Smaller Zero", "< LOD", "< LLOQ",
"> ULOQ", "No Intercept", "Missing Measurement", "ISTD Out of Range",
"STD/QC < Limit", "STD/QC > Limit", "Invalid", "Incomplete",
"Blank Out of Range")[1:2],
preproc_q500_urine_limits = FALSE,
filter_compound_qc_ref_max_mv_ratio = 0.3,
filter_compound_qc_ref_max_rsd = 15,
filter_compound_qc_pool_max_mv_ratio = 0.3,
filter_compound_qc_pool_max_rsd = 15,
filter_compound_bs_max_mv_ratio = 0.3,
filter_compound_bs_min_rsd = 15,
filter_sample_max_mv_ratio = 0.2,
data_tables = c("all", "stats", "none")[1],
data_export_long = FALSE,
data_export_wide = FALSE,
data_export_prefix = file.path(report_output_dir, report_output_name),
metadata_import = NULL,
metadata_import_overlap = c("rename", "replace", "omit")[1],
metadata_name_mods_org = NULL,
metadata_name_mods_add = NULL,
metadata_value_mods = NULL,
lowcon_conditions = NULL,
lowcon_sd_outlier_removal = FALSE,
lowcon_minimum_intensity = 20000,
...
)
Arguments
data_files
Data files to import. List MetIDQ data (txt), as an R list providing the
files per batch via named vectors. E.g.
list(Batch1 = c("Batch1_LC1.txt", "Batch1_LC2.txt"),
Batch2 = c("Batch2_LC1.txt","Batch2_LC2.txt")).
For generic data, use only one list element, but name the data types within
the vector (to be mapped via generic_data_types). E.g.
list(c("Concentration [ng/ml]" = "data_conc.tsv", "Area" = "data_area.tsv",
"Status" = "data_status.tsv")).
Listing batches via different files is currently not support for generic
data, but can be indicated via a "Batch" column within the data.
kit
The Biocrates Kit used to create the data to import.
Currently supported are
"Biocrates Bile Acids Kit"
"Biocrates AbsoluteIDQ p180 Kit",
"Biocrates AbsoluteIDQ p400 HR Kit",
"Biocrates MxP Quant 500 Kit",
"Biocrates AbsoluteIDQ Stero17 Kit" and
"Generic Data"
(default = "Biocrates AbsoluteIDQ p400 HR Kit").
measurement_type
The measurement type (i.e. injection type) of the
data to import, i.e. either "LC" or "FIA" (default = "LC").
Only relevant for Biocrates data.
generic_data_types
For generic data, indicate which file contains which data using the names
defined in data_files. This way MeTaQuaC knows which type of data is
available and allows for custom naming in the report (e.g. to include units).
E.g.
c(CONCENTRATION = "Concentration [ng/ml]", AREA = "Area", STATUS = "Status").
Currently supported data type mappings are
CONCENTRATION, AREA, INTENSITY, ISTD_AREA, ISTD_INTENSITY, STATUS
generic_index_first_compound
For generic data, indicate which columns contains the first compound after
the sample annotation columns (i.e. the beginning of the data matrix).
Note: Please double check this parameter, as with a wrong selection data
transformation and merging will fail.
title
Custom title for report (default = "Biocrates QC Report").
author
Name of the person responsible for creating the report (default = system user).
report_output_name
Custom name of report file (default =
"YYYYMMDD_HHMMSS_qc_report_{LC|FIA}").
report_output_dir
Custom path of an output directory (default = "reports").
report_output_clean
Indicate if intermediate report files (the final
markdown document and figures) should be removed (default) or should remain.
pool_indicator
Indicate a column/variable name which should be scanned for pooled QC samples
(Biocrates only). (default = "Sample Identification", set NULL to disable).
