T-BioInfo is a platform for processing, analyzing, and integrating multi-omics data. It is used by multiple research groups to extract meaningful insights from large multi-omics datasets. The platform is expanding its educational capabilities to enable more people to extract meaningful, data-driven insights from omics datasets with biomedical applications. The document provides links to learn more about the platform's research and educational features.

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2. 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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6. Modeling Precision Medicine
Machine Learning forTranscriptomics Data: Extracting Meaningful
insights from high-throughput biomedical data.
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7. Clinical Subtypes Molecular Subtypes
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8. Diagnosis, Prognosis, Response toTreatment
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Survival prediction
Treatment Selection
OncotypeDXPAM50

9. 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 9

10. Files we will use in this session
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Q&A

12. Part 1:
RNA-Seq Processing
from raw reads to a table of expression
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13. 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

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.…TCTGAAACAATGCTTCAATCTAACTTATCATTCATTGGGA….Gene A Gene B Gene C
Transcr. ATranscript A Transcr. ATranscript C
Reads
RNA-Seq: some details
1. Shattering 2. Adapters ligation 3. PCR amplification 4. “Reading”

16. Preprocessing:
• Adapters removal plus additional
• 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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Data Processing Practice
Create a pipeline:
1. Upload same SVL files
2. pre-processing steps:Trimmomatic, PCRclean
3. Mapping on Genome: HiSat2
4. IsoformConstruction: Cufflinks
5. GTF Merging: Cuffmerge
6. Mapping onTranscripts: Bowtie2-t
7. Quantification: RSEMExpTable

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RNA-Seq: extended pipeline

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ExpressionTable
Sample Name
Gene ID What is this number?

21. Standard Measures of RNA Quantification:
• Counts
• FPKM – fragments per kilobase per million mapped reads:
Number of reads mapped on the gene
((total number of mapped reads – in millions) x (gene length in
kilobases))
• TPM – transcripts per million
For one sample TPMg = C x FPKMg, where C is selected in such a way that sum of all
million. Constants C are different for different samples.
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22. Linear scale vs Log-scale
Relative differences are biologically more meaningful than absolute.
Computations are simplified if a log-scaling is performed:
Log-scaled measure =
log2 (linear-scale measure + shift)
For relatively large values:
difference equal to 1 in log-scale is a 2x difference in linear scale;
difference equal to 3 in log-scale is a 8x difference in linear scale. etc;
difference equal to -1 in log-scale is a 2x difference in linear scale, but in the opposite
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23. Preprocessing:
• Adapters removal plus additional
• 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

24. Comparison: the role of preprocessing
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High expression can be affected by pre-processing steps like PCR-clean and “Trimmomatic”

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Q&A

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Q&A
Error Correction – CORAL, ECHO, RACER, eMER
Different Mappers – HiSat,TopHat, STAR, BWA
Other Sections:
• Differential Expression – CuffDiff, EDGER, DESEQ
• Segmentation - BinS

27. Part 2:
Machine Learning
Data exploration and classification
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Unsupervised Machine Learning
Dog
Dog
Dog
Cat
Cat
Cat

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Group 1
Group 2
Outlier

30. 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

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

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Unsupervised Analysis Practice
• Remove sample IDs
• Mark Group Names as ID
• Run H-clust
CellLines_ExprData_marked.txt

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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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SupportVector Machine (SVM) with Linear Kernel
d
d

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SupportVector Machine (SVM) with Linear Kernel
?

