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Merge pull request #33 from biosustain/add_PXD070899
Add pxd070899
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‎.devcontainer/README.md‎

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The devcontainer uses the
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[`nextflow-io/training` container](https://github.com/nextflow-io/training/pkgs/container/training)
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from GitHub Container Registry, as provided by the nextflow team. I copied the
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from GitHub Container Registry, as provided by the nextflow team. The
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[Dockerfile](https://github.com/biosustain/dsp_course_proteomics_intro/blob/HEAD/.devcontainer/Dockerfile)
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which was used to build the container image `2.1.7` for reference of what the container contains.
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extends this image with some additional software, e.g. for the downstream data analysis, and is built
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and published to the GitHub Container Registry using an action.
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The Dockerimage is published based on a devcontainer configuration file in the nextflow training repo,
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[`.devcontainer/codespaces-dev/devcontainer.json`](https://github.com/nextflow-io/training/blob/master/.devcontainer/codespaces-dev/devcontainer.json)
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and then published to the GitHub Container Registry using an action.
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The devcontainer then uses this image, see,
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[`.devcontainer/codespaces-dev/devcontainer.json`](https://github.com/nextflow-io/training/blob/master/.devcontainer/codespaces-dev/devcontainer.json).
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## Additional software
2020

‎3_data_analysis_PXD070899.ipynb‎

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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "823dca58",
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"metadata": {},
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"source": [
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"# Exercise: Data Analysis PXD070899\n",
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"\n",
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"Plan\n",
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"- read data and log2 transform intensity values\n",
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"- aggregate peptide intensities to protein intensities\n",
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"- format data from long to wide format\n",
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"- remove contaminant proteins\n",
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"- check for missing values\n",
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"- Clustermap of sample and proteins\n",
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"- differential analysis (Volcano Plots)\n",
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"- Enrichment Analysis\n",
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"- check for maltose update pathway (Fig. 3 in paper)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "3206180f",
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"metadata": {
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"tags": [
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"hide-output"
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]
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},
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"outputs": [],
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"source": [
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"%pip install acore vuecore \"pingouin<0.6.0\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "9aad124f",
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"metadata": {},
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"outputs": [],
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"source": [
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"from pathlib import Path\n",
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"\n",
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"import acore.differential_regulation\n",
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"import acore.enrichment_analysis\n",
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"import acore.normalization\n",
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"import matplotlib.pyplot as plt\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"import plotly.express as px\n",
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"import scipy.stats\n",
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"import seaborn as sns\n",
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"import vuecore\n",
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"from acore.io.uniprot import fetch_annotations, process_annotations\n",
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"from vuecore.viz import get_enrichment_plots"
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]
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},
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{
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"cell_type": "markdown",
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"id": "cabfd7c2",
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"metadata": {},
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"source": [
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"# Paramters\n",
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"- `file_in`: input file with the quantified peptide data in MSstats format\n",
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" as provided by quantms\n",
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"- `out_dir`: output directory for the results of the data analysis, \n",
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" which will be used later for the report generation with VueGen.\n",
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"\n",
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"The file will be loaded from the online repository if it is not present."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "b503b660",
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"metadata": {
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"tags": [
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"parameters"
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]
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},
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"outputs": [],
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"source": [
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"file_in: str = Path(\n",
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" \"data/PXD070899/processed/report.pg_matrix.tsv\"\n",
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") # input file with the quantified peptide data in MSstats format as provided by quantms\n",
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"out_dir = \"data/PXD070899/report/\" # output directory for the results of the data analysis, which will be used later for the report generation with VueGen.\n",
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"min_obs_per_group:int = 3 # minimum number of observations per group for a protein to be included in the differential regulation analysis"
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]
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},
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{
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"cell_type": "markdown",
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"id": "fa6869d2",
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"metadata": {},
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"source": [
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"Create output directory if it does not exist"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "dbe29342",
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"metadata": {},
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"outputs": [],
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"source": [
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"out_dir = Path(out_dir)\n",
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"out_dir.mkdir(parents=True, exist_ok=True)\n",
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"print(f\"Output directory: {out_dir}\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "6f757866",
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"metadata": {},
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"source": [
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"We have the following columns in the data:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "06f54aee",
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"metadata": {},
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"outputs": [],
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"source": [
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"if not file_in.exists():\n",
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" file_in = (\n",
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" \"https://raw.githubusercontent.com/biosustain/dsp_course_proteomics_intro/HEAD\"\n",
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" \"/data/PXD070899/processed/report.pg_matrix.tsv\"\n",
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" )\n",
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"df = pd.read_csv(file_in, sep=\"\\t\", header=0, index_col=0) # .set_index([])\n",
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"df.head()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "c836978a",
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"metadata": {},
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"source": [
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"Potentiall clean filepath names in DIANN output"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "a83ac030",
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"metadata": {},
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"outputs": [],
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"source": [
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"df.columns = df.columns.str.split(r\"/|\\\\\").str[-1]\n",
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"df.head()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "f46b47ea",
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"metadata": {},
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"source": [
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"The first 6 columns contain the meta information about the peptides, \n",
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"while the remaining columns contain the intensities."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "9fd0ea35",
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"metadata": {},
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"outputs": [],
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"source": [
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"proteins_meta = df.iloc[:,:5]\n",
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"proteins_meta "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "038374d1",
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"metadata": {},
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"outputs": [],
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"source": [
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"proteins = df.iloc[:,5:].T\n",
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"proteins.index.name = \"SampleID\"\n",
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"proteins.columns.name = \"ProteinName\"\n",
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"proteins.head()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "d4a33fd1",
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"metadata": {},
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"source": [
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"Log2 transform the intensity values and remove contaminant proteins"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "23a805ed",
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"metadata": {},
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"outputs": [],
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"source": [
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"to_drop = proteins.filter(regex=\"cRAP-|CON_\", axis=1).columns\n",
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"to_drop"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "b5190a90",
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"metadata": {},
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"outputs": [],
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"source": [
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"proteins = np.log2(proteins).drop(to_drop, axis=1)\n",
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"proteins"
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]
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},
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{
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"cell_type": "markdown",
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"id": "3477e9a4",
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"metadata": {
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"lines_to_next_cell": 2
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},
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"source": [
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"Add label encoding"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "99b01cb0",
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"metadata": {
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"lines_to_next_cell": 0
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},
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"outputs": [],
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"source": [
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"# label_encoding = {\"WT\": 0, \"AYa2022\": 1, \"AYa18\": 2, \"AY\": 3}\n",
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"label_suf = pd.Series(\n",
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" proteins.index.str.split(\"_\").str[-2],\n",
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" index=proteins.index,\n",
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" name=\"condition\",\n",
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")\n",
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"label_suf"
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]
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},
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{
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"cell_type": "markdown",
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"id": "750467e3",
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"metadata": {},
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"source": [
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"# Homework\n",
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"Repeat the analysis based on the tutorial from the course.\n",
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"\n",
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"> Small adjustments are needed.\n",
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"\n",
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"- copy bit by bit"
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]
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}
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],
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"metadata": {
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"jupytext": {
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"cell_metadata_filter": "tags,-all",
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"formats": "ipynb,py:percent"
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},
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"kernelspec": {
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"display_name": "base",
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"language": "python",
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"name": "python3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

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