All samples containing "pool" (case insensitive) anywhere in this variable's
values are transformed to Sample Type = "Pooled QC". For generic data, please
indicate pooled samples within the data using value "Pooled QC" in column
"Sample Type".
profiling_variables
Indicate a vector of study variables of interest which will be
used for profiling group sizes (i.e. number of samples) within a variable but also the size of
group intersections between these variables. It is recommended to keep the variables limited to
factors of primary interest (for instance disease status or treatment and sex), otherwise
intersections might end up rather small.
study_variables
Indicate a list (actual R list!) of study variables of interest.
These will be used to create group-colored versions of some plots to illustrate group
differences. Nested variables are possible by including named sublists, whereby plots
will be created recursively based on data filtered by groups in the list-naming variable.
replicate_variables
Indicate a vector of study variables which donate the unique
grouping of samples (could be as simple as a patient identifier or several conditions)
Samples with the same characteristic in these variables are considered as replicates
for compound and class %RSD analysis plots. If the data contains "BR" and "TR" columns
these plots will be extended for technical replicates.
preproc_keep_status
Indicate which values are acceptable for processing with respect to
(Biocrates) statuses. The default includes only "Valid" and "Semi Quant."
measurements, the rest is discarded (i.e. transformed to missing values, set
to NA). For Biocrates, possible statuses to select from include "Valid",
"Semi Quant.", "Smaller Zero", "< LOD", "< LLOQ", "> ULOQ", "No Intercept",
"Missing Measurement", "ISTD Out of Range", "STD/QC < Limit",
"STD/QC > Limit", "Invalid", "Incomplete" and "Blank Out of Range".
For generic data, status values may differ depending on the software used.
preproc_q500_urine_limits
Only to be used in the special case of using the Biocrates MxP Quant 500 Kit
LC data with urine samples and additional calibration samples (cal 0.25 and
cal 9)! As MetIDQ doesn't consider the additional calibration samples when
calculating limits of detection ("< LLOQ", "> ULOQ"), status values for
calibrated compounds will be corrected based on cal 0.25 and cal 9
concentrations (default = FALSE).
filter_compound_qc_ref_max_mv_ratio
Set maximum ratio of missing values allowed for compounds in reference QC
samples (Biocrates' QC Level 2, Reference QC in generic data)
(default = 0.3, exclusive, disable with NULL).
filter_compound_qc_ref_max_rsd
Set maximum %RSD allowed for compounds in reference QC samples
(Biocrates' QC Level 2, Reference QC in generic data)
(default = 15%, exclusive, disable with NULL).
filter_compound_qc_pool_max_mv_ratio
Set maximum ratio of missing values allowed for compounds in pooled QC
samples (Pooled QC)
(default = 0.3, exclusive, disable with NULL).
filter_compound_qc_pool_max_rsd
Set maximum %RSD allowed for compounds in pooled QC samples (Pooled QC)
(default = 15%, exclusive, disable with NULL).
filter_compound_bs_max_mv_ratio
Set maximum ratio of missing values allowed for compounds in biological
samples (Sample) (default = 0.3, exclusive, disable with NULL).
filter_compound_bs_min_rsd
Set minimum %RSD allowed for compounds in biological samples (Sample)
(default = 15%, exclusive, disable with NULL).
filter_sample_max_mv_ratio
Set maximum ratio of missing values allowed per biological sample (Sample)
(default < 0.2, exclusive, disable with NULL).
data_tables
Control data tables availability in reports. "all" (default) will show all
implemented data tables (with csv export buttons). "stats" will only show tables of summarized
data (such as countings,
pre-processed). "none" will show no data tables at all, i.e. the report is mainly limited to
visualizations.
data_export_long, data_export_wide
Enable export of measurement data sets (default = FALSE).
Use in combination with data_tables = "stats", to keep big data sets
exclusively outside of the report (e.g. to prevent memory issues either
during pandoc conversion or when viewing the report in a browser).
data_export_prefix
File path prefix to apply for data exports.
