combineSamples: Load and Combine Data From Multiple Samples

In mlizhangx/Network-Analysis-for-Repertoire-Sequencing-: Network Analysis of Immune Repertoire

Load and Combine Data From Multiple Samples

Description

Given multiple data frames stored in separate files, loadDataFromFileList() loads and combines them into a single data frame.

combineSamples() has the same default behavior as loadDataFromFileList(), but possesses additional arguments that allow the data frames to be filtered, subsetted and augmented with sample-level variables before being combined.

Usage

loadDataFromFileList(
  file_list,
  input_type,
  data_symbols = NULL,
  header, sep, read.args
)

combineSamples(
  file_list,
  input_type,
  data_symbols = NULL,
  header, sep, read.args,
  seq_col = NULL,
  min_seq_length = NULL,
  drop_matches = NULL,
  subset_cols = NULL,
  sample_ids = NULL,
  subject_ids = NULL,
  group_ids = NULL,
  verbose = FALSE
)

Arguments

file_list	A character vector of file paths, or a list containing connections and file paths. Each element corresponds to a single file containing the data for a single sample.
input_type	A character string specifying the file format of the sample data files. Options are "rds", "rda", "csv", "csv2", "tsv", "table". See details.
data_symbols	Used when input_type = "rda". Specifies the name of each sample's data frame within its respective Rdata file. Accepts a character vector of the same length as file_list. Alternatively, a single character string can be used if all data frames have the same name.
header	For values of input_type other than "rds" and "rda", this argument can be used to specify a non-default value of the header argument to read.table(), read.csv(), etc.
sep	For values of input_type other than "rds" and "rda", this argument can be used to specify a non-default value of the sep argument to read.table(), read.csv(), etc.
read.args	For values of input_type other than "rds" and "rda", this argument can be used to specify non-default values of optional arguments to read.table(), read.csv(), etc. Accepts a named list of argument values. Values of header and sep in this list take precedence over values specified via the header and sep arguments.
seq_col	If provided, each sample's data will be filtered based on the values of min_seq_length and drop_matches. Passed to filterInputData() for each sample.
min_seq_length	Passed to filterInputData() for each sample.
drop_matches	Passed to filterInputData() for each sample.
subset_cols	Passed to filterInputData() for each sample.
sample_ids	A character or numeric vector of sample IDs, whose length matches that of file_list.
subject_ids	An optional character or numeric vector of subject IDs, whose length matches that of file_list. Used to assign a subject ID to each sample.
group_ids	A character or numeric vector of group IDs whose length matches that of file_list. Used to assign each sample to a group.
verbose	Logical. If TRUE, generates messages about the tasks performed and their progress, as well as relevant properties of intermediate outputs. Messages are sent to stderr().

Details

Each file is assumed to contain the data for a single sample, with observations indexed by row, and with the same columns across samples.

Valid options for input_type (and the corresponding function used to load each file) include:

• "rds": readRDS()

• "rds": readRDS()

• "rda": load()

• "csv": read.csv()

• "csv2": read.csv2()

• "tsv": read.delim()

• "table": read.table()

If input_type = "rda", the data_symbols argument specifies the name of each data frame within its respective file.

When calling combineSamples(), for each of sample_ids, subject_ids and group_ids that is non-null, a corresponding variable will be added to the combined data frame; these variables are named SampleID, SubjectID and GroupID.

Value

A data frame containing the combined data rows from all files.

