sneumann

library(multtest)


## A convenience function cut&paste from
## https://github.com/sneumann/xcms/blob/master/R/xcmsSet.R#L2052
pval <- function(X, classlabel, teststat) {

    n1 <- rowSums(!is.na(X[,classlabel == 0]))
    n2 <- rowSums(!is.na(X[,classlabel == 1]))
    A <- apply(X[,classlabel == 0], 1, sd, na.rm=TRUE)^2/n1 ## sd(t(X[,classlabel == 0]), na.rm = TRUE)^2/n1

sneumann

## TODO:
## study-variable , separated -> DONE
## assay[1]-assay[2]-assay[3]-assay[4]-assay[5]-assay[6]

## link sample - assay:
##
## MTD	sample[1]-description	KO15
## MTD	assay[1]-sample_ref	sample[1]

library(plyr) ## for rbind.fill

sneumann

   - id: chemont
     preferredPrefix: ChemOnt
     title: ChemOnt: A semantic-based ontology for chemical and biological data integration.
     uri: http://classyfire.wishartlab.com/
     description: "ChemOnt is comphrehensive, computable, and manually curated. 
It covers >4000 chemical classes of organic and inorganic compounds. The set of classes was created 
and extended by extracting common terms from the scientific literature (e.g.: IUPAC) and available databases. 
The flexibility The taxonomy is currently mapped to the ChEBI and LIPID MAPS ontologies in collaboration 
with the respective teams. Moreover, ChemOnt has been partially mappped to the MeSH thesaurus. 
The mapping effort in this regard is ongoing. Thanks to the flexibility and simplicity of its structure 

sneumann

library(httr)
library(rmzTabM)
library(xcms)
library(faahKO)
data(faahko3)

faahkoMzTab <- rmzTabM::MzTab$new(
  metadata=Metadata$new(
    'mzTab-version' = "2.2.0-M",
    'mzTab-ID' = "faahKO",

sneumann

import pykube
from os import environ as os_environ

# pykube imports:
try:
    from pykube.config import KubeConfig
    from pykube.http import HTTPClient
    from pykube.objects import (
        Job,
        Pod

sneumann

install.packages("microbenchmark")
install.packages("plyr")

install.packages("rcdk")
install.packages("OrgMassSpecR")
install.packages("CHNOSZ")

BiocManager::install("Rdisop")
BiocManager::install("MetaboCoreUtils")

sneumann

#!/bin/bash
wget -O- https://www.chemotion-repository.net/api/v1/public/molecule.json?id=6338 |\
jq "$(cat <<EOF
{ "@context": "https://schema.org/", 
  "@type": "MolecularEntity", 
  "dct:conformsTo": { "http://purl.org/dc/terms/conformsTo": {
    "@id": "https://bioschemas.org/profiles/MolecularEntity/0.5-RELEASE", 
    "@type": "CreativeWork" }
  }, 
  "@id": .molecule.tag.taggable_data.chemotion.doi, 

sneumann

library(rmzTabM)       ## https://lifs-tools.github.io/rmzTab-m/ 
library(metabolighteR) ## https://aberhrml.github.io/metabolighteR/

convertMzTab2MAF <- function(mzTabfile, MAFfile) {

  mzTabTable <- readMzTab(mzTabfile)

  ## To turn these tables into objects, use the R6 class constructor method `new()`:
  mzTabObject <- MzTab$new()$fromDataFrame(mzTabTable)
  metadata <- mzTabObject$metadata

sneumann

## 1) Get paginated list of Molecules from Chemotion
## 2) extract DOI and internal chemotion ID for each molecule with `jq`
## 3) Query each molecule and convert with `jq` to (bio)schemas
## 4) Embedd the JSON in a CDATA in a minimalistic XML
## 5) Pipe the XML to the FIZ-OAI backend running at $BACKEND, e.g. localhost

export BACKEND=http://localhost:8081/oai-backend

for I in 1 2 3 4 5 6 7 8 9 10; do
wget -O- "https://www.chemotion-repository.net/api/v1/public/molecules.json?page=$I&per_page=100" |\

sneumann

#!/usr/bin/env bash

export BACKEND=http://localhost:8081/oai-backend

jq -c '.[]' DataDump-IPB.jsonld | \
while read i; do

IDENTIFIER=`echo "$i" | jq '.identifier'`

(