Pine Biotech conducts monthly informational workshops on the topics related to high-throughput data analysis, interpretation and integration. The workshops highlight our research tools and educational resources developed with collaborators in the US and across the world.

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Elia Brodsky
Co-founder and CEO
Pine Biotech (pine-biotech.com)
Survey: bit.ly/2KG7iEj
Georgetown

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Overview
Education & Training
Research Support
Survey: bit.ly/2KG7iEj

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Patients
Most important clinical features
Normalizedclinicalfeatures
Missing Values
Group-to-group
associations
(bipartite network)
OmicsFeatures
PREDICTION
Enrichment of Clinical Data
Clinical characteristics of patients that are of most interest
can be predicted using other clinical features. This type of
group-to-group association network can be prepared within
a normalized set of clinical features across patients, thus
identifying patients at risk or selecting patients for specific
data to be generated. Validated associations can become a
part of a clinical application used regularly to select patients
or match patients to requirements.
Integration of Omics Data
Omics data represents a feature-rich resource that can be
used for discovery of underlying biology linked to a
particular condition, disease stage, co-morbidities and
population stratification. Integrating the omics data into a
usable, normalized resource and linking it to clinical
outcomes can enable precision targeting of patients and
more precise treatment for complex diseases.
Machine Learning Applications

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Exploring Ideas Experiment Design Research and Development
Data is playing a key role in all stages of R&D:

7. the National Center
for Biotechnology
Information (NCBI)
Gene Expression
Omnibus repository
(GEO) alone
contains 80,985
public datasets,
spanning hundreds
of tissue types in
thousands of
organisms
Omics Data: What’s out there?
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Education & Training
Research Support
Dataset: bit.ly/2vNovGC

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Online resources Curated Datasets User-friendly Tools

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The T-BioInfo Platform is at the core of this experience
Server.t-bio.info

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15. INDEPENDENT PROJECTS (ONCOLOGY SPECIALIZATION EXAMPLE)
Projects are prepared from high impact publications relevant to the specialization. Public domain datasets are curated to prepare focused
assignments illustrating how the data is processed and used to achieve similar results to the publication. Other approaches that can perform a
similar function are discussed, providing a review of the methods section of the paper. Finally, full dataset is organized into a project format that
can be analyzed for discovery.

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https://edu.t-bio.info/workshops/

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FALL 2018
INDIA:
USA:
ONLINE:
Louisiana Biomedical Research Network – September 2018
Georgetown University – October 2018
August 25 – workshop in Kolkata (APT SOFTWARE AT SALT LAKE)
September 6 – AIIMS (AIIMS, THEATRE ROOM )
September 15: Short Term Training in Next Generation Sequencing
(Transcriptomics and Genomics)
October 15: Short Term Training in Machine Learning for Biomedical Data

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server.t-bio.info
Login: test@pine-biotech.com
Password: WF(9iobE
Files: bit.ly/2vNovGC

19. T-BioInfo is designed for processing, analysis and
integration of multi-omics data. The platform is used in
multiple research groups to extract meaningful insights
from large multi-omics datasets. Our current effort
expands to education, by enabling more people to
extract meaningful, data-driven insights from omics
datasets with biomedical applications. To learn more
about the platform and it’s research and educational
features, follow the highlighted links .
T-bio.info | edu.t-bio.info | server.t-bio.info
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20. Modeling Precision Medicine
Machine Learning for Transcriptomics Data: Extracting
Meaningful insights from high-throughput biomedical data.
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21. Clinical Subtypes Molecular Subtypes
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22. Diagnosis, Prognosis, Response to Treatment
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Survival prediction
Treatment Selection
OncotypeDXPAM50

23. Daemen et al., 2013, “Modeling precision treatment of breast cancer”: an analysis of over 70 different Breast Cancer cell lines and over 90
different therapeutic agents. https://genomebiology.biomedcentral.com/articles/10.1186/gb-2013-14-10-r110
Sample 1 Sample 2 Sample 3 Sample 4
gene 1 4 3 3 7
gene 2 6 5 5 8
gene 3 6 6 6 6
gene 4 1 2 1 2
gene 5 9 10 1 5
gene 6 12 4 0 5
gene 7 1 7 9 8
gene 8 4 8 3 10 23

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server.t-bio.info
Login: test@pine-biotech.com
Password: WF(9iobE
Files: https://bit.ly/2n54U0A

25. Files we will use in this session
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/export-data/sciservice/data/pipelines/5629cc88e71b7bf5/upload/SRR925687_1.fq
/export-data/sciservice/data/pipelines/5629cc88e71b7bf5/upload/SRR925687_2.fq
/export-data/sciservice/data/pipelines/5629cc88e71b7bf5/upload/SRR925697_1.fq
/export-data/sciservice/data/pipelines/5629cc88e71b7bf5/upload/SRR925697_2.fq

26. Part 1:
RNA-Seq Processing
from raw reads to a table of expression
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27. RNA-Seq: overview
.…TCTGAAACAATGCTTCAATCTAACTTATCATTCATTGGGA….
Genome
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Gene A Gene B Gene C
Transcr. ATranscript A Transcr. ATranscript C

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.…TCTGAAACAATGCTTCAATCTAACTTATCATTCATTGGGA….Gene A Gene B Gene C
Transcr. ATranscript A Transcr. ATranscript C
Reads
RNA-Seq: overview

29. Preprocessing:
• Adapters removal plus additional
trimming
• Removing PCR duplicates
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Quantification of expression levels
Mapping
• Mapping on the set of known transcripts
• Mapping on genome (and potential
identification of novel transcripts)
• Combined strategy
RNA-Seq: overview

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RNA-Seq: basic pipeline

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RNA-Seq: extended pipeline

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Expression Table
Sample Name
Gene ID What is this number?

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34. Part 2:
Machine Learning
Data exploration and classification
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Group 1
Group 2
Outlier

36. Unsupervised analysis: PCA
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• Explore data
• Visualize
Why use Principal Component
Analysis?
• Data Filtering
• Outliers
• Interpretation
Considerations:

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Unsupervised analysis: PCA

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Unsupervised analysis: PCA
PCA 7,000 genes PCA PAM50 (35) genes
Normal-like
Basal
Claudin-low
Luminal

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Unsupervised analysis: Hierarchical Clustering
• Identify groups
• Associate sample to group
Why use clustering?
• Various methods
• Random selection in some methods
• Interpretation
Considerations:

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Unsupervised analysis: Hierarchical Clustering

41. Unsupervised analysis: hierarchical clustering
Dendrogram
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2 clusters
4 clusters
8 clusters

42. BREAK
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Q&A

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DogsCats
?????
Training Set Test Set
Supervised Machine Learning

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Step-wise Linear Discriminant Analysis (swLDA)

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Support Vector Machine (SVM) with Linear Kernel
d
d

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Support Vector Machine (SVM) with Linear Kernel
?

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Support Vector Machine (SVM) with Linear Kernel

48. BREAK
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Q&A

49. Part 3:
Interpretation
Annotating and Interpreting Gene Expression
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50. Gene annotation: ENSG to Gene Symbols plus GO
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51. BREAK
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Q&A

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Download and Modify R Scripts

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Upcoming Programs and Registration:
Elia Brodsky
Co-founder and CEO
Pine Biotech (pine-biotech.com)
bit.ly/2KG7iEj
https://edu.t-bio.info/workshops/
elia@pine.bio; sahil@pine.bio