Supervised analysis of gene expression data

Transcription

1 Supervised analysis of gene expression data Bing Zhang Department of Biomedical Informatics Vanderbilt University

2 Gene expression Gene expression is the process by which information from a gene is used in the synthesis of a functional gene product. For a specific cell at a specific time, only a subset of the genes coded in the genome are expressed. Transcriptional control is critical in gene expression regulation. Measure of mrna expression level can Provide a good indicator of corresponding protein expression level Provide insight on the mechanisms of transcriptional regulation graph courtesy of Wikipedia

3 Candidate gene approach vs high-throughput approach Chalcone synthase Protein kinase Actin Northern 0 10m 30m 1h 3h 6h 24h Microarray 10m 30m 1h 3h 6h 24h Advantages of high-throughput technologies High-throughput Exploratory analysis Relationship between genes or between samples Challenges in high-throughput technologies Cost Data analysis

4 High-throughput transcriptome profiling approaches Transcriptome: the set of all messenger RNA (mrna) molecules, or "transcripts, produced in one or a population of cells. Hybridization based approaches: incubating fluorescently labeled cdna with microarrays. Hybridization signal is measured. cdna microarray (printed arrays) High density olio arrays (synthesized arrays) Sequencing based approaches: directly determine the cdna sequence. Count is measured. Sanger sequencing of cdna or EST libraries Serial Analysis of Gene Expression (SAGE) Massively Parallel Signature Sequencing (MPSS) RNA-Seq

5 Array preparation Array preparation PerfectPerfect match match Probe Probe set set Mismatch Mismatch Insert amplification Insert amplification by PCR by PCR Vector-specific Vector-specific primersprimers Gene-specific Gene-specific primersprimers In situ In synthesis situ synthesis by photolithography by photolithography Microarray: two-color vs single-color PrintingPrinting Coupling Coupling Denaturing Denaturing Array 2 Array 2 Array 1 Array 1 Ratio array Ratio1/array array 1/array 2 2 Target preparation Target preparation Ratio Cy5/Cy3 Ratio Cy5/Cy3 Staining Staining hybridization hybridization Hybridization Hybridization mixingmixing Cy3 Cy3 orcy3 Cy5or Cy5 labelled labelled cdna cdna Cy5 Cy3 TTTTTTTT TTTTTTTT TTTTTTTT TTTTTTTT TTTTTTTT TTTTTTTT TTTTTTTT TTTTTTTT TTTTTTTT TTTTTTTT TTTTTTTT TTTTTTTT TTTTTTTT TTTTTTTT TTTTTTTT TTTTTTTT First-strand First-strand cdna cdna synthesis synthesis Total RNA Total RNA Cells/tissue Cells/tissue two-color arrays Biotin-labelled Biotin-labelled crna crna Cy5 In vitrointranscription vitro transcription TTTTTTTTTTTTTTTT T7 T7 Double-stranded Double-stranded cdna cdna TTTTTTTTTTTTTTTT T7 T7 cdna synthesis cdna synthesis PolyA+PolyA+ RNA RNA Cells/tissue Cells/tissue single-color arrays FigureFigure 1 Schematic 1 Schematic overview overview of probe of array probeand array target and preparation target preparation for spotted for spotted intensities intensities and ratios and of ratios mrna of abundance mrna abundance for the for genes the represented genes represented on the on array. the array. Schulze andb,downward, Nature Cell Biol, 3:E190, 2001 cdna microarrays cdna microarrays and high-density and high-density oligonucleotide oligonucleotide microarrays. microarrays. a, cdna a,microarcdna microarhigh-density b, High-density oligonucleotide oligonucleotide microarrays. microarrays. Array preparation: Array preparation: sequences sequences of of rays. Array rays.preparation: Array preparation: inserts inserts from cdna fromcollections cdna collections or libraries or libraries (such as (such IMAGE as IMAGE short oligonucleotides short oligonucleotides (typically (typically 25mers) 25mers) are chosen are chosen from the from mrna the reference mrna reference Applied Bioinformatics, Spring 2011 libraries) libraries) are amplified are amplified using either using vector-specific either vector-specific or gene-specific or gene-specific primers. primers. PCR PCR sequence sequence of eachofgene, each often gene,representing often representing the most theunique most unique part of part the transcript of the transcript in in products products are printed are printed at specified at specified sites onsites glass onslides glass using slideshigh-precision using high-precision arrayingarrayingthe 5 -untranslated the 5 -untranslated region. region. Light-directed, Light-directed, in situ oligonucleotide in situ oligonucleotide synthesis synthesis is usedistoused to robots.robots. ThroughThrough the usethe of chemical use of chemical linkers,linkers, selective selective covalent covalent attachment attachment of the of the generate generate high-density high-density probe arrays probe arrays containing containing over 300,000 over 300,000 individual individual elements. elements. + + RNA from RNAdifferent from different tissuestissues or cell populations or cell populations is usedistoused to coding coding strand strand to the glass to thesurface glass surface can be can achieved. be achieved. Target preparation: Target preparation: RNA from RNA from Target preparation: Target preparation: polya polya

