1. Many Sample Size and Power Calculators Exist On-Line

2. Day 3 Session 16: Questions and follow-up…. James C. Fleet, PhD
Distinguished Professor
Department of Nutrition Science
Pete Pascuzzi, PhD
Assistant Professor
Purdue Libraries

3. Day 3 Session 17: Visualization III: Networks James C. Fleet, PhD
Distinguished Professor
Department of Nutrition Science
Pete Pascuzzi, PhD
Assistant Professor
Purdue Libraries

4. Pathways vs. Networks Pathway: Network:
Both aggregate molecular events across multiple genes . Increases statistical detection threshold by the number of hypotheses tested
Pathway:
Small scale
Well-studied
Known linear relationship
Easily visualized and interpreted
Network:
Large scale
Integration of multiple studies
Hard to visualize and interpret
Contain novel information not covered in pathways
Creixell et al. (2015) Nature Methods 12: 615
Fleet 2016

5. Patterns of Regulation in Genomic Data : Guilt by Association
Human primary fibroblast cultures
Serum starvation and refeeding
9600 transcripts, spotted cDNA array
Hierarchical clustering
* Genes in common cluster = common molecular regulation?
Iyer et al., 1999, Science 283:83
Fleet 2016

6. Subnetwork construction and clustering Gene set enrichment
Network-based modeling
Simple but discard known biological network information
Creixell et al. (2015) Nature Methods 12: 615
Fleet 2016

7. Networks Integrate Information
Dong and Han (2008) Cell Res 18: 224
Fleet 2016

8.
……an open source software tool for integrating, visualizing, and analyzing data in the context of networks.
This does not do primary network building from your dataset.
Fleet 2016

9. Data Format: *.txt or Excel (1st worksheet only)
Header Row
Identifier
G1 Value
G1 FC
G1 FDR
G2 value
G2 FC
G2 FDR
Up to 20 observations per treatment/group
* IPA can
average
Stats done prior to IPA
Valid Expression Value type       Expected Values Ratio                                               (0, +INF) Fold Change                                   (-INF, -1) (1, +INF) LogRatio                                         (-INF, +INF). p-value                                            (0, 1) FDR, q-value                                   (0, 100) Intensity                                          (0, +INF) RPKM/FPKM                                  (0, +INF)
(INF = infinity)
Fleet 2016

10. Ingenuity Network Analysis
Expression Dataset
Network
Network Generation
IPA Knowledge Base
Genes in Network
DEG fit to a probabilistic fit to networks
Scored Genes
DEG
Network Scoring
Associated
Functions
DEG fit involved in a biological function
DEG = differentially expressed genes

11. Wednesday
BREAK #1

12. Day 3 Session 18: Flexible time for reinforcement… James C. Fleet, PhD
Distinguished Professor
Department of Nutrition Science
Pete Pascuzzi, PhD
Assistant Professor
Purdue Libraries

13. Day 3 Session 19: Patrick Finnegan Hardware Engineer Purdue University
Tour of Data Center and Conte Cluster

14. Day 3 Session 20: Introduction to NGS James C. Fleet, PhD
Distinguished Professor
Department of Nutrition Science
Pete Pascuzzi, PhD
Assistant Professor
Purdue Libraries

15. Integration and interpretation
Data Analysis
Integration and interpretation
Technology
SNP
Indels
Genomics
WGS, WES
CNV
Functional effect of mutation
Structural
Discovery and Application
DGE
Network + pathway analysis
Transcript-omics
RNA-Seq
Fusion
Splicing
Editing
Integrative analysis
Methyl DNA
Epigenomics
Bisulfite-Seq
ChIP-Seq
Histones
TF binding
Modified from Shyr D, Liu Q. Biol Proced Online. (2013)15,4
Fleet 2016

16. NGS Sequencing Pipeline
Input Fragment Add adapter
Fragment library
Library preparation
Library amplification
Parallel sequencing
Read 1
Reads
Read 2
Voelkerding et al., J Mol Diagn (2010) 12,
Fleet 2016

17. How Much Sequencing is Enough?
Read Length
# Reads/Rxn
Target
Coverage
# reads
DNA variation
10-30X
N/A
ChIP-seq
100X
RNA-seq (DEG)
(rare)
20 million
100+ million
Sims et al. (2014) Nat Rev Genet 15:121
Fleet 2016

18. Stephen Turner, PhD; Director
Stephen Turner, PhD; Director. University of Virginia Bioinformatics Core
Fleet 2016

19. Understanding RNA-seq
Fleet 2016

20. Correlation between RNA-seq and Microarray Analysis
Analysis from two different S. cervasisiae papers using the same growth conditions
Modified from Wang et al. (2009) Nat Rev Genet 10:57
Tiling Array (log2)
RNA-seq (log2)
Array (log2)
Zhao et al. (2014) PLOS One
Activated T cells
RNA-seq (log2)
Fleet 2016

21. RNA-seq vs. Microarray: Which is “better”?
Issue
Microarray
RNA-seq
Reproducibility
High
Dynamic Range
Modest
Wide
Sensitivity
Low/Medium
Accuracy
High (but better for FC)
Cost
Low
Complexity of analysis
Species
Limited to available platforms
Any species possible
Fleet 2016

22. Wednesday
BREAK #2

23. Day 3 Session 21: Visualization IV: IGV James C. Fleet, PhD
Distinguished Professor
Department of Nutrition Science
Pete Pascuzzi, PhD
Assistant Professor
Purdue Libraries

24. Fleet 2016

25. Data for Visualization Lives Here……
Fleet 2016

26. Types of Files Commonly Used
File type
Description
SAM
Tab-delimited text file of sequence alignment data (i.e. primary read data)
BAM
Binary version of the SAM file
Bedgraph
Display of continuously valued data (e.g. transcriptome)
Wiggle (Wig)
bigWig
Displays dense continuous data from Wig or bedgraph files for faster viewing
BED
Tiled data file that defines a feature track
TDF
Binary tiled data file that has been preprocessed for faster displays in IGV (e.g. for ChIP- and RNA-seq data)
narrowPeak
Called peaks of signal enrichment based on pooled, normalized data
Fleet 2016

27. IGV Displays Various File Types
Mouse Large Intestine DNAse-Seq Data from ENCODE
Chromosomal
Location
BroadPeaks
NarrowPeaks
bigWig
Bam
Refseq genes
Fleet 2016

28. Visualizing RNA-seq Data
Cell line
Tissue ?