Interactive and reproducible data analysis with the open-source KNIME Analytics Platform

© 2018 KNIME AG. All Rights Reserved.
Interactive and reproducible data
analysis with the open-source KNIME
Analytics Platform
Greg Landrum, Ph.D.
KNIME AG
@dr_greg_landrum
ACS New Orleans
19 March 2018

© 2018 KNIME AG. All Rights Reserved. 2
Topics
• A brief intro to KNIME
– The company
– The software
• Context: some data analysis problems we’re trying
to help with using workflows
• A case study of reproducible interactive data
analysis in KNIME

KNIME, the company
• KNIME AG founded in 2008
• Offices in Zürich (HQ), Konstanz, Berlin, and Austin
• 40+ employees
• Maintainer of the Open Source KNIME Analytics Platform
– comprehensive data loading, processing, analysis, modeling platform
– visual frontend
– open: to all sorts of data, other tools (R and Python, etc.), various user
personas
– 20+ open source releases since 2006
– open source.
• KNIME Server
– 14 commercial product releases since 2008
• KNIME cloud offerings

The KNIME® Analytics Platform

Visual KNIME Workflows
Nodes perform tasks on data
Workflows combine nodes
to model data flow
Status
Input(s)
Outputs
Not Configured
Idle
Executed
Error

Analysis & Mining
Statistics, Machine Learning, Data
Mining, Web Analytics, Text
Mining, Network Analysis, Social
Media Analysis, R, Weka, Python,
Community / 3rd party, ...
Data Access
MySQL, Oracle, ...
SAS, SPSS, ...
Excel, Flat, ...
Hive, Impala, ...
XML, JSON, PMML
Text, Doc, Image, ...
Web Crawlers,
Industry Specific,
Community / 3rd
party ...
Transformation
Row, Column, Matrix
Text, Image, Networks, Time
Series, Java, Python,
Visualization
R, Python,
JFreeChart,
JavaScript,
Deployment
via BIRT
PMML, XML, JSON
Databases, Excel, Flat, etc.
Text, Doc, Image
Industry Specific
Over 2000 native and embedded nodes included:
Big Data
Hive, Impala, HDFS Vertica,
Teradata/Aster, Spark, MLlib,

Free E-Learning Course: Web Page
7
• Hands-on e-learning course
• Data Access, ETL, Analytics, Control
Structures, Visualization
• Around 50 small units
• … with exercises
• … and with solutions on the
EXAMPLES server
• Final exercises to test your
knowledge!
https://www.knime.org/knime-
introductory-course

The KNIME Software Ecosystem
Deployment:
- to Applications
- to Humans
Collaboration:
- Best Practices
- Sharing Expertise
Automation:
- Scheduling
- (Model) Management
KNIME
Analytics
Platform
KNIME
Supported
Extensions
KNIME
Extensions
Partner
Extensions
Community
Extensions
KNIME Server

KNIME Server
Shared Repositories Access Management Web Enablement
Flexible Execution

Some data analysis problems
• Interactive data analysis and modeling
• Repeatability and reproducibility

• The need to use multiple tools and multiple data
sources
• Collaboration between users with different
sophistication levels
• Deployment
• Just staying organized

sources
• Deployment
I think workflows can help
with these

sources
• Deployment
I think KNIME can help with
these

Interactive data analysis and modeling
• Fairly often the whole process of data
preprocessing, analysis, and modeling can’t be (or
shouldn’t be) fully automated.
• We want/need a human in the loop
• Would be lovely if this weren’t painful
Interactive

Repeatability and reproducibility
• I can reproduce what I did before or repeat the
same process with a different data set/method
• You can do the same thing
• Not necessarily talking about strict reproducibility
(out to the 15th decimal place), but if we miss that
we should be able discover where deviations come
from
Reproducible

The need to use multiple tools and multiple data sources
• There is no one-size-fits-all solution (or “one-stop
shop”)
• We’re inevitably going to be using more than one
piece of software and working with data from more
than one source.
Open

