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Lecture18 - Gene Expression and Regulation Lecture 18 Data...

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Lecture 18: Data Analysis and Thresholds § Data analysis and threshold methods • Fold change • Fluorescence intensity thresholds • t-test • Z-score § DNA microarray and sequencing experimental design considerations and applications Gene Expression and Regulation § Genes are differentially expressed in... • Different cell types (e.g. muscle cells, fibroblasts) • Environmental conditions (e.g. heat shock, nutrient deprivation) • Developmental phases (e.g. embryonic day 10 [e10]) • Cell-cycle stages (e.g. G1 phase) • Disease states (e.g. tumor cells, virus-infected cells) Identifying Differentially Expressed Genes 258 102 p53 MCF-7 cells (Intensity) Normal cells (Intensity) Gene § Raw microarray data: 17 275 18 120 p53 Background Cy5 intensity Cy5 intensity Background Cy3 intensity Cy3 intensity Gene MCF-7 cells Normal cells § Background-subtracted microarray data: § Normalized microarray data: (k = MCF-7/normal = 0.95) 271.6 102 p53 MCF-7 cells (Intensity) Normal cells (Intensity) Gene • Is p53 differentially expressed in MCF-7 breast cancer cells? Importance of Experimental Replicates § Repeat microarray experiment and get following (normalized) data: #2 #1 Experiment 271.6 102 p53 193.2 158 p53 MCF-7 cells (Intensity) Normal cells (Intensity) Gene § Average fold change = (1/2)(271.6/102 + 193.2/158) = 1.94 § Need to perform multiple experimental replicates to have confidence in microarray data § Types of replicates: (1) Biological replicates: prepare independent cell/RNA preps for multiple microarray experiments (2) Microarray replicates: hybridize/scan same RNA prep on multiple microarrays § Need to perform at least 2 biological replicates (ideally 3 to 4 replicates) § For Statistical analysis typically need to perform at least 3 replicates
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§ Data analysis and threshold methods • Fold change • Fluorescence intensity thresholds • t-test • Z-score Identifying Differentially Expressed Genes Fold Change Method § Differentially expressed genes (DEG) are identified using a fold-change criteria or threshold • For example, genes whose mRNA levels increase or decrease more than 2-fold are considered differentially expressed § Calculate average ratio (R/G) across replicate experiments and compare to fold change criteria. • A 2-fold change criteria is commonly used § Averaging fold changes: Arithmetic avg. mean Geometric avg: log(mean) § Geometric average (mean) gives more accurate average of ratios = R 1 / G 1 + R 2 / G 2 + R 3 / G 3 ( ) 3 = log( R 1 / G 1 ) + log( R 2 / G 2 ) + log( R 3 / G 3 ) ( ) 3 Fold Change Method § Advantages : • Simply to implement and easy to calculate • Straightforward biological interpretation • Can be used to analyze data with few replicates (e.g. 2 replicates) § Disadvantages : • For poorly expressed genes, small changes in intensity can lead to large calculated fold changes -- Hence, more false positives among genes with low expression levels • Doesn’t account for noise in array data (e.g. standard deviation of intensities) -- Hence, data outliers can have large affect on average fold change • Not a statistically based method
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