Retrieve data from a DuckDB view and convert to Summarized Experiment

'loadParquetData' accesses a DuckDB view created by 'accessParquetData' and loads it into R as a Summarized Experiment object. Arguments can be provided to filter or transform the DuckDB view. To filter, provide a named list (format: name = column name, value = single exact value or vector of exact values) to filter_values. To further transform the view, provide a saved sequence of function calls starting with dplyr::tbl to custom_view.

Usage

loadParquetData(
  con,
  data_type,
  filter_values = NULL,
  custom_view = NULL,
  include_empty_samples = FALSE,
  dry_run = FALSE
)

Arguments

con: DuckDB connection object of class 'duckdb_connection'
data_type: Single string: value found in the data_type' column of output_file_types() and also as part of the name of a view found in DBI::dbListTables(con), indicating which views to consider when collecting data.
filter_values: Named list: element name equals the column name to be filtered and element value equals a vector of exact column values. Default: NULL
custom_view: Saved object with the initial class 'tbl_duckdb_connection' (optional): DuckDB tables/views can be accessed with with the 'dplyr::tbl' function, and piped into additional functions such as 'dplyr::filter' prior to loading into memory with 'dplyr::collect'. A particular sequence of function calls can be saved and provided to this function for collection and formatting as a Summarized Experiment. See the function example. Default: NULL
include_empty_samples: Boolean (optional): should samples provided via a 'uuid' argument within 'filter_values' be included in the final TreeSummarizedExperiment if they do not show up in the results from filtering the source parquet data file. Default: FALSE
dry_run: Boolean (optional): if TRUE, the function will return the tbl_duckdb_connection object prior to calling 'dplyr::collect'. Default: FALSE

Value

A TreeSummarizedExperiment object with process metadata, row data, column names, and relevant assays. If dry_run = TRUE, a tbl_duckdb_connection object.

Details

If 'custom_view' is provided, it must use one of the views indicated by data_type'.

Examples

# \donttest{
 con <- accessParquetData(repo = "waldronlab/metagenomics_mac_examples",
                          data_types = "pathcoverage_unstratified")

 custom_filter <- dplyr::tbl(con, "pathcoverage_unstratified_pathway") |>
                  dplyr::filter(grepl("UMP biosynthesis", pathway))

 uuids <- c("8793b1dc-3ba1-4591-82b8-4297adcfa1d7",
            "cc1f30a0-45d9-41b1-b592-7d0892919ee7",
            "fb7e8210-002a-4554-b265-873c4003e25f",
            "d9cc81ea-c39e-46a6-a6f9-eb5584b87706",
            "4985aa08-6138-4146-8ae3-952716575395",
            "8eb9f7ae-88c2-44e5-967e-fe7f6090c7af")

 custom_tse <- loadParquetData(con,
                               data_type = "pathcoverage_unstratified",
                               filter_values = list(uuid = uuids),
                               custom_view = custom_filter)
 custom_tse
#> class: TreeSummarizedExperiment 
#> dim: 3 6 
#> metadata(0):
#> assays(1): coverage
#> rownames(3): PWY-5686: UMP biosynthesis I PWY-7790: UMP biosynthesis II
#>   PWY-7791: UMP biosynthesis III
#> rowData names(1): pathway
#> colnames(6): 8793b1dc-3ba1-4591-82b8-4297adcfa1d7
#>   cc1f30a0-45d9-41b1-b592-7d0892919ee7 ...
#>   4985aa08-6138-4146-8ae3-952716575395
#>   8eb9f7ae-88c2-44e5-967e-fe7f6090c7af
#> colData names(305): uuid humann_header ...
#>   WallenZD_2022_uncurated_Day_of_stool_collection_digestion_issue
#>   WallenZD_2022_uncurated_Day_of_stool_collection_constipation
#> reducedDimNames(0):
#> mainExpName: NULL
#> altExpNames(0):
#> rowLinks: NULL
#> rowTree: NULL
#> colLinks: NULL
#> colTree: NULL
# }

fpaths <- c(file.path(system.file("extdata",
                                  package = "parkinsonsMetagenomicData"),
                      "pathcoverage_unstratified_uuid.parquet"),
            file.path(system.file("extdata",
                                  package = "parkinsonsMetagenomicData"),
                      "pathcoverage_unstratified_pathway.parquet"))

con <- accessParquetData(local_files = fpaths,
                         data_types = "pathcoverage_unstratified")

custom_filter <- dplyr::tbl(con, "pathcoverage_unstratified_pathway") |>
                 dplyr::filter(grepl("UMP biosynthesis", pathway))

uuids <- c("8793b1dc-3ba1-4591-82b8-4297adcfa1d7",
           "cc1f30a0-45d9-41b1-b592-7d0892919ee7",
           "fb7e8210-002a-4554-b265-873c4003e25f",
           "d9cc81ea-c39e-46a6-a6f9-eb5584b87706",
           "4985aa08-6138-4146-8ae3-952716575395",
           "8eb9f7ae-88c2-44e5-967e-fe7f6090c7af")

custom_tse <- loadParquetData(con,
                              data_type = "pathcoverage_unstratified",
                              filter_values = list(uuid = uuids),
                              custom_view = custom_filter)
custom_tse
#> class: TreeSummarizedExperiment 
#> dim: 3 6 
#> metadata(0):
#> assays(1): coverage
#> rownames(3): PWY-5686: UMP biosynthesis I PWY-7790: UMP biosynthesis II
#>   PWY-7791: UMP biosynthesis III
#> rowData names(1): pathway
#> colnames(6): 8793b1dc-3ba1-4591-82b8-4297adcfa1d7
#>   cc1f30a0-45d9-41b1-b592-7d0892919ee7 ...
#>   4985aa08-6138-4146-8ae3-952716575395
#>   8eb9f7ae-88c2-44e5-967e-fe7f6090c7af
#> colData names(305): uuid humann_header ...
#>   WallenZD_2022_uncurated_Day_of_stool_collection_digestion_issue
#>   WallenZD_2022_uncurated_Day_of_stool_collection_constipation
#> reducedDimNames(0):
#> mainExpName: NULL
#> altExpNames(0):
#> rowLinks: NULL
#> rowTree: NULL
#> colLinks: NULL
#> colTree: NULL

Usage

Arguments

Value

Details

See also

Examples