Collaboration between users with different sophistication levels
• Some personae:1
– The scripter/programmer: “I’ve got this great new
method you should try”
– The tool user: “I’ll use software, but there’s no way I’m
writing code”
– The “stakeholder”: “Those folks are doing useful stuff and
I need their results, but I don’t have time to learn some
complex new piece of software.”
• Would be lovely if enabling collaboration between
these different personae wasn’t painful
1 Yes, these are stereotypes
Collaborative

Deployment
• Once I’ve built something I’d like to make it available
to my colleagues
– Sharing models
– Sharing methods
– Sharing results
Deployable

Just staying organized
• I can usually remember where my scripts are
• There’s no way I can remember where yours are
• It would be lovely if it weren’t painful to find stuff
Findable

sources
• Deployment

sources
• Deployment
I think workflows can help
with these

sources
• Deployment
I think KNIME can help with
these

The case study: HTS hit list triage

Background
• The problem: Processing a hit list from a high-
throughput phenotypic screen for malaria.
– Clean up the hit list
– Suggest compounds to be sent to a validation assay
• Data source: 2014 Teach-Discover-Treat challenge
http://www.tdtproject.org/challenge-1---malaria-
hts.html
• Additional info:
– https://github.com/sriniker/TDT-tutorial-2014
– Riniker et al. https://f1000research.com/articles/6-1136/v2

Approach we’ll take: cleanup
• Remove ”ugly” molecules:
– PAINS filters1,2: containing substructures that are likely to
interfere with/have interfered with the assay.
– ”Rapid elimination of swill” (REOS)3: Too big, complicated
or greasy.
• Don’t want to apply these filters mindlessly, so we
should always look at the results and allow manual
rescue
1. Baell, J. B. & Holloway, G. A. J. Med. Chem. 53, 2719–40 (2010).
2. http://rdkit.blogspot.ch/2015/08/curating-pains-filters.html
3. Walters, W. P. & Namchuk, M. Nat. Rev. Drug Discov. 2, 259–66 (2003).

Approach we’ll take: selection for validation
• We want good coverage of the chemical space of
the HTS actives, but would ideally also like to learn
something from the validation results
• Approach:
– Start with a diverse subset of the cleaned actives
– Pick neighbors of each of these so that we have some SAR
information in the results
https://github.com/sriniker/TDT-tutorial-2014

The workflows
• Download (with data) from
EXAMPLES folder in KNIME itself

Deploying it
• Both workflows are built from a series of wrapped
metanodes. Each of these becomes a separate page
in a Web Portal app when the workflow is copied to
the KNIME server.
DeployableCollaborative

Cleanup workflow (part 1)

Interactive

Reproducible

Interactive

The output (in Excel)

Selection workflow

Selection workflow
Interactive

The output (in Excel)

Reviewing…
• Discussed today:
– Interactive data analysis and modeling
– Repeatability and reproducibility
– Collaboration between users with different sophistication
levels
– Deployment
• For the 40 minute version of this presentation:
– Just staying organized
– The need to use multiple tools and multiple data sources

US Roadshow

46© 2018 KNIME AG. All Rights Reserved.
KNIME is hiring!
• Software developers (Java and/or JavaScript)
• Data scientists (Application Scientists)
• Director of product marketing
Positions open in Austin, Berlin, Konstanz, and Zürich
More info: https://www.knime.com/careers

Backups

Validating Reproducibility
• Built-in support for validating that results generated
from one run to the next are the same
• Can be automated across multiple workflows or
groups of workflows

groups of workflows

The KNIME® trademark and logo and OPEN FOR INNOVATION® trademark are used by
KNIME.com AG under license from KNIME GmbH, and are registered in the United States.
KNIME® is also registered in Germany.

Interactive and reproducible data analysis with the open-source KNIME Analytics Platform

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