Sparse implementation of Alpha Diversity Metrics

Computes the alpha diversity based on Shannon index, simpson or invsimpson. Code is adapted from diversity and uses sparseMatrix in triplet format over the dense matrix. The code is much faster and memory efficient, while still being mathematically correct. This function is built into the class omics with method alpha_diversity() and inherited by other omics classes, such as; metagenomics and proteomics.

Usage

diversity(
  x,
  metric = c("shannon", "simpson", "invsimpson"),
  normalize = TRUE,
  base = exp(1)
)

Arguments

x

A matrix, sparseMatrix or Matrix.

metric

A character variable for metric; shannon, simpson or invsimpson.

normalize

A boolean variable for sample normalization by column sums.

base

Input for log to use natural logarithmic scale, log2, log10 or other (default: exp(1)).

Value

A numeric vector with type double.

See also

diversity

Examples

n_row <- 1000
n_col <- 100
density <- 0.2
num_entries <- n_row * n_col
num_nonzero <- round(num_entries * density)

set.seed(123)
positions <- sample(num_entries, num_nonzero, replace=FALSE)
row_idx <- ((positions - 1) %% n_row) + 1
col_idx <- ((positions - 1) %/% n_row) + 1

values <- runif(num_nonzero, min = 0, max = 1)
sparse_mat <- sparseMatrix(
  i = row_idx,
  j = col_idx,
  x = values,
  dims = c(n_row, n_col)
)

# Alpha diversity is computed on column level
## Transpose the sparseMatrix if required with t() from Matrix R package.
result <- OmicFlow::diversity(
  x = sparse_mat,
  metric = "shannon"
)