Leanne Kuwahara-Week 4

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  • Filename: BIOL388_S19_microarray-data_dGLN3_LK
    • Strain: Δgln3
    • Time points and Replicates:
      • t=15min: 5 replicates (data not provided for replicates 0 and 1)
      • t=30min: 5 replicates (data not provided for replicates 0 and 3)
      • t=60min: 4 replicates (data not provided for replicate 0)
      • t=90min: 5 replicates (data not provided for replicates 0 and 4)
      • t=120min: 5 replicates (data not provided for replicates 0 and 3)
        • t15, t30, & t60 (CS @ 13C)
        • t90 & t120 (CS @13C, then 30-60min recovery @30C)

Overview of DNA Microarray Analysis

  • Steps 1-3 have been performed using GenePix Pro software (Runs the microarray scanner).
  • Steps 4-5 were performed for using R (see: Microarray Data Analysis Workflow)
  • Statistical analysis will be performed in Microsoft Excel (steps 6 & 7)
  • STEM software used for clustering and mapping (steps 8 & 9)
  • Modeling will be performed in MATLAB (step 10 & 11)
  1. Quantitate the fluorescence signal in each spot
  2. Calculate the ratio of red/green fluorescence
  3. Log2 transform the ratios
  4. Normalize the ratios on each microarray slide
  5. Normalize the ratios for a set of slides in an experiment
  6. Perform statistical analysis on the ratios
  7. Compare individual genes with known data
  8. Pattern finding algorithms (clustering)
  9. Map onto biological pathways
  10. Identifying regulatory transcription factors responsible for observed changes in gene expression
  11. Dynamical systems modeling of the gene regulatory network


  • Data was provided in the form of an Excel spreadsheet by Dr. Dahlquist and Dr. Fitzpatrick. Each row on the worksheet was assigned a gene, and the "Mast Index" (column A) numbers the rows sequentially, so that they may be used to sort the genes into their initial order. the "ID" (column B) contains the gene identifier from Saccharomyces Genome Database. Column C contains the standard name for each corresponding gene. Subsequent columns contain the log(2) ratio of Red:Green fluorescence from each microarray experiment. Column headings strain_log(2) fold change_time(min)-replicate.

Statistical Analysis Part 1: Witin-stain ANOVA

  • Purpose: To determine if any genes had a gene expression change that was significantly different than zero at any timepoint.
  1. New "dGLN3_ANOVA" Excel worksheet was created.
  2. All data from the "Master_Sheet" worksheet for dGLN3 was copied and pasted into dGLN3_ANOVA.
  3. Five columns were added labeled dGLN3_AvgLogFC_(TIME)
    • (TIME) is 15, 30, 60, 90, or 120 minutes.
  4. The AVERAGE function in Excel was used to calculate the averages of the log fold change data for each gene and time point of every replicate.
    • To Auto Fill Cells: Click on this cell and position your cursor at the bottom right corner. You should see your cursor change to a thin black plus sign (not a chubby white one). When it does, double click, and the formula will magically be copied to the entire column of 6188 other genes.
  5. The column dGLN3_ss_HO was created, and the SUMSQ function in Excel was used to square the sum of all data points collected for each gene.
  6. The column dGLN3_ss_(TIME) was created for each time point and the formula =SUMSQ(<range of cells for logFC_(TIME)>)-COUNTA(<range of cells for logFC_(TIME)>)*<AvgLogFC_(TIME)>^2 was used for each time point.
    • The COUNTA function counts the number of cells in the specified range that have data in them (i.e., does not count cells with missing values).
    • The phrase <range of cells for logFC_(TIME)> should be replaced by the data range associated with the appropriate time point.
    • The phrase <AvgLogFC_(TIME)> should be replaced by the cell number in which you computed the AvgLogFC for the appropriate time point.
  7. The column dGLN3_SS_full was created. The sum for the range of cells containing "ss" for each timepoint was calculated through Excel using the SUM function.
  8. The columns dGLN3_Fstat and dGLN3_p-value were created.
  9. Under the dGLN3_Fstat column, the formula =((n-5)/5)*(<dGLN3_ss_HO>-<dGLN3_SS_full>)/<dGLN3_SS_full> was used.
    • "n" represents the total number of replicates from all timepoints.
    • "5" is the number of timepoints.
    • Replace the phrase dGLN3_ss_HO with the cell designation.
    • Replace the phrase <dGLN3_SS_full> with the cell designation.
  10. Below the dGLN3_p-value header, the formula =FDIST(<dGLN3_Fstat>,5,n-5) was used.
    • Replace the phrase <dGLN3_Fstat> with the cell designation and the "n" with the total number of replicates.

Sanity Check: Filter p-values less then 0.05

  • Excel was used to filter the data according to the p-value (dGLN3_p-value). Criterion was set so that only data with a p value less than 0.05 was present.
    • A number will appear in the lower left hand corner of the window giving you the number of rows that meet that criterion.