(default = report_output_dir + report_output_name).
metadata_import
Indicate a text file (csv or tsv) with additional
metadata/annotations to import and merge (by column "Sample Identification").
metadata_import_overlap
Specify the handling of overlaping columns.
"rename" (default) will extend duplicate column names in the metadata import
with ".D" to make them unique, where D is an increasing number according to
the occurance of the same name. Keep this in mind when indicating variables.
"replace" will replace original columns with new metadata columns.
"omit" will keep original columns and ignore new metadata columns.
metadata_name_mods_org
Rename columns in the original data.
This is applied **before** the import the of additional metadata, if any.
Consider that non-unique column names are modified in general. Names not in
the data are ignored. Use a named vector to indicate columns to rename, e.g.:
c(oldname1 = "newname1", oldname2 = "newname2", ...)
metadata_name_mods_add
Rename columns in original and added metadata.
This is applied **after** the import of additional metadata, if any. Consider
that non-unique column names are modified in general. Names not in the data
are ignored. Use a named vector to indicate columns to rename, e.g.:
c(oldname1 = "newname1", oldname2 = "newname2", ...)
metadata_value_mods
Batch change values in the data, e.g. to correct
sample identifiers, groups, etc. This is applied after metadata import and
renaming, if any. Indicated columns and values not in the data are ignored.
Use a named list, with names indicating the columns and named vectors
indicate the changes to apply, e.g.:
list("columnX" = c("oldvalueA" = "newvalueA", "oldvalueB" = "newvalueB"),
lowcon_conditions
Indicate a vector of study variables of interest
which will be applied to additional reproducibility analysis designed for low
concentration data below the limit of quantification. Hence, this analysis
is performed on completely unfiltered data using area (LC) or intensity
(FIA), resp.
lowcon_sd_outlier_removal
If set to TRUE, data of sample groups with
same conditions (i.e. combination of indicated study variables) is removed if
SD is higher than 1.5 or not available (e.g. when group consists of only one
sample). This is ment to reject unreliable technical replicates and not
recommended to apply on actual study samples and thus biological variance.
lowcon_minimum_intensity
Minimal intensity threshold to keep values in
low concentration analysis (default = 20000). Below, values are set to NA.
...
Masked parameters for development and testing only.
Value
Returns TRUE to indicate if a report was successfully created.
Note, however, errors may still occur withing the report,
sometimes more disruptive (e.g. failed imports),
sometimes less disruptive (e.g. a single plot failing).
Examples
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# Note: These examples make use of data sets included in the package,
# hence the use of the system.file function.
# Normally, please provide file paths strings directly instead,
# e.g. data_files = list(Batch1 = c("filepath")).
# Biocrates MxP Quant 500 Kit - LC injection
metaquac::create_qc_report(
data_files = list(
Batch1 = c(
system.file("extdata", "biocrates_q500_test_01/Batch1_LC.txt", package = "metaquac"))),
kit = "Biocrates MxP Quant 500 Kit",
measurement_type = "LC",
title = "Biocrates QC - Q500 - LC",
report_output_name = "biocrates_qc_q500_lc",
