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Microarray Data Analysis Using BASE You’ve got data! What was I asking? – remember your experimental design How do I analyze the data? – How do I find interesting stuff? – learn some analysis tools – How do I trust the results? – statistics is key What was I asking? Typically: “which genes changed expression levels when I did ____” Common ____: – Binary conditions: knock out, treatment, etc – Continuous scales: time courses, levels of treatment, etc – Unordered discrete scales: multiple types of treatment or mutations This tutorial’s focus: binary experiments How do I analyze the data? BASE – BioArray Software Environment – Data storage and distribution – Simple filtering, normalization, averaging, and statistics – Export/Download results to other tools MS Excel TIGR Multi Experiment Viewer (TMEV) This tutorial’s focus: using BASE Today’s Presentation Demonstrate the most basic analysis techniques Using our most frequently used software (BASE) For the most common kind of experiments Work Flow RNA QC & label Labeled cDNA hybridize Slides Researcher scan, segment analysis BASE upload Images & data files The Most Common experiment Two-sample comparison w/N replicates – KO vs. WT – Treated vs. untreated – Diseased vs. normal – Etc Question of interest: which genes are (most) differentially expressed? Experimental Design – naïve A From Gary Churchill, Jackson Labs B Experimental Design – tech repl A From Gary Churchill, Jackson Labs B Experimental Design – bio repl Treatment Biological Replicate Technical Replicate Dye Array From Gary Churchill, Jackson Labs A A B B The Most Common Analysis Filter out bad spots Adjust low intensities Normalize – correct for non-linearities and dye inconsistencies Filter out dim spots Calculate average fold ratios and pvalues per gene Rank, sort, filter, squint, sift data Export to other software BASE @ MGH BASE is a microarray data storage and analysis package BASE resides on our web server – Data is stored at our facility – Computation is performed on our machines All you need is a web browser – https://base.mgh.harvard.edu/ – A Microarray Core technician will provide you with a username, password, and experiment name BASE – Login page BASE – Login page BASE – Login page BASE – Login page BASE – Logged in BASE – Logged in BASE – Sidebar Reporters BASE – Sidebar Reporters BASE – Sidebar Array LIMS BASE – Sidebar Array LIMS BASE – Sidebar Biomaterials BASE – Sidebar Biomaterials BASE – Sidebar Hybridizations BASE – Sidebar Hybridizations BASE – Sidebar Analyze Data BASE – Sidebar Analyze Data BASE – Sidebar Users BASE – Sidebar Users BASE – My Account Change your password and access defaults BASE – My Account Change your password and access defaults BASE – My Account Change your password and access defaults BASE – My Account Change your password and access defaults Find your experiment Find your experiment Find your experiment Find your experiment Experiment view: Four Tabs Experiment view: Four Tabs Experiment view: Four Tabs Experiment view: Four Tabs Experiment view: Four Tabs Experiment view: Four Tabs Experiment view: Four Tabs Experiment view: Four Tabs Group slide data together Group slide data together Select the slides that measure the same thing. Later in analysis, they will be averaged together. In this experiment, all ten slides are replicates, so there is only one grouping. Group slide data together Select the slides that measure the same thing. Later in analysis, they will be averaged together. In this experiment, all ten slides are replicates, so there is only one grouping. Group slide data together Select the slides that measure the same thing. Later in analysis, they will be averaged together. In this experiment, all ten slides are replicates, so there is only one grouping. Group slide data together Group slide data together Give your data set a descriptive name to distinguish it from other slide groupings. In this Myd88 knockout experiment, there is only one grouping, so a generic name is fine. Group slide data together Give your data set a descriptive name to distinguish it from other slide groupings. In this Myd88 knockout experiment, there is only one grouping, so a generic name is fine. Group slide data together Give your data set a descriptive name