lwaldron

suppressPackageStartupMessages({
  library(lefser)
  library(curatedMetagenomicData)
})
zeller <-
  curatedMetagenomicData("ZellerG_2014.pathway_abundance",
                         counts = TRUE,
                         dryrun = FALSE)[[1]]
zeller <- zeller[, zeller$study_condition != "adenoma"]
zeller <- relativeAb(zeller)

lwaldron

metaphlanToPhyloseq <- function(
  metaphlandir,
  metadat=NULL,
  simplify=TRUE){
  ## tax is a matrix or data.frame with the table of taxonomic abundances, rows are taxa, columns are samples
  ## metadat is an optional data.frame of specimen metadata, rows are samples, columns are variables
  ## if simplify=TRUE, use only the most detailed level of taxa names in the final object
  ## metaphlanToPhyloseq("~/Downloads/metaphlan_bugs_list")
  .getMetaphlanTree <- function(removeGCF=TRUE, simplify=TRUE){
    if (!requireNamespace("ape")) {

lwaldron

suppressPackageStartupMessages({
  library(curatedMetagenomicData)
  library(mia)
  library(dplyr)
  library(purrr)
})

datasets <- sampleMetadata |>
  group_by(study_name) |>
  count() |>

lwaldron

Before using this script you need to create an OpenAI API key (https://platform.openai.com/api-keys)
and put it in ~/.Renviron:

OPENAI_API_KEY='my_key_here'

# libraries used
library(openai)
library(dplyr)
library(stringr)

lwaldron

library(bugsigdbr)
bsdb <- importBugSigDB(version = "devel")

# Create a stacked barplot of the proportion of Pielou, Shannon, Chao1, Simpson, Inverse Simpson, and Richness as a function of year
library(tidyverse)
bsdb_by_year <- bsdb |>
  filter(Year > 2014) |>
  dplyr::group_by(Year) |>
  dplyr::summarize(
    Pielou = sum(!is.na(Pielou)) / n(),

lwaldron

---
title: "Stepwise treadmill test"
author: "Levi Waldron"
date: "`r Sys.Date()`"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

lwaldron

% \VignetteIndexEntry{Using Affymetrix Probe Level Data}
% \VignetteDepends{hgu95av2.db, rae230a.db, rae230aprobe, Biostrings}
% \VignetteKeywords{Annotation}
%\VignettePackage{annotate}

\documentclass{article}

\newcommand{\Rfunction}[1]{{\texttt{#1}}}
\newcommand{\Rmethod}[1]{{\texttt{#1}}}

lwaldron

suppressPackageStartupMessages(library(curatedMetagenomicData))
zeller <- curatedMetagenomicData::curatedMetagenomicData("Zeller.+relative_abundance", counts = FALSE, dryrun = FALSE)[[1]]
zellercounts <- curatedMetagenomicData::curatedMetagenomicData("Zeller.+relative_abundance", counts = TRUE, dryrun = FALSE)[[1]]

zeller <- zeller[, zeller$study_condition != "adenoma"]
zellercounts <- zellercounts[, zellercounts$study_condition != "adenoma"]

suppressPackageStartupMessages(library(lefser))
res_group <- lefser(zeller, groupCol = "study_condition")
res_group_counts <- lefser(zellercounts, groupCol = "study_condition")

lwaldron

suppressPackageStartupMessages(library(curatedMetagenomicData))

sampleMetadata[sampleMetadata$study_name == "FengQ_2015", ] |>
                     returnSamples("relative_abundance", rownames = "NCBI")
    
allstudies <- unique(sampleMetadata$study_name)
allres <- sapply(allstudies, function(study) {
    message(study)
    try(
        suppressMessages(sampleMetadata[sampleMetadata$study_name == study,] |>

lwaldron

---
title: "NYC CHS import"
author: "Levi Waldron"
date: "`r Sys.Date()`"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, message = FALSE)
```