report_output_dir = "biocrates_q500_test",
pool_indicator = "Sex",
profiling_variables = c('Sex'),
study_variables = list('Sex'),
replicate_variables = c('Sex')
)
# Biocrates MxP Quant 500 Kit - FIA injection
metaquac::create_qc_report(
data_files = list(
Batch1 = c(
system.file("extdata", "biocrates_q500_test_01/Batch1_FIA.txt", package = "metaquac"))),
kit = "Biocrates MxP Quant 500 Kit",
measurement_type = "FIA",
title = "Biocrates QC - Q500 - FIA",
report_output_name = "biocrates_qc_q500_fia",
report_output_dir = "biocrates_q500_test",
pool_indicator = "Sex",
profiling_variables = c('Sex'),
study_variables = list('Sex'),
replicate_variables = c('Sex')
)
# Biocrates AbsoluteIDQ p400 HR Kit - LC injection
metaquac::create_qc_report(
data_files = list(
Batch1 = c(
system.file("extdata", "biocrates_p400_test_01/Batch1_LC1.txt", package = "metaquac"),
system.file("extdata", "biocrates_p400_test_01/Batch1_LC2.txt", package = "metaquac")),
Batch2 = c(
system.file("extdata", "biocrates_p400_test_01/Batch2_LC1.txt", package = "metaquac"),
system.file("extdata", "biocrates_p400_test_01/Batch2_LC2.txt", package = "metaquac")),
Batch3 = c(
system.file("extdata", "biocrates_p400_test_01/Batch3_LC1.txt", package = "metaquac"),
system.file("extdata", "biocrates_p400_test_01/Batch3_LC2.txt", package = "metaquac")),
Batch4 = c(
system.file("extdata", "biocrates_p400_test_01/Batch4_LC1.txt", package = "metaquac"),
system.file("extdata", "biocrates_p400_test_01/Batch4_LC2.txt", package = "metaquac"))),
kit = "Biocrates AbsoluteIDQ p400 HR Kit",
measurement_type = "LC",
title = "Biocrates QC - p400 - LC",
report_output_name = "biocrates_qc_p400_lc",
report_output_dir = "biocrates_p400_test",
pool_indicator = "Sample.Identification",
profiling_variables = c("Group", "Condition"),
study_variables = list("Group", "Condition", "Group" = list("Condition")),
replicate_variables = c("Group", "Condition")
)
# Biocrates AbsoluteIDQ p400 HR Kit - FIA injection
metaquac::create_qc_report(
data_files = list(
Batch1 = c(
system.file("extdata", "biocrates_p400_test_01/Batch1_FIA1.txt", package = "metaquac"),
system.file("extdata", "biocrates_p400_test_01/Batch1_FIA2.txt", package = "metaquac")),
Batch2 = c(
system.file("extdata", "biocrates_p400_test_01/Batch2_FIA1.txt", package = "metaquac"),
system.file("extdata", "biocrates_p400_test_01/Batch2_FIA2.txt", package = "metaquac")),
Batch3 = c(
system.file("extdata", "biocrates_p400_test_01/Batch3_FIA1.txt", package = "metaquac"),
system.file("extdata", "biocrates_p400_test_01/Batch3_FIA2.txt", package = "metaquac")),
Batch4 = c(
system.file("extdata", "biocrates_p400_test_01/Batch4_FIA1.txt", package = "metaquac"),
system.file("extdata", "biocrates_p400_test_01/Batch4_FIA2.txt", package = "metaquac"))),
kit = "Biocrates AbsoluteIDQ p400 HR Kit",
measurement_type = "FIA",
title = "Biocrates QC - p400 - FIA",
report_output_name = "biocrates_qc_p400_fia",
report_output_dir = "biocrates_p400_test",
pool_indicator = "Sample.Identification",
profiling_variables = c("Group", "Condition"),
study_variables = list("Group", "Condition", "Group" = list("Condition")),
replicate_variables = c("Group", "Condition")
)
bihealth/metaquac documentation built on Aug. 7, 2021, 8:40 a.m.
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Description Usage Arguments Value Examples
View source: R/report_creation.R
Description
Create a MeTaQuaC QC report based on Biocrates data (as exported by MetIDQ).