to distinguish it from other slide groupings. In this Myd88 knockout experiment, there is only one grouping, so a generic name is fine. Analysis: Begin Analysis: Begin Analysis: Begin Analysis: Begin Analysis: Filter Setup “Bad” spots are marked with a negative Flag value. Analysis: Filter Setup “Bad” spots are marked with a negative Flag value. Analysis: Filter Setup “Bad” spots are marked with a negative Flag value. Analysis: Filter Setup “Bad” spots are marked with a negative Flag value. Analysis: Filter Setup “Bad” spots are marked with a negative Flag value. Analysis: Filter Setup “Bad” spots are marked with a negative Flag value. Analysis: Filter Setup “Bad” spots are marked with a negative Flag value. Analysis: Filter Setup “Bad” spots are marked with a negative Flag value. Analysis: Filter Setup “Bad” spots are marked with a negative Flag value. Oligos are annotated with species codes, but control spots are not. Set species to your two-letter code of choice (Mm, Hs, Dr, Pa, etc) Analysis: Filter Setup “Bad” spots are marked with a negative Flag value. Oligos are annotated with species codes, but control spots are not. Set species to your two-letter code of choice (Mm, Hs, Dr, Pa, etc) Analysis: Filter Setup “Bad” spots are marked with a negative Flag value. Oligos are annotated with species codes, but control spots are not. Set species to your two-letter code of choice (Mm, Hs, Dr, Pa, etc) Analysis: Filter Setup “Bad” spots are marked with a negative Flag value. Oligos are annotated with species codes, but control spots are not. Set species to your two-letter code of choice (Mm, Hs, Dr, Pa, etc) Analysis: Filter Setup “Bad” spots are marked with a negative Flag value. Oligos are annotated with species codes, but control spots are not. Set species to your two-letter code of choice (Mm, Hs, Dr, Pa, etc) Analysis: Filter Setup “Bad” spots are marked with a negative Flag value. Oligos are annotated with species codes, but control spots are not. Set species to your two-letter code of choice (Mm, Hs, Dr, Pa, etc) Analysis: Filter Setup “Bad” spots are marked with a negative Flag value. Oligos are annotated with species codes, but control spots are not. Set species to your two-letter code of choice (Mm, Hs, Dr, Pa, etc) Analysis: Filter Setup “Bad” spots are marked with a negative Flag value. Oligos are annotated with species codes, but control spots are not. Set species to your two-letter code of choice (Mm, Hs, Dr, Pa, etc) Analysis: Filter Setup Naming the filter and the child data set are essential to reducing confusion later. Analysis: Filter Setup Naming the filter and the child data set are essential to reducing confusion later. Analysis: Filter Setup Naming the filter and the child data set are essential to reducing confusion later. Analysis: Filter Run Analysis: Quality Data Analysis: Quality Data Analysis: Unfiltered Data Analysis: Filter Parameters Analysis: Limit-Int Setup Analysis: Limit-Int Setup Analysis: Limit-Int Setup Analysis: Limit-Int Setup Analysis: Limit-Int Setup Analysis: Limit-Int Setup Analysis: Check job status Analysis: Check job status Analysis: Check job status Analysis: Check job status Analysis: Check job status “All done” indicates the job is complete. Analysis: Check job status “All done” indicates the job is complete. Analysis: Limit-Int Output Analysis: Limit-Int Output Analysis: Limit-Int Output Analysis: Limit-Int Output Analysis: Limit-Int Output Analysis: Limit-Int Output Analysis: Change data set name Analysis: Change data set name Analysis: Change data set name Change the name of this set to “Intensity limited Data” Analysis: Change data set name Analysis: Change data set name Analysis: Change data set name Analysis: Change data set name Analysis: LOWESS Setup Analysis: LOWESS Setup Analysis: LOWESS Setup Analysis: LOWESS Setup Analysis: LOWESS Setup Analysis: LOWESS Setup Analysis: Check job status Analysis: Check job status Analysis: LOWESS Output Analysis: LOWESS Output Analysis: LOWESS Output Change the name of this set to “Normalized Data” using the same steps as before. Analysis: Change data set name Change the name of this set to “Normalized Data” using the same steps as before. Analysis: Change data set name Change the name of this set to “Normalized Data” using the same steps as before. Analysis: