1. Bioinformatics for Stem Cell Lecture 2
Debashis Sahoo, PhD

2. Outline Lecture 1 Recap Multivariate analysis Microarray data analysis
Boolean analysis
Sequencing data analysis

3. Multivariate Analysis

4. Identify Markers of Human Colon Cancer and Normal Colon
Piero Dalerba
Tomer Kalisky

5. Single Cell Analysis of Normal Human Colon Epithelium

6. Hierarchical Clustering

7. Hierarchical Clustering
Distance metric
Euclidian, Squared Euclidean, Manhattan, maximum, cosine, Pearson’s correlation
Linkage
Single, complete, average, median, centroid

8. Multivariate Analysis - PCA
Principal Component Analysis
X = data matrix
V = loading matrix
U = scores matrix

9. Fundamentals of PCA Reduces dimensions of the data
PCA uses orthogonal linear transformation
First principal component has the largest possible variance.
Exploratory tool to uncover unknown trends in the data

10. PCA Analysis

11. High-throughput data analysis

12. Microarray analysis

13. Microarray Spotted vs. in situ Two channel vs. one channel
Probe vs. probeset vs. gene

14. Quantile Normalization#1 #2 #3
SortedAvg
Average
Sort
Val(Probe_i) =
SortedAvg[Rank(Probe_i)]

15. Invariant Set Normalization
Before
Normalization
After
Invariant set

16. Good to Check the Image

17. SAM Two-Class Unpaired
Assign experiments to two groups, e.g., in the expression matrix
below, assign Experiments 1, 2 and 5 to group A, and
experiments 3, 4 and 6 to group B.
Exp 1
Exp 2
Exp 3
Exp 4
Exp 5
Exp 6
Gene 1
Gene 2
Gene 3
Gene 4
Gene 5
Gene 6
Group A
Group B
Exp 1
Exp 2
Exp 3
Exp 4
Exp 5
Exp 6
Gene 1
Gene 2
Gene 3
Gene 4
Gene 5
Gene 6
2. Question: Is mean expression level of a gene in group A
significantly different from mean expression level in group B?

18. SAM Two-Class Unpaired
Permutation tests
For each gene, compute d-value (analogous to t-statistic). This is
the observed d-value for that gene.
ii) Rank the genes in ascending order of their d-values.
iii) Randomly shuffle the values of the genes between groups A and B,
such that the reshuffled groups A and B respectively have the same
number of elements as the original groups A and B. Compute the
d-value for each randomized gene
Exp 1
Exp 2
Exp 3
Exp 4
Exp 5
Exp 6
Gene 1
Group A
Group B
Original grouping
Exp 1
Exp 4
Exp 5
Exp 2
Exp 3
Exp 6
Gene 1
Group A
Group B
Randomized grouping

19. SAM Two-Class Unpaired
iv) Rank the permuted d-values of the genes in ascending order
v) Repeat steps iii) and iv) many times, so that each gene has many
randomized d-values corresponding to its rank from the observed
(unpermuted) d-value. Take the average of the randomized d-values
for each gene. This is the expected d-value of that gene.
vi) Plot the observed d-values vs. the expected d-values

20. SAM Two-Class Unpaired
Significant positive genes
(i.e., mean expression of group B >
mean expression of group A)
SAM Two-Class Unpaired
“Observed d = expected d” line
The more a gene deviates from the “observed = expected” line, the more likely it is to be significant. Any gene beyond the first gene in the +ve or –ve direction on the x-axis (including the first gene), whose observed exceeds the expected by at least delta, is considered significant.
Significant negative genes
(i.e., mean expression of group A > mean expression of group B)

21. GenePattern

22. AutoSOME http://jimcooperlab.mcdb.ucsb.edu/autosome/ Aaron Newman
Aaron Newman and James Cooper, BMC Bioinformatics, 2010, 11:117

23. Gene Set Analysis Your Gene Set Cell Cycle Transcription factor
Compute enrichment in pathways and networks
TGF-beta Signaling Pathway
Wnt-signaling Pathway
Protein-protein
interaction network
Tools: GSEA, DAVID, Toppfun, MSigDB, and STRING

24. Boolean Analysis

25. Boolean Implication Analyze pairs of genes.
Analyze the four different quadrants.
Identify sparse quadrants.
Record the Boolean relationships.
If ACPP high, then GABRB1 low
If GABRB1 high, then ACPP low
45,000 Affymetrix microarrays
GABRB1
Put the introductory slides
How many microarrays
Seems like a fundamental…
If -> then
Describe x and y axis.
Describe a point.
Statistical tests for identifying sparse quadrant.
ACPP
[Sahoo et al. Genome Biology 08]

