Course: Bioinformatics for Biomedical Research (2014). Session: 3.2- Basic Aspects of Microarray Technology and Data Analysis. Statistics and Bioinformatisc Unit (UEB) & High Technology Unit (UAT) from Vall d'Hebron Research Institute (www.vhir.org), Barcelona.

1. Bioinformàtica per a la
Recerca Biomèdica
Ricardo Gonzalo Sanz
ricardo.gonzalo@vhir.org
20/05/14
Hospital Universitari Vall d’Hebron
Institut de Recerca - VHIR
Institut d’Investigació Sanitària de l’Instituto de Salud Carlos III (ISCIII)
Basic aspects of Microarray
technology

2. Affymetrix microarrays manufacture.
2
3
4
5
6
Microarray experiment workflow.
Quality Controls.
Different types of Affymetrix arrays.
1 Introduction
Different types of arrays. Manufactoring. DNA/RNA/Protein

3. 1 Introduction
 reproducibility
 only show you what you’re looking for
 what about ‘indels’, inversions, translocations...
 accuracy
 sensitivity

4. 1 Introduction

5. 1 Introduction
 RNA-Seq was superior in detecting low abundance transcripts
 also better detecting differentiating biologically isoforms
 RNA-Seq demonstrated a broader dynamic range than microarray.

6. 1 Introduction
• In molecular biology exist a lot of techniques to measure the gene expression
(Northern blot)
• Main characteristic from the microarrays discovery (Schena et al. (1995)
Science 270:467-70), was not what could be measured, instead the quantity of
simultaneous measures that could be done.
• Pre microarrays time: study of genes was one by one
• Post microarrays time: all the genes together.

7. 1 Introduction
• But.... what is a microarray in few words?
 DNA fixed to a solid surface (nylon, silica, glass,...)
 RNA “problem” is labeled and have to bind to DNA
fixed in the solid surface in an specific way.
 DNA binded usually is called “probe”
 Labeled RNA usually is called “target”

8. Important to know in advanced...
1 Introduction
• Microarrays are usually hypothesis-generating:
They highlight specific genes or features that are particularly
interesting for follow-up experiments.
An exception would be the biomarkers discovery studies.
• This does not reduce the importance of experimental design

9. 2
Two color microarrays (cDNA)
• Usually probes are long (20nt)
• Probe is fixed to a glass
• Labeling is with two fluorocrom (Cy3/Cy5).
• Direct comparison of the two samples due
to they are hybridized in the same array.
• Each gene appear few times in the array
• Long probes facilitate crosshybridization
• Not very good reproducibility.
Different types of arrays. Manufactoring. DNA/RNA

10. 2
One color microarrays
• Short probes (20-25 nt)
• Target is labeled with only one fluorocrom
• Only one sample is hybridized in each array.
• Each gene is represented by a lot of probes
in the array
Different types of arrays. Manufactoring. DNA/RNA

11. 2 Different types of arrays. Manufactoring. DNA/RNA
• DNA Polymorphism (GWAS)
• Transcription Factors
• Resequencing
• Cytogenetics
• Expression
• Alternative splicing
• microRNA
DNA RNA

12. 2 Different types of Affymetrix arrays.
3’5’
3’ IVT Arrays
• Biased measurement of the gene expression
• Array more used in the literature. A lot of species present.
Only genes with polyA tail and good 3’ site will
be amplified and will have the chance of
hybridize correctly.

13. 2 Different types of Affymetrix arrays.
3’5’
Gene Arrays
Exon Arrays
Gene/Exon Arrays
• Gene arrays are the most used (good quality and price ratio)
• Gene arrays 2.0 more updated library and also includes lncRNAs

14. 2 Different types of expression arrays.
•153 organisms in the array (human, mouse, rat, canine, ….)
•100% miRBase v17
•2.216 snoRNAs and scaRNAs (human small nuclear RNAs)
•Low inputs amounts (130 ng total RNA)
•2.999 probe sets unique to pre-miRNA hairpins
•Able to differentiate pre and mature miRNAs
•Useful for FFPE samples
miRNA

15. 2 Different types of expression arrays.
HTA array

16. Affymetrix microarrays manufacture.3
Photolitografy

17. Affymetrix microarrays manufacture.3

18. 5 Microarray experiment workflow

19. 5 Microarray experiment workflow

20. 5 Microarray experiment workflow

21. 6 Quality Controls

22. 6 Quality Controls

23. 6 Quality Controls
Length of amplified cRNA

24. 6 Quality Controls
Length of fragmented cRNA

25. Bioinformàtica per a la
Recerca Biomèdica
Ricardo Gonzalo Sanz
ricardo.gonzalo@vhir.org
20/05/14
Hospital Universitari Vall d’Hebron
Institut de Recerca - VHIR
Institut d’Investigació Sanitària de l’Instituto de Salud Carlos III (ISCIII)
Basic aspects of Microarray
Data Analysis