Usage
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 | create_qc_report(
data_files,
kit = c("Biocrates Bile Acids Kit", "Biocrates AbsoluteIDQ p180 Kit",
"Biocrates AbsoluteIDQ p400 HR Kit", "Biocrates AbsoluteIDQ Stero17 Kit",
"Biocrates MxP Quant 500 Kit", "Generic Data")[5],
measurement_type = c("LC", "FIA")[1],
generic_data_types = c(CONCENTRATION = "Concentration", AREA = "Area", INTENSITY =
NULL, ISTD_AREA = NULL, ISTD_INTENSITY = NULL, STATUS = NULL),
generic_index_first_compound = NULL,
title = ifelse(kit %in% KITS_BIOCRATES, "Biocrates QC Report",
"Generic Data QC Report"),
author = unname(Sys.info()["user"]),
report_output_name = paste0(format(Sys.time(), "%Y%m%d_%H%M%S"), "_qc_report_",
measurement_type),
report_output_dir = "reports",
report_output_clean = TRUE,
pool_indicator = "Sample.Identification",
profiling_variables = NULL,
study_variables = NULL,
replicate_variables = NULL,
preproc_keep_status = c("Valid", "Semi Quant.", "Smaller Zero", "< LOD", "< LLOQ",
"> ULOQ", "No Intercept", "Missing Measurement", "ISTD Out of Range",
"STD/QC < Limit", "STD/QC > Limit", "Invalid", "Incomplete",
"Blank Out of Range")[1:2],
preproc_q500_urine_limits = FALSE,
filter_compound_qc_ref_max_mv_ratio = 0.3,
filter_compound_qc_ref_max_rsd = 15,
filter_compound_qc_pool_max_mv_ratio = 0.3,
filter_compound_qc_pool_max_rsd = 15,
filter_compound_bs_max_mv_ratio = 0.3,
filter_compound_bs_min_rsd = 15,
filter_sample_max_mv_ratio = 0.2,
data_tables = c("all", "stats", "none")[1],
data_export_long = FALSE,
data_export_wide = FALSE,
data_export_prefix = file.path(report_output_dir, report_output_name),
metadata_import = NULL,
metadata_import_overlap = c("rename", "replace", "omit")[1],
metadata_name_mods_org = NULL,
metadata_name_mods_add = NULL,
metadata_value_mods = NULL,
lowcon_conditions = NULL,
lowcon_sd_outlier_removal = FALSE,
lowcon_minimum_intensity = 20000,
...
)
|
Arguments
data_files |
Data files to import. List MetIDQ data (txt), as an R list providing the files per batch via named vectors. E.g. list(Batch1 = c("Batch1_LC1.txt", "Batch1_LC2.txt"), Batch2 = c("Batch2_LC1.txt","Batch2_LC2.txt")). For generic data, use only one list element, but name the data types within the vector (to be mapped via generic_data_types). E.g. list(c("Concentration [ng/ml]" = "data_conc.tsv", "Area" = "data_area.tsv", "Status" = "data_status.tsv")). Listing batches via different files is currently not support for generic data, but can be indicated via a "Batch" column within the data. |
kit |
The Biocrates Kit used to create the data to import. Currently supported are "Biocrates Bile Acids Kit" "Biocrates AbsoluteIDQ p180 Kit", "Biocrates AbsoluteIDQ p400 HR Kit", "Biocrates MxP Quant 500 Kit", "Biocrates AbsoluteIDQ Stero17 Kit" and "Generic Data" (default = "Biocrates AbsoluteIDQ p400 HR Kit"). |
measurement_type |
The measurement type (i.e. injection type) of the data to import, i.e. either "LC" or "FIA" (default = "LC"). Only relevant for Biocrates data. |
generic_data_types |
For generic data, indicate which file contains which data using the names defined in data_files. This way MeTaQuaC knows which type of data is available and allows for custom naming in the report (e.g. to include units). E.g. c(CONCENTRATION = "Concentration [ng/ml]", AREA = "Area", STATUS = "Status"). Currently supported data type mappings are CONCENTRATION, AREA, INTENSITY, ISTD_AREA, ISTD_INTENSITY, STATUS |
generic_index_first_compound |
For generic data, indicate which columns contains the first compound after the sample annotation columns (i.e. the beginning of the data matrix). Note: Please double check this parameter, as with a wrong selection data transformation and merging will fail. |
title |
Custom title for report (default = "Biocrates QC Report"). |
author |
Name of the person responsible for creating the report (default = system user). |
report_output_name |