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Support Vector Machine (SVM) with Linear Kernel

43. • Fitting classifier on training set and predicting classes on the test set
• Is it possible to tune 7000 coefficients by 52 samples?
• Some algorithms do feature selection: swLDA, random forest
• Other algorithms won’t work if number of features >> number of
samples
• Curse of dimensionality
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Considerations Supervised analysis

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• Extracting 15 highly informative genes from the swLDA classifier
• How other supervised learning algorithms can be applied (e.g.,
SVM)
• Feature selection can also improve quality of unsupervised learning
analysis
Step-wise Linear Discriminant Analysis (swLDA)

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Classification Practice
• Organize the table with 15
genes by sample type
• Color expression (green –
low; red – high)
• Which genes stand out?
• Which sample stand out?
• What groups are hard to
detect?
CellLines_15Genes_market.txt

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Classification Practice: PCA of 15 gene table

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Hierarchical Clustering of 15 gene table
N-like Basal
C-low
Luminal4 clusters

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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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Annotation Practice

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http://www.oncotarget.com/index.php?journal=oncotarget&page=arti
cle&op=view&path[]=23869&path[]=75083
https://www.nature.com/articles/1208329

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Q&A

54. 1. PCA plot using top 15 genes
from differential expression analysis
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Homework:

55. Separation of samples from various sources:TCGA and PDX
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2. New Datasets

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Part 1: Conventional Machine Learning Approaches for Next
Generation Sequencing
Rapid RNA-seq processing for expression quantification applying
logical pipeline construction and pre-processing considerations.
hands-on exercises, participants will explore the expression
using conventional unsupervised machine learning methods and
supervised classifiers with and without feature extraction. Using
BioInfo platform, participants will learn about the logic and
considerations of applying such methods and be prepared for
independent downstream analysis and visualization of data
downloaded R scripts produced by the system. The
produced/downloaded code will be reviewed, customized and
subsequent session.
T-bio.info | edu.t-bio.info (FREE) | server.t-bio.info (14 days DEMO)

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Required installations:
R >= 3.4
R Studio
gplots
ggfortify
ggplot2
ggpubr
e1071
mda
MASS
klaR
Part 2: Combining custom software with R to
streamline analysis workflows and visualize ‘Omics
data insights.
Differential Gene Expression, Gene Set Enrichment
Analysis
R visualization from scratch: utilize the same dataset for
basic data exploration and visualization in R.
This session will strengthen the participants ability to
transition to script-based workflows in RNA-seq
downstream analysis and visualization. Participants will
learn about downstream capabilities of R-based workflow
to transform and manipulate tables and visualize findings
in a meaningful way.

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Download and Modify R Scripts

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61. Differential expression analysis
Quantities related to the degree of differential expression:
• Difference between mean expression levels – fold change
(please, pay attention to scale);
• Statistical significance – p-value, adjusted p-value (e.g., FDR)
• Level of Expression (caution with low-expressed genes from the
analysis)
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62. • Hard to interpret when number of groups is greater than two, so we can use Claudin-low vs normal-
like groups.
• Differential Expression is a natural and easy to interpret feature selection procedure.
• Pathway enrichment analysis can be applied to the resulting table 62
Differential expression analysis

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Differential expression analysis

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Differential expression analysis

65. Gene set / pathway enrichment analysis
GAGE -
• Use only lists (thresholding required): one of the standard tools here isThe
Database for Annotation,Visualization and Integrated Discovery – DAVID
(https://david.ncifcrf.gov/home.jsp, https://david-d.ncifcrf.gov/).
• Takes into consideration level of differential expression
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Gene set / pathway enrichment analysis

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Gene set / pathway enrichment analysis

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Gene set / pathway enrichment analysis
Regulation of Actin Cytoskeleton B Cell Receptor Signaling Pathway

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Required installations:
R >= 3.4
R Studio
gplots
ggfortify
ggplot2
ggpubr
e1071
mda
MASS
klaR
Part 2: Combining custom software with R to
streamline analysis workflows and visualize ‘Omics
data insights.
Differential Gene Expression, Gene Set Enrichment
Analysis
R visualization from scratch: utilize the same dataset for
basic data exploration and visualization in R.
This session will strengthen the participants ability to
transition to script-based workflows in RNA-seq
downstream analysis and visualization. Participants will
learn about downstream capabilities of R-based workflow
to transform and manipulate tables and visualize findings
in a meaningful way.

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R Studio

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