Calculate the Bonferroni and p-value Correction

  • Purpose: To make adjustments to the p-value to correct for the multiple testing problem. (multiplies p-value by number of hypotheses tests run)
  • The "less than 0.05" filter on the dGLN3_p-value was removed for the following steps.
  1. Added 2 columns labeled dGLN3_Bonferroni_p-value.
  2. The formula =<dGLN3_p-value>*6189 was used in the first column labeled dGLN3_Bonferroni_p-value and all cells were autofilled
  3. Corrected p-values greater than 1 were replaced with the number 1 using the formula =IF(dGLN3_Bonferroni_p-value>1,1,dGLN3_Bonferroni_p-value) in the second dGLN3_Bonferroni_p-value column.
    • "dGLN3_Bonferroni_p-value" refers to the cell in which the first Bonferroni p-value computation was made.

Calculate the Benjamini & Hochberg p-value Correction

  1. Created a new worksheet titled "dGLN3_ANOVA_B-H".
  2. The "MasterIndex", "ID", and "Standard Name" columns were copied and pasted into the first columns (A, B, & C).
    • For the following, use Paste special > Paste values.
  3. The unadjusted p-values from the dGLN3_ANOVA worksheet was copied and pasted into column D.
  4. Columns A, B, C, and D were selected and sorted by ascending values on Column D.
    • To Sort Data: Click the "sort" button from A to Z on the toolbar. Select sort by column D, smallest to largest.
  5. The Column labeled "Rank" was created in cell E1. We will create a series of numbers in ascending order from 1 to 6189 in this column.
    • This is the p-value rank, smallest to largest. Type "1" into cell E2 and "2" into cell E3.
    • To autofill: Select both cells E2 and E3. Double-click on the plus sign on the lower right-hand corner of your selection to fill the column with a series of numbers from 1 to 6189.
  6. To calculate the Benjamini and Hochberg p-value correction, a column labeled dGLN3_B-H_p-value was created in cell F1. The formula =(D2*6189)/E2 was used in cell F2, and used to autofill the rest of the column.
  7. A new column labeled dGLN3_B-H_p-value was entered into cell G1. The formula =IF(F2>1,1,F2) was entered into cell G2 and used to autofill column G.
  8. Columns A through G were selected and sorted them by your MasterIndex in Column A in ascending order.
  9. Column G values were copied and pasted into the dGLN3_ANOVA sheet.
  • This Excel file has been uploaded to LMU Box.

Sanity Check: Number of genes significantly changed

Before we move on to further analysis of the data, we want to perform a more extensive sanity check to make sure that we performed our data analysis correctly. We are going to find out the number of genes that are significantly changed at various p value cut-offs.

  • Go to your (STRAIN)_ANOVA worksheet.
  • Select row 1 (the row with your column headers) and select the menu item Data > Filter > Autofilter (The funnel icon on the Data tab). Little drop-down arrows should appear at the top of each column. This will enable us to filter the data according to criteria we set.
  • Click on the drop-down arrow for the unadjusted p value. Set a criterion that will filter your data so that the p value has to be less than 0.05.
    • How many genes have p < 0.05? and what is the percentage (out of 6189)?
    • How many genes have p < 0.01? and what is the percentage (out of 6189)?
    • How many genes have p < 0.001? and what is the percentage (out of 6189)?
    • How many genes have p < 0.0001? and what is the percentage (out of 6189)?
  • When we use a p value cut-off of p < 0.05, what we are saying is that you would have seen a gene expression change that deviates this far from zero by chance less than 5% of the time.
  • We have just performed 6189 hypothesis tests. Another way to state what we are seeing with p < 0.05 is that we would expect to see this a gene expression change for at least one of the timepoints by chance in about 5% of our tests, or 309 times. Since we have more than 309 genes that pass this cut off, we know that some genes are significantly changed. However, we don't know which ones. To apply a more stringent criterion to our p values, we performed the Bonferroni and Benjamini and Hochberg corrections to these unadjusted p values. The Bonferroni correction is very stringent. The Benjamini-Hochberg correction is less stringent. To see this relationship, filter your data to determine the following:
    • How many genes are p < 0.05 for the Bonferroni-corrected p value? and what is the percentage (out of 6189)?
    • How many genes are p < 0.05 for the Benjamini and Hochberg-corrected p value? and what is the percentage (out of 6189)?
  • In summary, the p value cut-off should not be thought of as some magical number at which data becomes "significant". Instead, it is a moveable confidence level. If we want to be very confident of our data, use a small p value cut-off. If we are OK with being less confident about a gene expression change and want to include more genes in our analysis, we can use a larger p value cut-off.
  • Comparing results with known data: the expression of the gene NSR1 (ID: YGR159C)is known to be induced by cold shock. Find NSR1 in your dataset. What is its unadjusted, Bonferroni-corrected, and B-H-corrected p values? What is its average Log fold change at each of the timepoints in the experiment? Note that the average Log fold change is what we called "STRAIN)_AvgLogFC_(TIME)" in step 3 of the ANOVA analysis.
  • We will compare the numbers we get between the wild type strain and the other strains studied, organized as a table. Use this sample PowerPoint slide to see how your table should be formatted. (Note that you will need to unzip the file after downloading.)