eclarke

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eclarke

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eclarke

# This is the testing function that builds the models
  testMixedEffects <- function(test.col) {
    # We return this default in case of error/no fit
    default <- list(null.model=NA, model=NA)
    
    counts <- .mat[, colnames(.mat)==test.col]
    if (sum(counts > 0)/length(counts) < sparsity) {
      message(sprintf("%s: too sparse, skipping", test.col))
      return(default) 
    }

eclarke

# Using indicspecies with a melted data frame

# Their input is actually not hard to work with. First we need to re-create the count matrix.
# This creates a data frame with the sample ID and study group as the first two columns, then each column after that is an OTU name
# (Replace column names as appropriate)
mat <- melted.df %>% reshape2::dcast(SampleID + StudyGroup ~ otu)

# Next, we actually convert things into inputs
# Hadley's stuff hates rownames, so we have to remake them
rownames(mat) = mat$SampleID

eclarke

docs:
	Rscript -e "devtools::document(roclets=c('rd', 'collate', 'namespace', 'vignette'))"

gh-pages:
	git checkout gh-pages
	git merge master -X theirs -m "merge master"

site:docs gh-pages
	Rscript -e "staticdocs::build_site(site_path='.', launch=FALSE)"
	git commit -am 'updated docs'

eclarke

# Indicator value functions -----------------------------------------------

#' Returns the indicator value (Dufrene, 1997) for a given row of species counts
#' along with a vector of class assignments.
#' @param row:     vector of counts (usually a row in a counts matrix)
#' @param class:   which level in the grouping variable to test
#' @param classes: factor describing the grouping of the counts vector
.indval <- function(row, class, classes) {
  idxs <- classes == class
  A.ij <- sum(row[idxs]) / sum(row)

eclarke

alpha_diversity <- function(df, group.col, freq.col) {
  # Returns a variety of diversity indices, including the Gini-Simpson index,
  # the inverse Simpson index, and the Shannon-Weaver index. The SW index is 
  # calculated using the natural logarithm.
  #
  # Arguments:
  # df: a data frame where the rows are species, with a column containing the 
  #   grouping variable and another column containing the proportional 
  #   abundances of each species
  # group: the name of the column defining the grouping variable

eclarke

>denovo1793
GGAGTCTGGGCCGTGTCTCAGTCCCAGTGTGGCCGATCACCCTCTCAGGTCGGCTATGTATCGTCGCCTTGGTGAGCCGTTACCCCACCAACTAGCTAATACAACGCAGGTCCATCTGGTAGTGATGCAATTGCACCTTTTAATTGACTATCATGCAATAGTCAATATTATGCGGTATTAGCTATCGTTTCCAATAGTTATCCCCCGCTACCAGGCAGGTTACCTACGCGTTACTCACCCGTTCGCAACTCATCCAGAGAAGCAAGCTCCTCCTTCAGCGTTCTACTTGCATGTATTAGGCACGCCGCCAGCGTTCGTC
>denovo2518
GGAGTTTGGGCCGTGTCTCAGTCCCAATGTGGCCGATCACCCTCTCAGGTCGGCTATGCATCACGGCCTTGGTGAGCCGTTACCTCACCAACTAGCTAATGCACCGCGGGTCCATCCATCAGCAGAAGCTTGCGCCTCTTTTCCTCTTCAAACCATGCGGTTCGAAGACCTATGCGGTTTTAGCATCCGTTTCCGAATGTTATCCCCCTCTGATGGGCAGGTTACCCACGTGTTACTCACCCGTTCGCCACTAGATTGACCAGTGCAAGCACCGGTCGCTCTCGTTCGACTTGCATGTATTAGGCACGCCGCCAGCGTTCGTC
>denovo271
GGAGTCTGGGCCGTGTCTCAGTCCCAGTGTGGCCGATCACCCTCTCAGGTCGGCTATGTATCGTCGCCTTGGTGAGCCGTTACCCCACCAACTAGCTAATACAACGCAGGTCCATCTGGTAGTGATGCAATTGCACCTTTTAAGCAAATGTCATGCAACATTTACTGTTATGCGGTATTAGCTATCGTTTCCAATAGTTATCCCCCGNTACCAGGCAGGTTACCTACGCGTTACTCACCCGTTCGCAACTCGTCCAGAAGAGCAAGCTCTCCCTTCAGCGTTCTACTTGCATGTATTAGGCACGCCGCCAGCGTTCGTC
>denovo3052
GGAGTCTGGTCCG

eclarke

# -*- coding: utf-8 -*-
"""
Uses a context manager to provide a dictionary as a 'namespace' of sorts,
allowing you to use dot notation to work with the dictionary. Example:

d = {'a': 5, 'b': 10}
with named(d) as n:
  # prints 5
  print n.a
  # reassignment changes both n and d

eclarke

<2014-09-25 Thu>

Reading this (Wang et al. 2010), in particular Supp. report 4. Has to do with bootstrapping population estimates using the different enzymes used to find integration sites.

<2014-09-26 Fri>

Notes on (Wang et al. 2010) :popest:petersen:intsites: