diff --git a/KNIME Basics.md b/KNIME Basics.md index 9254097..537dadd 100644 --- a/KNIME Basics.md +++ b/KNIME Basics.md @@ -1,6 +1,6 @@ -###Table of contents +### Table of contents -* [What's the idea?](#whats-the-idea) +* [Introduction](#introduction) * [Basic features](#basic-features) * [1. Open existing workflow](#1-open-existing-workflow) * [2. Change workflow settings](#2-change-workflow-settings) @@ -8,15 +8,15 @@ * [4. Save intermediate results](#4-save-intermediate-results) * [5. Share your workflow](#5-share-your-workflow) -## What's the idea? +## Introduction -If you've already had some experience with other workflow management systems (WMS) such as [Galaxy](galaxyproject.org) or [Taverna](www.taverna.org.uk), it'll be easier for you to get started with KNIME. One of the main advantages of using WMSs instead of regular monolothic applications/scripts is that they enable people without skills in programming to actually create programs. Such programs, or workflows, are essentially sets of different operations, or workflow nodes, that are applied consecutively to the input data. One can think about a workflow execution as a flow of the input data through different units that either modify the data or add some new information to it. The programming itself resembles playing with Lego bricks. In this case, bricks are essentially different algorithms that process your data. They’re similar to bricks indeed as WMSs usually have a graphical interface where nodes are represented as rectangles of different colors and can be arranged by a user. After a workflow is built, it can be shared as a file with other people that can run it on their own datasets. +If you've already had some experience with other workflow management systems (WMS) such as [Galaxy](galaxyproject.org) or [Taverna](www.taverna.org.uk), it'll be easier for you to get started with KNIME. One of the main advantages of using WMSs instead of regular monolothic applications/scripts is that they enable people without skills in programming to actually create programs. Such programs, or workflows, are essentially sets of different operations, or workflow nodes, that are applied consecutively to the input data. One can think about a workflow execution as a flow of the input data through different units that can modify the data or/and add some new information to it. The programming itself resembles playing with Lego bricks. In this case, bricks are different algorithms that process your data. They’re similar to bricks indeed as WMSs usually have a graphical interface where nodes are represented as rectangles of different colors and can be arranged by a user. After a workflow is built, it can be shared as a file with other people that can run it on their own datasets. ## Basic features Below, you can find a tutorial on those KNIME features that you’ll need when using the workflow published at this repository. -###1. Open existing workflow +### 1. Open existing workflow In KNIME window go to `{ File => Import KNIME Workflow… }`. @@ -37,11 +37,11 @@ Now, the workflow should appear in the `KNIME Explorer` section. -###2. Change workflow settings +### 2. Change workflow settings Basically, there’re no settings affecting a whole workflow. Instead, each workflow node has its own set of settings that you can see in a dialog shown upon right-click on the node `=> Configure…`. -###3. Execute workflow +### 3. Execute workflow Once you set input files for your workflow, you can process them. There’re two options for this: execution of all the workflow or execution of a part of the workflow. * To execute the workflow at once, press `Execute all executable nodes` on the main toolbar. @@ -51,11 +51,11 @@ There’s also a possibility of cancel a running workflow. To do this, press `Ca -###4. Save intermediate results +### 4. Save intermediate results You can save the current state of your workflow at any time when it's not running. For example, you can change configurations of some nodes, execute several other nodes and, afterwards, save your workflow to get back to it later and continue execution. To do this, press `Save` at the top-left corner of the KNIME window when your workflow is opened. -###5. Share your workflow +### 5. Share your workflow You can save your workflow to a *.zip file and send it to another person afterwards. diff --git a/LICENSE.md b/LICENSE.md index 24b4504..06b952f 100644 --- a/LICENSE.md +++ b/LICENSE.md @@ -1,4 +1,4 @@ -Copyright 2015 Theodore Alexandrov and Ivan Protsyuk +Copyright 2015-2017, Theodore Alexandrov and Ivan Protsyuk Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. diff --git a/README.md b/README.md index 9a673c5..afd6855 100644 --- a/README.md +++ b/README.md @@ -27,14 +27,14 @@ Optimus is a workflow for LC-MS-based untargeted metabolomics. It can be used for feature detection, quantification, filtering (e.g. removing background features), annotation, normalization and, finally, for spatial mapping of detected molecular features in 2D and 3D using the [`ili app](https://github.com/ili-toolbox/ili). Optimus employes the state-of-the-art LC-MS feature detection and quantification algorithms by [OpenMS](http://www.openms.de) which are joined into a handy pipeline with a modern workflow management software [KNIME](https://www.knime.org) with additional features implemented by us. -The workflow is being developed by [Alexandrov Team](http://www.embl.de/research/units/scb/alexandrov/index.html) at EMBL Heidelberg ([contact information](http://www.embl.de/research/units/scb/alexandrov/contact/index.html)). +The workflow is being developed by [Alexandrov Team](http://www.embl.de/research/units/scb/alexandrov/index.html) at EMBL Heidelberg ([contact information](http://www.embl.de/research/units/scb/alexandrov/contact/index.html)) in collaboration with the [Dorrestein Lab](http://dorresteinlab.ucsd.edu/Dorrestein_Lab/Welcome.html) at UCSD. * Developer: Ivan Protsyuk * Principal investigator: Theodore Alexandrov ## Who needs this workflow? -The workflow was initially developed for LC-MS-based metabolite cartography, but can be useful in almost any study of LC-MS-based untargeted metabolomics. It is developed to be open-source, sharable, and efficient enough to process hundreds of LC-MS runs in reasonable time. +The workflow was initially developed for LC-MS-based metabolite cartography, but can be useful in almost any study of LC-MS-based untargeted metabolomics. Direct-infusion experimental data is also supported. Optimus is developed to be open-source, sharable, and efficient enough to process hundreds of LC-MS runs in reasonable time. ## What it does? @@ -174,22 +174,22 @@ The third `OptimusViewer_input.db` file contains extracted ion chromatograms (XI ## KNIME Basics -If you're new to workflow management systems or KNIME in particular, you can find an introductory tutorial on basic features of KNIME [here](./KNIME Basics.md). +If you're new to workflow management systems or KNIME in particular, you can find an introductory tutorial on basic features of KNIME [here](./KNIME%20Basics.md). ## Demo This repository contains real-life samples that you can test the workflow on. They're available in this [archive](./examples/3D/apple_samples.zip) (courtesy of Alexey Melnik, Dorrestein Lab, UCSD). Inside, you'll find a directory called `samples` that contains LC-MS samples in mzXML format ready to be processed with the workflow. Blank samples separated from the normal ones in the `blanks` directory inside `samples`. They can be used to remove background features from your result features set. -There're also 2 files in the root folder called `coords.csv` and `Rotten_Apple_Model.stl`. You'll need to supply them at the last step of the workflow that is supposed to produce spatial maps for `ili. +There're also 2 files in the root folder called `coords.csv` and `Rotten_Apple_Model.stl`. You'll need to supply the former one at the last step of the workflow that is supposed to produce spatial maps for `ili. -If you want to check quickly, what are actually the results of the workflow, without diving into KNIME and installing everything, you can find the needed file in the `results` folder in the archive. It contains file `features_mapping.csv` which is a spreadsheet containing a table with intensities of different features detected in different runs. This file can be visualized in `ili along with `Rotten_Apple_Model.stl`. You can simply drag&drop both of them to the `ili window. +If you want to check quickly, what are actually the results of the workflow, without diving into KNIME and installing everything, you can find the needed file in the `results` folder in the archive. It contains file `features_mapping.csv` which is a spreadsheet containing a table with intensities of different features detected in different runs. This file can be visualized in `ili along with `Rotten_Apple_Model.stl`. You can simply drag&drop both of them to the [`ili](http://ili.embl.de) window. -Below, you can find an example of a spatial map obtained from `ili for a feature that is localized mainly in the vicinity of rot on the apple. +Below, you can find an example of a spatial map obtained from `ili for a feature that is localized mainly in the vicinity of rot on the apple. ## Advanced use-cases -The workflow has many capabilities that you can discover in the documentation embedded into it. Just click on any node, and the description of its role and its parameters will show up in the banner at the right-hand side of the KNIME window. Different nodes don't depend on each other, so you can experiment with different settings and track changes of the workflow output. +The workflow has many capabilities that you can discover in the documentation embedded into it. Click on any node, and the description of its role and its parameters will show up in the banner at the right-hand side of the KNIME window. Different nodes don't depend on each other, so you can experiment with different settings and track changes of the workflow output. ## Troubleshooting @@ -213,7 +213,7 @@ Some errors can appear in the application log that interrupt workflow execution. A computer runs out of hard drive space when Optimus is running Temporary files produced by Optimus are too large. - Cancel the workflow execution. Either free up some space or use space from another hard disk drive for temporary files as follows. Make sure an additional hard drive is connected. Open KNIME preferences dialog and in the “KNIME” section set “Directory for temporary files” to be located in a hard drive with more free space available. Restart KNIME to apply the new settings. + Cancel the workflow execution. Either free up some space or use space from another hard disk drive for temporary files as follows. Make sure an additional hard drive is connected. Open KNIME preferences dialog and in the KNIME section set Directory for temporary files to be located in a hard drive with more free space available. Restart KNIME to apply the new settings. ValueError: Input list of LC-MS features is empty. Try to change settings of feature detection or your filters.