outliers_by_pool_fragments: Identify and flag outliers based on pool fragments.

In calabrialab/ISAnalytics: Analyze gene therapy vector insertion sites data identified from genomics next generation sequencing reads for clonal tracking studies

Identify and flag outliers based on pool fragments.

Description

Identify and flag outliers based on expected number of raw reads per pool.

Usage

outliers_by_pool_fragments(
  metadata,
  key = "BARCODE_MUX",
  outlier_p_value_threshold = 0.01,
  normality_test = FALSE,
  normality_p_value_threshold = 0.05,
  transform_log2 = TRUE,
  per_pool_test = TRUE,
  pool_col = "PoolID",
  min_samples_per_pool = 5,
  flag_logic = "AND",
  keep_calc_cols = TRUE,
  report_path = default_report_path()
)

Arguments

metadata	The metadata data frame
key	A character vector of numeric column names
outlier_p_value_threshold	The p value threshold for a read to be considered an outlier
normality_test	Perform normality test? Normality is assessed for each column in the key using Shapiro-Wilk test and if the values do not follow a normal distribution, other calculations are skipped
normality_p_value_threshold	Normality threshold
transform_log2	Perform a log2 trasformation on values prior the actual calculations?
per_pool_test	Perform the test for each pool?
pool_col	A character vector of the names of the columns that uniquely identify a pool
min_samples_per_pool	The minimum number of samples that a pool needs to contain in order to be processed - relevant only if per_pool_test = TRUE
flag_logic	A character vector of logic operators to obtain a global flag formula - only relevant if the key is longer than one. All operators must be chosen between: AND, OR, XOR, NAND, NOR, XNOR
keep_calc_cols	Keep the calculation columns in the output data frame?
report_path	The path where the report file should be saved. Can be a folder, a file or NULL if no report should be produced. Defaults to {user_home}/ISAnalytics_reports.

Details

Modular structure

The outlier filtering functions are structured in a modular fashion. There are 2 kind of functions:

• Outlier tests - Functions that perform some kind of calculation based on inputs and flags metadata

• Outlier filter - A function that takes one or more outlier tests, combines all the flags with a given logic and filters out rows that are flagged as outliers

This function is an outlier test, and calculates for each column in the key

• The zscore of the values

• The tstudent of the values

• The the associated p-value (tdist)

Optionally the test can be performed for each pool and a normality test can be run prior the actual calculations. Samples are flagged if this condition is respected:

• tdist < outlier_p_value_threshold & zscore < 0

If the key contains more than one column an additional flag logic can be specified for combining the results. Example: let's suppose the key contains the names of two columns, X and Y key = c("X", "Y") if we specify the the argument flag_logic = "AND" then the reads will be flagged based on this global condition: (tdist_X < outlier_p_value_threshold & zscore_X < 0) AND (tdist_Y < outlier_p_value_threshold & zscore_Y < 0)

The user can specify one or more logical operators that will be applied in sequence.

Value

A data frame of metadata with the column to_remove

See Also

Other Data cleaning and pre-processing: aggregate_metadata(), aggregate_values_by_key(), compute_near_integrations(), default_meta_agg(), outlier_filter(), purity_filter(), realign_after_collisions(), remove_collisions(), threshold_filter()

Examples

data("association_file", package = "ISAnalytics")
flagged <- outliers_by_pool_fragments(association_file,
    report_path = NULL
)
head(flagged)

calabrialab/ISAnalytics documentation built on Nov. 2, 2023, 8:57 p.m.