26. Filtering
2
3
4
5
6
Statistical inference of diferential expression
Clustering
Normalization
1 Introduction. Experimental design
Quality control
7
8
Annotation
Biological interpretation

27. 1 Introduction. Experimental design

28. 1 Introduction. Experimental design

29. 1 Introduction. Experimental design

30. 1 Introduction. Experimental design

31. 1 Introduction. Experimental design

32. 1 Introduction. Experimental design
Microarrays Analysis
Workflow

33. 2 Quality Control

34. 2 Quality Control
Was the experiment a success???
• Microarray experiments generate huge quantitites of data
• Standard statistical approach use plots to check the quality
 show all data together
 highlight structures
 may help to detect problems (“unusual patterns”)
It is hard to decide if things “seem to be
all right” just by looking at the numbers.

35. 2 Quality Control
Diagnostics plots for microarrays:
• Microarray data usually considered at two levels
1. Low level. Data directly coming from the scanner
2. High level. Processed from low level data. Expression values,
normalized or not.
• Some plots are specific for some type of arrays or for some level

36. 2 Quality Control
Diagnostics plots for microarrays:
1. Low level:
 Layout image
 Degradation plots (only in 3’IVT)
 Histogram/density plots
 PCA, Boxplot
2. High level:
 MA plots
 Model based plots (NUSE,RLE,)
 PCA, Boxplot

37. 2 Quality Control
Diganostics plots for microarrays. Low level. Layout image.

38. 2 Quality Control
Diagnostic plots for microarrays. Low level. RNA degradation plot (3’IVT arrays)

39. 2 Quality Control
Diagnostics plots for microarrays. Low level. Histogram/density Plot

40. 2 Quality Control
Diagnostics plots for microarrays. Low level. Boxplot

41. 2 Quality Control

42. 2 Quality Control
Diagnostics plots for microarrays. Low level. PCA

43. 2 Quality Control
Diagnostics plots for microarrays. Low level. PCA

44. 2 Quality Control

45. 2 Quality Control
Diagnostics plots for microarrays. High level. RLE

46. 2 Quality Control

47. 2 Quality Control
Diagnostics plots for microarrays. High level. NUSE

48. 2 Quality Control
Diagnostics plots for microarrays. High level. MA plots
• MA plots allow pair wise comparison of log-intensity of each array to a
reference array and identification of intensity-dependent biases.
• The Y axis of the plot contains the log-ratio intentsity of one array to the
reference median array, which is called “M” while the X axis contains the
average log-intensity of both arrays – called “A”.
• The probe levels are not likely to differ a lot so we expect a MA plot centered
on the Y=0 axis from low to high intensities.

49. 2 Quality Control
Diagnostics plots for microarrays. High level. MA plots

50. 2 Quality Control

51. 3 Normalization
The goal of normalization is to adjust for the effects that are due to variations in the
technology rather than the biology.

52. 3 Normalization

53. 3 Normalization

54. 3 Normalization

55. 4 Filtering
• In a microarray experiment only a few hundreds/thousand of genes change their
expression due to the different conditions
•Researcher is interested in keeping the number of tests/genes as low as possible
while keeping the interesting genes in the selected subset.
•If the truly diferentially expressed genes are over-represented among those
selectec in the filtering step, the FDR associated with a certain threshold of the
statistic test will be lowered due to the filtering.
Genes that do not change introduce
noise, therefore is better not to be
present when the statistical analysis is
done

56. 4 Filtering
Exists different types of filtering:
• Annotation features (specific):
 Specific gene features (i.e. GO term, presence of transcriptional regulative
elements in promoters, etc.)
Data derived from IPA
• Signal features (non specific)
 % intensities greater of a user defined value
 Interquantile range (IQR) greater of a defined value

57. 4 Filtering
Signal filtering: This technique has as its premise the removal of genes that are
deemed to be not expressed or unchanged according to some specific criterion that
is under the control of the user.

59. 5 Statistical inference of diferential expression
• Indirect comparisons: 2 groups, unpaired
• Direct comparsions: 2 groups. paired

60. 5 Statistical inference of diferential expression
Limma package (Gordon Smith)

61. 5 Statistical inference of diferential expression

62. 5 Statistical inference of diferential expression

63. 5 Statistical inference of diferential expression

64. 5 Statistical inference of diferential expression

65. 6 Clustering
Types:
 Supervised clustering try to find the best partition for data that belong to a
know set o classes
 Unsupervised clustering try to define the number and the size of the classes
in which the transcription profiles can be fitted in.

66. 6 Clustering

67. 6 Clustering
Hierarchical Clustering (HCL)
• HCL is an agglomerative /divise clustering method.
• The iterative process continues until all groups are
connected in a hierarchical tree.
• Samples more similar between them are closed.

68. 6 Clustering

69. 7 Annotation

70. 8 Biological interpretation
Gene Ontology

71. 8 Biological interpretation