6 Overall workflow of a microarray study Biological question Experiment design Microarray experiment Image analysis Pre-processing Data Analysis Experimental verification Hypothesis

7 Data matrix Samples Genes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

8 Three major goals of gene expression studies Class comparison (supervised analysis) e.g. disease biomarker discovery Differential expression analysis Input: gene expression data, class label of the samples Output: differentially expressed genes Class detection (unsupervised analysis) e.g. patient subgroup detection Clustering analysis Input: gene expression data Output: groups of similar samples or genes Class prediction (supervised learning) e.g. disease diagnosis and prognosis Machine learning techniques Input: gene expression data, class label of the samples (training data) Output: prediction model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

9 Data preprocessing I: missing value imputation Replace with zeros Replace all missing values with 0 Replace with row averages Replace missing values with mean of available values in each row (gene) KNN imputation Estimate missing values via the K-nearest neighbors analysis

10 Data preprocessing II: normalization To make arrays comparable Adjust the arrays using some control or housekeeping genes that you would expect to have the same intensity level across all of the samples Adjust using spike control Multiply each array by a constant to make the mean (median) intensity the same for each individual array (Global normalization) Match the percentiles of each array (Quantile normalization) No normalization Global normalization Quantile normalization

11 Data preprocessing III: transformation To make the data more closely meet the assumptions of a statistical inference procedure log transformation to improve normality Histogram of a Histogram of log(a) Frequency Frequency a log(a)

12 Differential expression Samples Genes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ase Control

13 Fold change n-fold change Arbitrarily selected fold change cut-offs Usually 2 fold Pros Intuitive Simple and rapid Cons Statistically inefficient Magnitude does not necessarily indicate importance

14 Statistical analysis: hypothesis testing Samples Genes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ase A statistical hypothesis is an assumption about a population parameter, e.g. group mean. Control Null hypothesis Alternative hypothesis H 0 : µ 1 = µ 2 H 1 : µ 1 " µ 2

15 Statistical analysis: comparing means of two groups Parametric method Student s t-test Assumes normal distribution of the data Non-parametric method Mann-Whitney U test GeneX t-test: p=0.06; U test: p=0.1 GeneX t-test: p=0.32; U test: p=0.1 Does not rely on data belonging to any particular distribution Based on ranks of observations Student s t-test vs Mann-Whitney U test Robustness: U-test is more robust to outliers Efficiency: When normality holds, the efficiency of the U-test is about 0.95 when compared to the t-test. For distributions sufficiently far from normal and for sufficiently large sample sizes, the U-test can be considerably more efficient than the t-test.

16 Statistical tests for different types of comparisons DATA Continuous/ normal Rank Nominal Compare two unpaired groups Unpaired t- test Mann- Whitney test Fisher s exact test or chi-square test G O A L Compare two paired groups Compare three or more groups Association to quantitative phenotypes Paired t-test One-way ANOVA Pearson s correlation Wilcoxon test Kruskal- Wallis test Spearman s correlation McNemar s test Chi-square test Contingency coefficients

17 Correction for multiple testing: why? In an experiment with a 10,000-gene array in which the significance level p is set at 0.05, 10,000 x 0.05 = 500 genes would be inferred as significant even though none is differentially expressed The probability of drawing the wrong conclusion in at least one of the n different test is P(wrong) =1" (1 " # s ) n = # g " s Where is the significance level at single gene level, and is the global significance level. " g Each row is a test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n " s " g

18 Correction for multiple testing: how? Control the family-wise error rate (FWER), the probability that there is a single type I error in the entire set (family) of hypotheses tested. e.g. Standard Bonferroni Correction: uncorrected p value x no. of genes tested Control the false discovery rate (FDR), the expected proportion of false positives among the number of rejected hypotheses. e.g. Benjamini and Hochberg correction. Ranking all genes according to their p value Picking a desired FDR level, q (e.g. 5%) p " i m q Starting from the top of the list, accept all genes with, where i is the number of genes accepted so far, and m is the total number of genes tested. p Bonferroni Rank (i) q (i/m)*q significant?

19 Resources Data source Gene Expression Omnibus (GEO): ArrayExpress: Microarray data analysis tools Bioconductor: Expression profiler:

20 Summary Three major goals of gene expression studies Class comparison Class detection Class prediction Gene expression data pre-processing steps Missing data imputation Normalization Transformation Statistical tests for two group comparative studies Student s t-test Mann-Whitney U test Multiple-test adjustment Control the family-wise error rate (FWER) Control the false discovery rate (FDR)

21 Exercise Data set: james_west_2005_hne_6h_60vs0.txt (or james_west_2005_hne_6h_60vs0_head100.txt) probe sets (or the top 100 probe sets) Two groups (HNE0 and HNE60, three replicates in each group) No missing value; Already normalized; Already log transformed Use t-test in expression profiler ( or excel to identify genes that are differentially expressed between the two groups. Apply multiple test adjustment on the raw p-values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