sample_statistics {ISAnalytics} | R Documentation

## Computes user specified functions on numerical columns and updates the metadata data frame accordingly.

### Description

The function operates on a data frame by grouping the content by the sample key and computing every function specified on every column in the `value_columns` parameter. After that the metadata data frame is updated by including the computed results as columns for the corresponding key. For this reason it's required that both `x` and `metadata` have the same sample key, and it's particularly important if the user is working with previously aggregated data. For example:

    data("integration_matrices", package = "ISAnalytics")
    data("association_file", package = "ISAnalytics")
    aggreg <- aggregate_values_by_key(
     x = integration_matrices,
     association_file = association_file,
     value_cols = c("seqCount", "fragmentEstimate")
    )
    aggreg_meta <- aggregate_metadata(association_file = association_file)

    sample_stats <- sample_statistics(x = aggreg,
    metadata = aggreg_meta,
    value_columns = c("seqCount", "fragmentEstimate"),
    sample_key = c("SubjectID", "CellMarker","Tissue", "TimePoint"))

### Usage

    sample_statistics(
      x,
      metadata,
      sample_key = "CompleteAmplificationID",
      value_columns = "Value",
      functions = default_stats(),
      add_integrations_count = TRUE
    )

### Arguments

`x` | A data frame
`metadata` | The metadata data frame
`sample_key` | Character vector representing the key for identifying a sample
`value_columns` | The name of the columns to be computed, must be numeric or integer
`functions` | A named list of function or purrr-style lambdas
`add_integrations_count` | Add the count of distinct integration sites for each group? Can be computed only if `x` contains the mandatory columns `chr`, `integration_locus`, `strand`

### Value

A list with modified x and metadata data frames

### See Also

Other Analysis functions: `CIS_grubbs()`, `comparison_matrix()`, `compute_abundance()`, `cumulative_count_union()`, `cumulative_is()`, `is_sharing()`, `iss_source()`, `purity_filter()`, `separate_quant_matrices()`, `threshold_filter()`, `top_integrations()`

### Examples

    data("integration_matrices", package = "ISAnalytics")
    data("association_file", package = "ISAnalytics")
    stats <- sample_statistics(
        x = integration_matrices,
        metadata = association_file,
        value_columns = c("seqCount", "fragmentEstimate")
    )
    stats

[Package ISAnalytics version 1.4.3 Index]