lwaldron

1. Security Vulnerabilities:

• SQL injection risks
• Command injection vulnerabilities
• Path traversal issues
• Unsafe deserialization
• Hardcoded credentials or API keys
• Insecure random number generation
• Unsafe use of eval() or parse()
• XSS vulnerabilities in web outputs
• Unsafe file operations

lwaldron

Testing Taxoniq Against BugSigDB Issue #248 Divergences

This directory contains tests to verify Taxoniq's compatibility with the requirements identified in BugSigDB issue #248.

Background

BugSigDB was considering migrating from direct NCBI API calls to Taxoniq for improved performance and reliability. The issue documents known divergences between Taxoniq and the NCBI API, particularly around:

1. Rank name changes (March 2025): NCBI changed superkingdom to domain
2. Bacteria kingdom divergence: Taxoniq uses generic k__Bacteria (2) vs NCBI's newer kingdom ranks

lwaldron

bioc_repos <- BiocManager::repositories()[["BioCsoft"]]
pkgs <- rownames(utils::available.packages(repos = bioc_repos))
#pkgs <- head(pkgs, 100) # for testing

stats <- bioC.logs::bioC_downloads(pkgs, from = "08-2025", to = "09-2025")
names(stats) <- pkgs
stats.df <- Reduce(rbind, stats)
rownames(stats.df) <- pkgs
write.csv(stats.df, file = "bioc-pkg-downloads.csv")

lwaldron

-- Install and load the httpfs extension to read from GCS
install httpfs;
load httpfs;

-- Create a secret for accessing Google Cloud Storage using a JSON key file
CREATE SECRET metagenomics_mac (
    TYPE GCS,
    KEY_FILE '/path/to/your/gcp-credentials.json'
);

lwaldron

# Title: Benchmark Query Performance on Sorted vs. Unsorted Parquet Files
# Description: This script first creates a new Parquet file sorted by the
#              '# Gene Family' column. It then uses the 'microbenchmark'
#              package to compare the query speed for a specific gene
#              family between the original, unsorted file and the new,
#              sorted file.

# --- 1. SETUP: Load necessary libraries ---
# Ensure you have these packages installed:
# install.packages(c("duckdb", "dplyr", "arrow", "microbenchmark"))

lwaldron

---
title: "F. prausnitzii in PD"
author: "Levi Waldron"
date: "`r Sys.Date()`"
output: html_document
---

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

lwaldron

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

```{r, message=FALSE, warning=FALSE, echo=FALSE}
library(parkinsonsMetagenomicData)
library(dplyr)

lwaldron

suppressPackageStartupMessages(library(parkinsonsMetagenomicData))
#> Set default bucket name to 'metagenomics-mac'
packageVersion("parkinsonsMetagenomicData")
#> [1] '0.0.0.9000'
colnames(sampleMetadata) |> sort()
#>   [1] "Adhesive removal (s)"                                           
#>   [2] "age"                                                            
#>   [3] "Age"                                                            
#>   [4] "AGE"                                                            
#>   [5] "age_group"                                                      

lwaldron

library(curatedMetagenomicData)
library(dplyr)
library(table1)

dat <- sampleMetadata |>
  select(study_name, body_site, study_condition, age_category, age, BMI)

# labeling is optional, just to make the table nicer
label(dat$body_site) <- "Body Site"
label(dat$study_condition) <- "Study Condition"

lwaldron

library(nabor)

# suppose you have two vectors of propensity scores
propensity_scores1 <- c(0.1, 0.2, 0.3, 0.4, 0.5) #more controls
propensity_scores2 <- c(0.15, 0.25, 0.35) #fewer cases

# use the knn function from the nabor package to find the index of the closest match in propensity_scores2 for each score in propensity_scores1
matches <- nabor::knn(matrix(propensity_scores2), matrix(propensity_scores1), k = 1)$nn.idx

# print the matches