pmartR (version 2.4.6)

combine_lipidData: Combines two omicsData objects with identical sample information.

Description

Combines two omicsData objects with identical sample information.

Usage

combine_lipidData(
  obj_1,
  obj_2,
  retain_groups = FALSE,
  retain_filters = FALSE,
  drop_duplicate_emeta = TRUE,
  ...
)

Value

An object of the same type as the two input objects, with their combined data.

Arguments

obj_1

omicsData object of the same supported type as obj_2, currently "lipidData". See details for more requirements.

obj_2

omicsData object of the same supported type as obj_1, currently "lipidData". See details for more requirements.

retain_groups

logical indicator of whether to attempt to apply existing group information to the new object. Defaults to FALSE.

retain_filters

Whether to retain filter information in the new object (defaults to FALSE).

drop_duplicate_emeta

a logical indicator of whether duplicate molecule identifiers in e_meta should be dropped

...

Extra arguments, not one of 'omicsData', 'main_effects', or 'covariates' to be passed to `pmartR::group_designation`.

Details

General requirements:

* sample names: These must be identical for both objects (column names of e_data, and sample identifiers in f_data) * data attributes: Objects must be on the same scale and both be either normalized or unnormalized * group designation: Objects must have the same grouping structure if retain_groups = T

Examples

if (FALSE) { # requireNamespace("pmartRdata", quietly = TRUE)
library(pmartRdata)

obj_1 <- lipid_neg_object
obj_2 <- lipid_pos_object

# de-duplicate any duplicate edata identifiers
all(obj_2$e_data[, get_edata_cname(obj_2)] == obj_2$e_meta[, get_edata_cname(obj_2)])
obj_2$e_data[, get_edata_cname(obj_2)] <- paste0("obj_2_", obj_2$e_data[, get_edata_cname(obj_2)])
obj_2$e_meta[, get_edata_cname(obj_2)] <- obj_2$e_data[, get_edata_cname(obj_2)]

combine_object <- combine_lipidData(obj_1 = obj_1, obj_2 = obj_2)

# preprocess and group the data and keep filters/grouping structure

obj_1 <- edata_transform(omicsData = obj_1, data_scale = "log2")
obj_1 <- normalize_global(omicsData = obj_1, subset_fn = "all",
                          norm_fn = "median", apply_norm = TRUE)
obj_2 <- edata_transform(omicsData = obj_2, data_scale = "log2")
obj_2 <- normalize_global(omicsData = obj_2, subset_fn = "all",
                          norm_fn = "median", apply_norm = TRUE)

obj_1 <- group_designation(omicsData = obj_1, main_effects = "Virus")
obj_2 <- group_designation(omicsData = obj_2, main_effects = "Virus")

obj_1 <- applyFilt(filter_object = molecule_filter(omicsData = obj_1),
                   omicsData = obj_1, min_num = 2)
obj_2 <- applyFilt(filter_object = cv_filter(omicsData = obj_2), obj_2, cv_thresh = 60)

combine_object_later <- combine_lipidData(
                          obj_1 = obj_1,
                          obj_2 = obj_2,
                          retain_groups = TRUE,
                          retain_filters = TRUE
                        )
}