Custom name of report file (default = "YYYYMMDD_HHMMSS_qc_report_{LC|FIA}"). |
report_output_dir |
Custom path of an output directory (default = "reports"). |
report_output_clean |
Indicate if intermediate report files (the final markdown document and figures) should be removed (default) or should remain. |
pool_indicator |
Indicate a column/variable name which should be scanned for pooled QC samples (Biocrates only). (default = "Sample Identification", set NULL to disable). All samples containing "pool" (case insensitive) anywhere in this variable's values are transformed to Sample Type = "Pooled QC". For generic data, please indicate pooled samples within the data using value "Pooled QC" in column "Sample Type". |
profiling_variables |
Indicate a vector of study variables of interest which will be used for profiling group sizes (i.e. number of samples) within a variable but also the size of group intersections between these variables. It is recommended to keep the variables limited to factors of primary interest (for instance disease status or treatment and sex), otherwise intersections might end up rather small. |
study_variables |
Indicate a list (actual R list!) of study variables of interest. These will be used to create group-colored versions of some plots to illustrate group differences. Nested variables are possible by including named sublists, whereby plots will be created recursively based on data filtered by groups in the list-naming variable. |
replicate_variables |
Indicate a vector of study variables which donate the unique grouping of samples (could be as simple as a patient identifier or several conditions) Samples with the same characteristic in these variables are considered as replicates for compound and class %RSD analysis plots. If the data contains "BR" and "TR" columns these plots will be extended for technical replicates. |
preproc_keep_status |
Indicate which values are acceptable for processing with respect to (Biocrates) statuses. The default includes only "Valid" and "Semi Quant." measurements, the rest is discarded (i.e. transformed to missing values, set to NA). For Biocrates, possible statuses to select from include "Valid", "Semi Quant.", "Smaller Zero", "< LOD", "< LLOQ", "> ULOQ", "No Intercept", "Missing Measurement", "ISTD Out of Range", "STD/QC < Limit", "STD/QC > Limit", "Invalid", "Incomplete" and "Blank Out of Range". For generic data, status values may differ depending on the software used. |
preproc_q500_urine_limits |
Only to be used in the special case of using the Biocrates MxP Quant 500 Kit LC data with urine samples and additional calibration samples (cal 0.25 and cal 9)! As MetIDQ doesn't consider the additional calibration samples when calculating limits of detection ("< LLOQ", "> ULOQ"), status values for calibrated compounds will be corrected based on cal 0.25 and cal 9 concentrations (default = FALSE). |
filter_compound_qc_ref_max_mv_ratio |
Set maximum ratio of missing values allowed for compounds in reference QC samples (Biocrates' QC Level 2, Reference QC in generic data) (default = 0.3, exclusive, disable with NULL). |
filter_compound_qc_ref_max_rsd |
Set maximum %RSD allowed for compounds in reference QC samples (Biocrates' QC Level 2, Reference QC in generic data) (default = 15%, exclusive, disable with NULL). |
filter_compound_qc_pool_max_mv_ratio |
Set maximum ratio of missing values allowed for compounds in pooled QC samples (Pooled QC) (default = 0.3, exclusive, disable with NULL). |
filter_compound_qc_pool_max_rsd |
Set maximum %RSD allowed for compounds in pooled QC samples (Pooled QC) (default = 15%, exclusive, disable with NULL). |
filter_compound_bs_max_mv_ratio |
Set maximum ratio of missing values allowed for compounds in biological samples (Sample) (default = 0.3, exclusive, disable with NULL). |
filter_compound_bs_min_rsd |
