predict_true_entropy_from_diversity: predict_true_entropy_from_diversity

In Benjamin-Vincent-Lab/binfotron: Binfotron Bioinformatics Analysis Tools Suite

predict_true_entropy_from_diversity

Description

Uses an elastic net model to predict true Shannon entropy (ie Shannon entropy at an abundance of 100 million). Two models are used to cover different abundance ranges. 2-1024 is one model 1025-65536 is another. Lastly, if the abundance and Shannon entropy are over a threshhold predetermined to produce results within 95

Usage

predict_true_entropy_from_diversity(
  my_abundance,
  my_richness,
  my_d25,
  my_inv_simpson,
  my_chao1,
  my_shannon_entropy,
  my_evenness,
  min_abundance = 2,
  should_screen_counts = TRUE
)

Arguments

my_abundance	Numeric. Sum of counts for a population. For T/BCR this would be total reads for the chain.
my_richness	Numeric. Number of species or clonotypes.
my_d25	Numeric. dXX_index
my_inv_simpson	Numeric. inv_simpson
my_chao1	Numeric. chao1
my_shannon_entropy	Numeric. shannon_entropy
my_evenness	Numeric. evenness
min_abundance	Integer to indicate minimun number of counts needed (ie. sum(my_counts)) to get a valid prediction.
should_screen_counts	Boolean to indicate if the coutns should be screened for valid data. Set to false if the data has already been checked by another function.

Value

Modeled prediction of diversity

See Also

Other diversity: chao1(), dXX_index(), evenness(), get_corrected_entropy_rdata_2_4096_path(), get_corrected_entropy_rdata_4097_65536_path(), get_corrected_entropy_rdata_ab1025_65K_ent1_8_path(), get_corrected_entropy_rdata_ab8_1024_ent1_8_path(), has_sufficient_abundance_for_entropy(), inv_simpson(), predict_true_entropy_from_counts(), screen_counts(), shannon_entropy()

Benjamin-Vincent-Lab/binfotron documentation built on Oct. 1, 2024, 8:33 p.m.