Subset Data Object by Sample IDs in MicrobiomeStat

This function is part of the MicrobiomeStat package. It subsets a multi-omics data object by a specified set of sample IDs. It checks for the presence of various data components in the data object and subsets them if they exist.

Usage

mStat_subset_data(data.obj, samIDs = NULL, condition = NULL)

Arguments

data.obj

A list object in a format specific to MicrobiomeStat, which can include components such as feature.tab (matrix), feature.ann (matrix), meta.dat (data.frame), tree, and feature.agg.list (list). The data.obj can be converted from other formats using several functions from the MicrobiomeStat package, including: 'mStat_convert_DGEList_to_data_obj', 'mStat_convert_DESeqDataSet_to_data_obj', 'mStat_convert_phyloseq_to_data_obj', 'mStat_convert_SummarizedExperiment_to_data_obj', 'mStat_import_qiime2_as_data_obj', 'mStat_import_mothur_as_data_obj', 'mStat_import_dada2_as_data_obj', and 'mStat_import_biom_as_data_obj'. Alternatively, users can construct their own data.obj. Note that not all components of data.obj may be required for all functions in the MicrobiomeStat package. table (`feature.tab`), feature names, a full feature name list, and a feature aggregation list (`feature.agg.list`).

samIDs

A vector of sample IDs to subset by. This can be a logical vector, a numeric vector, or a character vector of sample IDs.

condition

A logical expression string describing rows to keep. This allows subsetting samples based on metadata, similarly to dplyr::filter. Only one of 'samIDs' and 'condition' should be provided.

Value

A list that is the modified version of the input data object. It includes the subsetted metadata, feature table, feature names, full feature name list, and feature aggregation list.

Details

The function first checks if samIDs is logical or numeric, and if so, converts it to a character vector of sample IDs. It then subsets the metadata by the sample IDs. If a feature table exists, it subsets the feature table and the feature names by the sample IDs. If a full feature name list exists, it subsets the full feature name list by the sample IDs. If a feature aggregation list exists, it subsets each feature aggregation table in the list by the sample IDs. The function returns the subsetted data object.

See also

filter, select

Examples

if (FALSE) { # \dontrun{
# Load the required libraries
library(MicrobiomeStat)
# Prepare data for the function
data(peerj32.obj)
peerj32.obj$meta.dat <- peerj32.obj$meta.dat %>%
dplyr::select(all_of("subject")) %>% dplyr::distinct() %>%
dplyr::mutate(cons = runif(dplyr::n(),0,5)) %>%
dplyr::left_join(peerj32.obj$meta.dat %>% rownames_to_column("sample"),by = "subject") %>%
tibble::column_to_rownames("sample")
# Example 1: Subset data for a specific time point
# Subset to include only samples from time point 1
subset_time1 <- mStat_subset_data(data.obj = peerj32.obj, condition = "time == '1'")
# Example 2: Subset data for a specific group
# Subset to include only samples from the 'LGG' group
subset_LGG <- mStat_subset_data(data.obj = peerj32.obj, condition = "group == 'LGG'")
# Example 3: Subset data for a specific sex
# Subset to include only male samples
subset_male <- mStat_subset_data(data.obj = peerj32.obj, condition = "sex == 'male'")
# Example 4: Complex condition subsetting
# Subset based on multiple conditions: male samples from 'Placebo' group at time point 2
complex_condition <- "sex == 'male' & group == 'Placebo' & time == '2'"
subset_complex <- mStat_subset_data(data.obj = peerj32.obj, condition = complex_condition)
# Example 5: Subset data using a combination of sample IDs and condition
# First, get a subset of sample IDs (e.g., first 10 samples)
subset_ids <- rownames(peerj32.obj$meta.dat)[1:10]
# Subset the data object based on these sample IDs
subset_by_ids <- mStat_subset_data(data.obj = peerj32.obj, samIDs = subset_ids)
# Then, further subset the result to include only those samples from the 'Placebo' group
subset_ids_condition <- mStat_subset_data(data.obj = subset_by_ids,
condition = "group == 'Placebo'")
# Example 6: Subset data for female samples in the 'Placebo' group
# Subset to include only female samples from the 'Placebo' group
subset_female_placebo <- mStat_subset_data(data.obj = peerj32.obj,
condition = "sex == 'female' & group == 'Placebo'")
# Example 7: Subset data excluding certain subjects
# Subset to exclude subjects S1 and S2
subset_exclude_subjects <- mStat_subset_data(data.obj = peerj32.obj,
condition = "!subject %in% c('S1', 'S2')")
# Example 8: Subset data for a specific range of the 'cons' variable
# Subset to include samples with 'cons' values greater than 2
subset_cons_gt_2 <- mStat_subset_data(data.obj = peerj32.obj, condition = "cons > 2")
# Example 9: Subset data for samples with even-numbered IDs
# Subset to include samples with even-numbered IDs
even_sample_ids <- rownames(peerj32.obj$meta.dat)[as.integer(gsub('sample-', '',
rownames(peerj32.obj$meta.dat))) %% 2 == 0]
subset_even_samples <- mStat_subset_data(data.obj = peerj32.obj, samIDs = even_sample_ids)
# Example 10: Combine multiple conditions with logical operators
# Subset to include male samples from 'LGG' group at time point 1 with 'cons' less than 3
complex_multiple_conditions <- "sex == 'male' & group == 'LGG' & time == '1' & cons < 3"
subset_complex_multiple <- mStat_subset_data(data.obj = peerj32.obj,
condition = complex_multiple_conditions)
} # }