26. Threshold Calculation
A threshold is determined for each gene.
The arrays are sorted by gene expression
StepMiner is used to determine the threshold
High
CDH expression
Intermediate
Threshold
Low
Say about linear shape.
Labels in the graph bigger.
Put forbidden zone
threshold. Labels.
Sorted arrays
[Sahoo et al. 07]

27. BooleanNet Statistics
nAlow = (a00+ a01), nBlow = (a00+ a10)
total = a00+ a01+ a10+ a11, observed = a00
expected = (nAlow/ total * nBlow/ total) * total
a00
a01
a11
a10 A B
statistic =
(expected – observed)
expected √
a00
(a00+ a01)
(a00+ a10) + ( ) 1 2
error rate =
Put the introductory slides
How many microarrays
Seems like a fundamental…
If -> then
Describe x and y axis.
Describe a point.
Statistical tests for identifying sparse quadrant.
Boolean Implication = (statistic > 3, error rate < 0.1)
[Sahoo et al. Genome Biology 08]

28. Six Boolean Implications
Sparse quadrants are highlighted.
Prepare a comparison slides.
Correlation vs Boolean
If then
Get rid of slide numbers
Divide the pictures:
Two slides
First show Asymmetric
Symmetric
[Sahoo et al. Genome Biology 08]

29. MiDReG Algorithm MiDReG = (Mining Developmentally Regulated Genes)
Replace seed with Gene A
Same slides with and without gene X
Just Differentiation
Make the arrow visible
Spell MiDReG
[Sahoo et al. PNAS 2010]

30. MiDReG Algorithm MiDReG = (Mining Developmentally Regulated Genes)
[Sahoo et al. PNAS 2010]

31. MiDReG Algorithm MiDReG = (Mining Developmentally Regulated Genes)
[Sahoo et al. PNAS 2010]

32. B Cell Genes Boolean Implications KIT CD19 [Sahoo et al. PNAS 2010]
Show actual Boolean implication/RTPCR
19 genes – put numbers
Say that cancer datasets can predict normal differentiation steps
Take the Stanford logo out
[Sahoo et al. PNAS 2010]

33. http://gexc.stanford.edu Jun Seita
Explain it better
[Seita, Sahoo et al. PLoS ONE, 2012]

34. Sequencing data analysis

35. Sequencing Data Format
>SEQUENCE_1
MTEITAAMVKELRESTGAGMMDCKNALSETNGDFDKAVQLLREKGLGKAAKKADRLAAEG
LVSVKVSDDFTIAAMRPSYLSYEDLDMTFVENEYKALVAELEKENEERRRLKDPNKPEHK
IPQFASRKQLSDAILKEAEEKIKEELKAQGKPEKIWDNIIPGKMNSFIADNSQLDSKLTL
MGQFYVMDDKKTVEQVIAEKEKEFGGKIKIVEFICFEVGEGLEKKTEDFAAEVAAQL
>SEQUENCE_2
SATVSEINSETDFVAKNDQFIALTKDTTAHIQSNSLQSVEELHSSTINGVKFEEYLKSQI
ATIGENLVVRRFATLKAGANGVVNGYIHTNGRVGVVIAAACDSAEVASKSRDLLRQICMH
FASTA
@HWI-EAS209:5:58:5894:21141#ATCACG/1
TTAATTGGTAAATAAATCTCCTAATAGCTTAGATNT
+HWI-EAS209:5:58:5894:21141#ATCACG/1
efcfffffcfeefffcffffffddf`feed]`]_Ba
FASTQ
S - Sanger Phred+33, (0, 40)
X - Solexa Solexa+64,(-5, 40)
I - Illumina 1.3+ Phred+64, (0, 40)
J - Illumina 1.5+ Phred+64, (3, 40)
L - Illumina 1.8+ Phred+33, (0, 41)

36. Mapping

37. Mapping Software Long reads Short reads BLAST, HMMER, SSEARCH BLAT
Bowtie, BWA, Partek, SOAP, Tophat, Olego, BarraCUDA

38. Visualizations

39. Visualizations UCSC Genome Browser
GenoViewer, Samtools tview, MaqView, rtracklayer, BamView, gbrowse2
Integrative Genomics Viewer (IGV)

40. Quantification Peak calling Expression quantification SNP calling
QuEST, MACS, PeakSeq, T-PIC, SIPeS, GLITR, SICER, SiSSRs, OMT
Expression quantification
Cufflinks, NEUMA, RSEM, ABySS, ERANGE, RSAT, Velvet, MISO, RSEQ
SNP calling
samtools, VarScan, GATK, SOAP2, realSFS, Beagle, QCall, MaCH

41. Peak Discovery
[Pepke et al. Nature Methods 2009]

42. Transcript Quantification
RPKM, FPKM
[Pepke et al. Nature Methods 2009]

43. SNP Calling

44. Typical RNA-seq Workflow
[Trapnell et al. Nature Biotech 2010]

45. [Trapnell et al. Nature Biotech 2010]