estimate_bias: Estimate bias from control measurements
In mikemc/metacal: Bias estimation and calibration for metagenomics experiments

Description Usage Arguments Details Value See Also Examples

Estimate bias using the compositional least-squares approach described in McLaren, Willis, and Callahan (2019).

estimate_bias(observed, actual, ...)

## S3 method for class 'matrix'
estimate_bias(observed, actual, margin, boot = FALSE, times = 1000)

## S3 method for class 'otu_table'
estimate_bias(observed, actual, ...)

## S3 method for class 'phyloseq'
estimate_bias(observed, actual, ...)

`observed`	Abundance matrix of observed compositions.
`actual`	Abundance matrix of actual or reference compositions for the same samples and taxa in `observed`.
`...`	Arguments passed to the matrix method.
`margin`	Matrix margin that corresponds to observations (samples); `1` for rows, `2` for columns.
`boot`	Whether to perform bootstrapping.
`times`	Number of bootstrap replicates.

Bias is estimated by applying center() to the compositional error matrix defined by observed/actual, which requires that observed and actual are non-zero for the same sample-taxa pairs. For convenience, this function will automatically set values in observed to 0 whose corresponding entries are 0 in actual, but it is up to you to replace 0 values in observed with a non-zero value (such as a pseudocount).

Requirements for observed and actual: The row and column names (for matrices) or taxa and sample names (for phyloseq objects) must match, but can be in different orders. Any taxa and samples in observed but not in actual will be dropped prior to estimation.

A mc_bias_fit object with coef(), fitted(), residuals(), and summary() methods.

center() calibrate()

# Load data from the cellular mock communities of Brooks et al 2015
dr <- system.file("extdata", package = "metacal")
list.files(dr)
actual <- file.path(dr, "brooks2015-actual.csv") |>
  read.csv(row.names = "Sample") |>
  as("matrix")
observed <- file.path(dr, "brooks2015-observed.csv") |>
  read.csv(row.names = "Sample") |>
  subset(select = - Other) |>
  as("matrix")
sam <- file.path(dr, "brooks2015-sample-data.csv") |> read.csv()

# Estimate bias with bootstrapping for error estimation
mc_fit <- estimate_bias(observed, actual, margin = 1, boot = TRUE)
summary(mc_fit)