Filter Setup Set up the filter as indicated, hit Add/Update on the Gene filter, then hit Accept and select the resulting data set. Analysis: Useful Data Analysis: Useful Data MA Plots: Raw Myd88 Data MA Plots: Raw Myd88 Data MA Plots: Raw Myd88 Data MA Plots: Raw Myd88 Data MA Plots: Quality Data MA Plots: Quality Data MA Plots: Quality Data MA Plots: Quality Data MA Plots: Quality Data MA Plots: Quality Data MA Plots: Int-limited Data MA Plots: Int-limited Data MA Plots: Int-limited Data MA Plots: Int-limited Data MA Plots: Int-limited Data MA Plots: Int-limited Data MA Plots: Normalized Data MA Plots: Normalized Data MA Plots: Normalized Data MA Plots: Normalized Data MA Plots: Normalized Data MA Plots: Normalized Data MA Plots: Norm. Corr. Factor MA Plots: Norm. Corr. Factor MA Plots: Useful Data MA Plots: Useful Data MA Plots: Useful Data MA Plots: Useful Data MA Plots: Useful Data MA Plots: Useful Data Analysis: Useful Data Analysis: Useful Data Analysis: Fold Ratio Setup Analysis: Fold Ratio Setup Analysis: Fold Ratio Setup Analysis: Fold Ratio Setup Analysis: Fold Ratio Output Analysis: Fold Ratio Output Analysis: Fold Ratio Output Analysis: Fold Ratio Output Analysis: Fold Ratio Output Analysis: Fold Ratio Output Analysis: Fold Ratio Output Analysis: Fold Ratio Output Analysis: Change list name Analysis: Change list name Analysis: Change list name Change the name of this list as indicated here. Analysis: Change list name Change the name of this list as indicated here. Analysis: Change list name Analysis: Change list name Analysis: Fold Ratio Graphs Analysis: Fold Ratio Graphs Analysis: Fold Ratio Graphs Analysis: Fold Ratio Graphs Analysis: Fold Ratio Graphs Analysis: Fold Ratio Graphs Analysis: t-test Setup Analysis: t-test Setup Analysis: t-test Setup Analysis: t-test Setup Analysis: t-test Output Analysis: t-test Output Analysis: t-test Output Analysis: t-test Output Analysis: t-test Output Analysis: t-test Output Analysis: Change list name Change the name of this set to “myd88 pvalue” using the same steps as before. Analysis: Change list name Change the name of this set to “myd88 pvalue” using the same steps as before. Analysis: Change list name Change the name of this set to “myd88 pvalue” using the same steps as before. Analysis: t-test Graphs Analysis: t-test Graphs Analysis: t-test Graphs Analysis: t-test Graphs Analysis: t-test Graphs Analysis: t-test Graphs Analysis: Experiment Explorer Analysis: Experiment Explorer EExplore: Single Gene View EExplore: Single Gene View EExplore: Single Gene View EExplore: Single Gene View EExplore: Single Gene View EExplore: Single Gene View EExplore: Gene List View EExplore: Gene List View EExplore: Gene List View EExplore: Gene List View Fill out the table as indicated, then hit Add/Update. EExplore: Gene List View EExplore: Gene List View EExplore: Gene List View EExplore: Gene List View EExplore: Gene List View EExplore: Gene List View EExplore: Gene List View EExplore: Gene List View EExplore: NCBI Links EExplore: Gene List View This additional row will restrict hits to P values of 5% or less. EExplore: Gene List View This additional row will restrict hits to P values of 5% or less. EExplore: Single Gene View EExplore: Single Gene View EExplore: Single Gene View EExplore: Single Gene View EExplore: Single Gene View EExplore: Single Gene View EExplore: Gene List View EExplore: Gene List View Open MS Excel and tell it to open the file you downloaded (typically called base.tsv). EExplore: Gene List View Open MS Excel and tell it to open the file you downloaded (typically called base.tsv). Have Fun! The rest of the analysis is largely driven by your biological understanding of the genes indicated in these lists. We cannot help much in the interpretation of this data. Don’t forget to go back to the raw data sets and repeat this entire analysis for any other slide groupings. Acknowledgements MGH Lipid Metabolism Unit Mason Freeman Harry Bjorkbacka MGH Molecular Biology Bioinformatics Group Chuck Cooper Xiaowei Wang Harvard School of Public Health Biostatistics Xiaoman Li MGH Microarray Core Glenn Short Jocelyn Burke Najib El Messadi Jason Frietas Zhiyong Ren LUND (Sweden) Dept. Theoretical Physics & Dept. Oncology Carl Troein Lao H. Saal Johan Vallon-Christersson Sofia Gruvberger Åke Borg Carsten Peterson