Session ii g1 lab genomics and gene expression mmc-corr

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Session ii g1 lab genomics and gene expression mmc-corr

  1. 1. Gene Expression Data Analysis (Microarray, NGS & qRT-PCR) Theme: Transcriptional Program in the Response of Human Fibroblasts to Serum. Lab #2 Etienne Z. Gnimpieba BRIN WS 2013 Mount Marty College – June 24th 2013 Etienne.gnimpieba@usd.edu
  2. 2. Resolution Process Context Specification & Aims Lab #2 Statement of problem / Case study: The temporal program of gene expression during a model physiological response of human cells, the response of fibroblasts to serum, was explored with a complementary DNA microarray representing about 8600 different human genes. Genes could be clustered into groups on the basis of their temporal patterns of expression in this program. Many features of the transcriptional program appeared to be related to the physiology of wound repair, suggesting that fibroblasts play a larger and richer role in this complex multicellular response than had previously been appreciated. Gene Expression Data Analysis 16 Vishwanath R. Iyer, Scince, 1999 Aim: The purpose of this lab is to initiate on gene expression data analysis process. We simulated the application on “Transcriptional Program in the Response of Human Fibroblasts to Serum” . Now we can understand how a researcher can come to identify a significant expressed gene from microarray dataset. T1. Excel used in Genomics Objective: Use of basic excel functionalities to solve some gene expression data analysis needs Acquired skills - Gene expression data overview - Excel Used for genomics - Microarray data analysis using GEPAS - ArrayTrack for WorkBench driven gene expression analysis T1.1. Import your dataset in Excel T1.2. Pre-treat your dataset obtained a centered (mean=0) and scaled (stdv=1) dataset 2 Slide Scanning: : T3. Gene Expression and Analysis for APT13A2 Objective: Use of Gene Expression and Analysis Tools to discover more information about the gene APT13A2. T3.1. Gene Expression Profile Quering using Gene Atlas T3.2. Experimental Condition Driven Dataset Extraction in Array Express T3.3. Gene Expression data quick analysis using GEO2R T2. Workbench driven gene expression analysis (ArrayTrack) Objective: Workbench driven gene expression analysis using ArrayTrack T2.1. ArrayTrack overview T2.2. Descriptive statistic: Data Exploring in ArrayTrack T2.3. Accessing gene expression profiles using BarChart T2.4. Using SAM through ArrayTrack
  3. 3. T1. Excel used in Genomics o Open Excel and go to the “Data” tab. Then click on “From Text” and select the text document with that contains the data you are studying. In this example select “fibroblasts_ori – Excel1”. o Click “Next”, “Next”, “Finish”, “ok” T1.1. Import your Data in Excel T1.2. Pre-treat your dataset to obtained a centered (mean=0) and scaling (stdv =1) dataset Centering data: o Go to A520 and type in “Average” o Select B520 cell, then go to the “Formulas” tab and click on “More Formulas”>”Statistical”>”Average”. Then enter the cells you want to average. (B2:B518) o Then go to cell B520 and hover over the lower right corner until a black plus sign appears. Then click and drag across so that the averages for the rest of the column will appear (This is a shortcut) Check to make sure the shortcut worked* o Be careful, for missing values (empty cells), replace empty contents by the NULL or NA string, in order to avoid introducing a zero value in Excel calculation in this cell. o Verify the value you get for the averages are zero. If it is not zero: o Select cell U2 and enter the equation: (=) The first cell (B2) (-) the average cell (B520) with a $ in between so it will not change when apply equation to the other cells (B$520) so the equation should look like (=B2-B$520) o Then apply to other cells in that column. (check to make sure it worked) o Then get the average of the new columns made and verify that they equal zero (or really close such as 4.29057E-16) Etienne Z. Gnimpieba BRIN WS 2013 Mount Marty College – June 24th 2013 Scaling data: o For each column (corresponding to one DNA microarray experiment), calculate the standard deviation value o Divide each experiment value by the corresponding standard deviation value o Once the calculation is over, verify that the standard deviation value of the column is equal to one, Objective: Use of basic excel functionalities to solve some gene expression data analysis needs Gene Expression Data Analysis
  4. 4. • Frouin, V. & Gidrol, X. (2005) • CBB group (Berlin) Using ArrayTrackT2. ArrayTrack Etienne Z. Gnimpieba BRIN WS 2013 Mount Marty College – June 24th 2013 Objective: Workbench driven gene expression analysis using ArrayTrack T2.1. ArrayTrack overview o Microarray Database o Biological libraries (gene, pathway, protein, …) o Microarray and Systems Biology Tools (quality, normalisation, analysis, visualization) T2.2. Descriptive statistic: gene expression data Exploring in ArrayTrack o Principal Component Analysis (PCA) o Hierarchical Cluster Analysis (HCA) o Correlation Matrix o Scatter plot o K-means T2.3. Accessing gene expression profiles using BarChart and Venn Diagram o Select 4 gene List o Right click to view the venn diagram o Pathway level browsing of venn diagram sub-dataset o Gene ontology level browsing of venn diagram sub-dataset o Open BarChart from gene List. o Query and sort data table o Grouping data bars o Standard deviation view T2.4. Using SAM through ArrayTrack o Two class paired o Two class unpaired o One class o Multi class o Survival o One class timecourse o Two class unpaired timecourse o Two class paired timecourse Gene Expression Data Analysis
  5. 5. Gene Expression Data Analysis Gene Expression and Analysis for APT13A2T3. Gene Expression and Analaysis On Gene Atlas website: http://www.ebi.ac.uk/gxa/ o Filter search by selecting “homo sapiens” for “organism”, and “H2O2” and “starvation” for “condition” o Then click on “Search Atlas” o Here you can click on the bottom right corner of the result that came up for more details. T3.1. Gene Expression Profile Quering using Gene Atlas Etienne Z. Gnimpieba BRIN WS 2013 T3.2. Experimental Condition Driven Dataset Extraction in Array Express On Array Express website: http://www.ebi.ac.uk/arrayexpress/ o Search “H2O2” o Filter by selecting “human” for “organism”, and “Array assay” instead of “all technologies”. Then click “Filter o Find and click on the experiment with the accession number E-GEOD-26143 T3.3. Gene Expression data quick analysis using GEO2R o Scroll down the webpage to the title “Link” and click on the link “GEO-GSE26143”. On this page you can see the details of the experiment. o Next scroll down the webpage and click on the link “Analyze with GEOR2” o Then group the different parts of the experiment. In this example we will group it by going to the end of the Titles and click on the “Treatment Group (Ch2)” so that is will go in order of that title. o Then we will separate in into 3 groups; Click and drag for group 1 (first 3), click on “define group” above the table, and name “Ble”, hit enter and click on “Ble” ; do the same for 6 and name “Gamma”, and the next 3 and name “Control”. o Click on the “Visualize distribution” tab at the bottom of the page and click “View” to see the result of the experiment in a box plot. o Click on the “Profile Graph” tab and search for the gene “5783” o Click on “GEOR2” Tab and click on “Top 250” genes that showed the most differentially expressed genes. o Here you can search for the gene “5783” again, as well as save the results. Objective: Use of Gene Expression and Analysis Tools to discover more information about the gene APT13A2.

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