Set minimum %RSD allowed for compounds in biological samples (Sample) (default = 15%, exclusive, disable with NULL). |
filter_sample_max_mv_ratio |
Set maximum ratio of missing values allowed per biological sample (Sample) (default < 0.2, exclusive, disable with NULL). |
data_tables |
Control data tables availability in reports. "all" (default) will show all implemented data tables (with csv export buttons). "stats" will only show tables of summarized data (such as countings, pre-processed). "none" will show no data tables at all, i.e. the report is mainly limited to visualizations. |
data_export_long, data_export_wide |
Enable export of measurement data sets (default = FALSE). Use in combination with data_tables = "stats", to keep big data sets exclusively outside of the report (e.g. to prevent memory issues either during pandoc conversion or when viewing the report in a browser). |
data_export_prefix |
File path prefix to apply for data exports. (default = report_output_dir + report_output_name). |
metadata_import |
Indicate a text file (csv or tsv) with additional metadata/annotations to import and merge (by column "Sample Identification"). |
metadata_import_overlap |
Specify the handling of overlaping columns. "rename" (default) will extend duplicate column names in the metadata import with ".D" to make them unique, where D is an increasing number according to the occurance of the same name. Keep this in mind when indicating variables. "replace" will replace original columns with new metadata columns. "omit" will keep original columns and ignore new metadata columns. |
metadata_name_mods_org |
Rename columns in the original data. This is applied **before** the import the of additional metadata, if any. Consider that non-unique column names are modified in general. Names not in the data are ignored. Use a named vector to indicate columns to rename, e.g.: c(oldname1 = "newname1", oldname2 = "newname2", ...) |
metadata_name_mods_add |
Rename columns in original and added metadata. This is applied **after** the import of additional metadata, if any. Consider that non-unique column names are modified in general. Names not in the data are ignored. Use a named vector to indicate columns to rename, e.g.: c(oldname1 = "newname1", oldname2 = "newname2", ...) |
metadata_value_mods |
Batch change values in the data, e.g. to correct sample identifiers, groups, etc. This is applied after metadata import and renaming, if any. Indicated columns and values not in the data are ignored. Use a named list, with names indicating the columns and named vectors indicate the changes to apply, e.g.: list("columnX" = c("oldvalueA" = "newvalueA", "oldvalueB" = "newvalueB"), |
lowcon_conditions |
Indicate a vector of study variables of interest which will be applied to additional reproducibility analysis designed for low concentration data below the limit of quantification. Hence, this analysis is performed on completely unfiltered data using area (LC) or intensity (FIA), resp. |
lowcon_sd_outlier_removal |
If set to TRUE, data of sample groups with same conditions (i.e. combination of indicated study variables) is removed if SD is higher than 1.5 or not available (e.g. when group consists of only one sample). This is ment to reject unreliable technical replicates and not recommended to apply on actual study samples and thus biological variance. |
lowcon_minimum_intensity |
Minimal intensity threshold to keep values in low concentration analysis (default = 20000). Below, values are set to NA. |
... |
Masked parameters for development and testing only. |
Value
Returns TRUE to indicate if a report was successfully created. Note, however, errors may still occur withing the report, sometimes more disruptive (e.g. failed imports), sometimes less disruptive (e.g. a single plot failing).
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 | # Note: These examples make use of data sets included in the package,