Author(s)

Brian Neal ([e-mail])

References

Hai Yang, Jason Cham, Brian Neal, Zenghua Fan, Tao He and Li Zhang. (2023). NAIR: Network Analysis of Immune Repertoire. Frontiers in Immunology, vol. 14. doi: 10.3389/fimmu.2023.1181825

Webpage for the NAIR package

Examples

# Generate example data
set.seed(42)
samples <- simulateToyData(sample_size = 5)
sample_1 <- subset(samples, SampleID == "Sample1")
sample_2 <- subset(samples, SampleID == "Sample2")

# RDS format
rdsfiles <- tempfile(c("sample1", "sample2"), fileext = ".rds")
saveRDS(sample_1, rdsfiles[1])
saveRDS(sample_2, rdsfiles[2])

loadDataFromFileList(
  rdsfiles,
  input_type = "rds"
)

# With filtering and subsetting
combineSamples(
  rdsfiles,
  input_type = "rds",
  seq_col = "CloneSeq",
  min_seq_length = 13,
  drop_matches = "GGG",
  subset_cols = "CloneSeq",
  sample_ids = c("id01", "id02"),
  verbose = TRUE
)

# RData, different data frame names
rdafiles <- tempfile(c("sample1", "sample2"), fileext = ".rda")
save(sample_1, file = rdafiles[1])
save(sample_2, file = rdafiles[2])
loadDataFromFileList(
  rdafiles,
  input_type = "rda",
  data_symbols = c("sample_1", "sample_2")
)

# RData, same data frame names
df <- sample_1
save(df, file = rdafiles[1])
df <- sample_2
save(df, file = rdafiles[2])
loadDataFromFileList(
  rdafiles,
  input_type = "rda",
  data_symbols = "df"
)

# comma-separated values with header row; row names in first column
csvfiles <- tempfile(c("sample1", "sample2"), fileext = ".csv")
utils::write.csv(sample_1, csvfiles[1], row.names = TRUE)
utils::write.csv(sample_2, csvfiles[2], row.names = TRUE)
loadDataFromFileList(
  csvfiles,
  input_type = "csv",
  read.args = list(row.names = 1)
)

# semicolon-separated values with decimals as commas;
# header row, row names in first column
utils::write.csv2(sample_1, csvfiles[1], row.names = TRUE)
utils::write.csv2(sample_2, csvfiles[2], row.names = TRUE)
loadDataFromFileList(
  csvfiles,
  input_type = "csv2",
  read.args = list(row.names = 1)
)

# tab-separated values with header row and decimals as commas
tsvfiles <- tempfile(c("sample1", "sample2"), fileext = ".tsv")
utils::write.table(sample_1, tsvfiles[1], sep = "\t", dec = ",")
utils::write.table(sample_2, tsvfiles[2], sep = "\t", dec = ",")
loadDataFromFileList(
  tsvfiles,
  input_type = "tsv",
  header = TRUE,
  read.args = list(dec = ",")
)

# space-separated values with header row and NAs encoded as as "No Value"
txtfiles <- tempfile(c("sample1", "sample2"), fileext = ".txt")
utils::write.table(sample_1, txtfiles[1], na = "No Value")
utils::write.table(sample_2, txtfiles[2], na = "No Value")
loadDataFromFileList(
  txtfiles,
  input_type = "table",
  read.args = list(
    header = TRUE,
    na.strings = "No Value"
  )
)

# custom value separator and row names in first column
utils::write.table(sample_1, txtfiles[1],
                   sep = "@", row.names = TRUE, col.names = FALSE
)
utils::write.table(sample_2, txtfiles[2],
                   sep = "@", row.names = TRUE, col.names = FALSE
)
loadDataFromFileList(
  txtfiles,
  input_type = "table",
  sep = "@",
  read.args = list(
    row.names = 1,
    col.names = c("rownames",
                  "CloneSeq", "CloneFrequency",
                  "CloneCount", "SampleID"
    )
  )
)

# same as previous example
# (value of sep in read.args overrides value in sep argument)
loadDataFromFileList(
  txtfiles,
  input_type = "table",
  sep = "\t",
  read.args = list(
    sep = "@",
    row.names = 1,
    col.names = c("rownames",
                  "CloneSeq", "CloneFrequency",
                  "CloneCount", "SampleID"
    )
  )
)

mlizhangx/Network-Analysis-for-Repertoire-Sequencing- documentation built on April 7, 2024, 12:02 p.m.