# hence the use of the system.file function.
# Normally, please provide file paths strings directly instead,
# e.g. data_files = list(Batch1 = c("filepath")).
# Biocrates MxP Quant 500 Kit - LC injection
metaquac::create_qc_report(
data_files = list(
Batch1 = c(
system.file("extdata", "biocrates_q500_test_01/Batch1_LC.txt", package = "metaquac"))),
kit = "Biocrates MxP Quant 500 Kit",
measurement_type = "LC",
title = "Biocrates QC - Q500 - LC",
report_output_name = "biocrates_qc_q500_lc",
report_output_dir = "biocrates_q500_test",
pool_indicator = "Sex",
profiling_variables = c('Sex'),
study_variables = list('Sex'),
replicate_variables = c('Sex')
)
# Biocrates MxP Quant 500 Kit - FIA injection
metaquac::create_qc_report(
data_files = list(
Batch1 = c(
system.file("extdata", "biocrates_q500_test_01/Batch1_FIA.txt", package = "metaquac"))),
kit = "Biocrates MxP Quant 500 Kit",
measurement_type = "FIA",
title = "Biocrates QC - Q500 - FIA",
report_output_name = "biocrates_qc_q500_fia",
report_output_dir = "biocrates_q500_test",
pool_indicator = "Sex",
profiling_variables = c('Sex'),
study_variables = list('Sex'),
replicate_variables = c('Sex')
)
# Biocrates AbsoluteIDQ p400 HR Kit - LC injection
metaquac::create_qc_report(
data_files = list(
Batch1 = c(
system.file("extdata", "biocrates_p400_test_01/Batch1_LC1.txt", package = "metaquac"),
system.file("extdata", "biocrates_p400_test_01/Batch1_LC2.txt", package = "metaquac")),
Batch2 = c(
system.file("extdata", "biocrates_p400_test_01/Batch2_LC1.txt", package = "metaquac"),
system.file("extdata", "biocrates_p400_test_01/Batch2_LC2.txt", package = "metaquac")),
Batch3 = c(
system.file("extdata", "biocrates_p400_test_01/Batch3_LC1.txt", package = "metaquac"),
system.file("extdata", "biocrates_p400_test_01/Batch3_LC2.txt", package = "metaquac")),
Batch4 = c(
system.file("extdata", "biocrates_p400_test_01/Batch4_LC1.txt", package = "metaquac"),
system.file("extdata", "biocrates_p400_test_01/Batch4_LC2.txt", package = "metaquac"))),
kit = "Biocrates AbsoluteIDQ p400 HR Kit",
measurement_type = "LC",
title = "Biocrates QC - p400 - LC",
report_output_name = "biocrates_qc_p400_lc",
report_output_dir = "biocrates_p400_test",
pool_indicator = "Sample.Identification",
profiling_variables = c("Group", "Condition"),
study_variables = list("Group", "Condition", "Group" = list("Condition")),
replicate_variables = c("Group", "Condition")
)
# Biocrates AbsoluteIDQ p400 HR Kit - FIA injection
metaquac::create_qc_report(
data_files = list(
Batch1 = c(
system.file("extdata", "biocrates_p400_test_01/Batch1_FIA1.txt", package = "metaquac"),
system.file("extdata", "biocrates_p400_test_01/Batch1_FIA2.txt", package = "metaquac")),
Batch2 = c(
system.file("extdata", "biocrates_p400_test_01/Batch2_FIA1.txt", package = "metaquac"),
system.file("extdata", "biocrates_p400_test_01/Batch2_FIA2.txt", package = "metaquac")),
Batch3 = c(
system.file("extdata", "biocrates_p400_test_01/Batch3_FIA1.txt", package = "metaquac"),
system.file("extdata", "biocrates_p400_test_01/Batch3_FIA2.txt", package = "metaquac")),
Batch4 = c(
system.file("extdata", "biocrates_p400_test_01/Batch4_FIA1.txt", package = "metaquac"),
system.file("extdata", "biocrates_p400_test_01/Batch4_FIA2.txt", package = "metaquac"))),
kit = "Biocrates AbsoluteIDQ p400 HR Kit",
measurement_type = "FIA",
title = "Biocrates QC - p400 - FIA",
report_output_name = "biocrates_qc_p400_fia",
report_output_dir = "biocrates_p400_test",
pool_indicator = "Sample.Identification",
profiling_variables = c("Group", "Condition"),
study_variables = list("Group", "Condition", "Group" = list("Condition")),
replicate_variables = c("Group", "Condition")
)
|
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