Alison S King Week 4/5

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Electronic Lab Notebook


The purpose of this assignment is to practice working with large data sets in Excel and to analyze the microarray data using the ANOVA statistical test and p-values. Additionally, we used online tools to identify transcription factors involved and create a network graph.

Experimental Design/Workflow and Results

In the Excel spreadsheet, there is a worksheet labeled "Master_Sheet_<strain>", where strain refers to the particular strain of yeast. In this worksheet, each row contains the data for one gene (one spot on the microarray). The first column contains the "Master Index", which numbers all of the rows sequentially in the worksheet so that we can always use it to sort the genes into the order they were in when we started. The second column (labeled "ID") contains the gene identifier from the Saccharomyces Genome Database. The third column contains the Standard Name for each of the genes. Each subsequent column contains the log2 ratio of the red/green fluorescence from each microarray hybridized in the experiment (steps 1-5 above having been performed for you already).

  • For this experiment, we will be working with the strain Δzap1, and the data can be found on "Master_Sheet_dZAP1".
  • There are 4 replicates of each strain at each of the 5 time points, for a total of 20 recordings per gene.

Each of the column headings from the data begin with the experiment name ("dZAP1" for the Δzap1 data). "LogFC" stands for "Log2 Fold Change" which is the Log2 red/green ratio. The timepoints are designated as "t" followed by a number in minutes. Replicates are numbered as "-0", "-1", "-2", etc. after the timepoint.

The timepoints are t15, t30, t60 (cold shock at 13°C) and t90 and t120 (cold shock at 13°C followed by 30 or 60 minutes of recovery at 30°C).

Statistical Analysis Part 1 : ANOVA

  1. Create a new worksheet, naming it "dZAP1_ANOVA".
  2. Copy the first three columns containing the "MasterIndex", "ID", and "Standard Name" from the "Master_Sheet" worksheet for dZAP1 and paste it into your new worksheet. Copy the columns containing the data for dZAP1 and paste it into your new worksheet.
  3. At the top of the first column to the right of your data, create five column headers of the form dZAP1_AvgLogFC_(TIME) where (TIME) is t15, t30, t60, t90, t120.
  4. In the cell below the dZAP1_AvgLogFC_t15 header, type =AVERAGE(
  5. Then highlight all the data in row 2 associated with dZAP1 and t15, press the closing paren key (shift 0),and press the "enter" key.
  6. This cell now contains the average of the log fold change data from the first gene at t=15 minutes.
  7. 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.
  8. Repeat steps (4) through (8) with the t30, t60, t90, and the t120 data.
  9. Now in the first empty column to the right of the dZAP1_AvgLogFC_t120 calculation, create the column header dZAP1_ss_HO.
  10. In the first cell below this header, type =SUMSQ(
  11. Highlight all the LogFC data in row 2 for your dZAP1 (but not the AvgLogFC), press the closing paren key (shift 0),and press the "enter" key.
  12. In the next empty column to the right of dZAP1_ss_HO, create the column headers dZAP1_ss_(TIME) as in (3).
  13. Make a note of how many data points you have at each time point for your strain (4 for dZAP1). Also, make a note of the total number of data point (20 for dZAP1).
  14. In the first cell below the header dZAP1_ss_t15, type =SUMSQ(<range of cells for logFC_t15>)-COUNTA(<range of cells for logFC_t15>)*<AvgLogFC_t15>^2 and hit enter.
    • 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_t15> should be replaced by the data range associated with t15.
    • The phrase <AvgLogFC_t15> should be replaced by the cell number in which you computed the AvgLogFC for t15, and the "^2" squares that value.
    • Upon completion of this single computation, use the Step (7) trick to copy the formula throughout the column.
  15. Repeat this computation for the t30 through t120 data points. Again, be sure to get the data for each time point, type the right number of data points, and get the average from the appropriate cell for each time point, and copy the formula to the whole column for each computation.
  16. In the first column to the right of dZAP1_ss_t120, create the column header dZAP1_SS_full.
  17. In the first row below this header, type =sum(<range of cells containing "ss" for each timepoint>) and hit enter.
  18. In the next two columns to the right, create the headers dZAP1_Fstat and dZAP1_p-value.
  19. Recall the number of data points from (13): call that total n (n=20).
  20. In the first cell of the dZAP1_Fstat column, type =((20-5)/5)*(<dZAP1_ss_HO>-<dZAP1_SS_full>)/<dZAP1_SS_full> and hit enter.
    • Note that "5" is the number of timepoints.
    • Replace the phrase <dZAP1_ss_HO> with the cell designation.
    • Replace the phrase <dZAP1_SS_full> with the cell designation.
    • Copy to the whole column.
  21. In the first cell below the dZAP1_p-value header, type =FDIST(<dZAP1_Fstat>,5,20-5) replacing the phrase <dZAP1_Fstat> with the cell designation. Copy to the whole column.
  22. Before we move on to the next step, we will perform a quick sanity check to see if we did all of these computations correctly.
    • Click on cell A1 and click on the Data tab. Select the Filter icon (looks like a funnel). 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 on your dZAP1_p-value column. Select "Number Filters". In the window that appears, set a criterion that will filter your data so that the p value has to be less than 0.05.
    • Excel will now only display the rows that correspond to data meeting that filtering criterion. A number will appear in the lower left hand corner of the window giving you the number of rows that meet that criterion. We will check our results with each other to make sure that the computations were performed correctly.
      • Note: 2485 genes had p < 0.05

Calculate the Bonferroni and p value Correction

  1. Now we will perform adjustments to the p value to correct for the multiple testing problem. Label the next two columns to the right with the same label, dZAP1_Bonferroni_p-value.
  2. Type the equation =<dZAP1_p-value>*6189, Upon completion of this single computation, use the Step (10) trick to copy the formula throughout the column.
  3. Replace any corrected p value that is greater than 1 by the number 1 by typing the following formula into the first cell below the second dZAP1_Bonferroni_p-value header: =IF(dZAP1_Bonferroni_p-value>1,1,dZAP1_Bonferroni_p-value), where "dZAP1_Bonferroni_p-value" refers to the cell in which the first Bonferroni p value computation was made. Use the Step (10) trick to copy the formula throughout the column.

Calculate the Benjamini & Hochberg p value Correction

  1. Insert a new worksheet named "dZAP1_ANOVA_B-H".
  2. Copy and paste the "MasterIndex", "ID", and "Standard Name" columns from your previous worksheet into the first two columns of the new worksheet.
  3. For the following, use Paste special > Paste values. Copy your unadjusted p values from your ANOVA worksheet and paste it into Column D.
  4. Select all of columns A, B, C, and D. Sort by ascending values on Column D. Click the sort button from A to Z on the toolbar, in the window that appears, sort by column D, smallest to largest.
  5. Type the header "Rank" 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. 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. Now you can calculate the Benjamini and Hochberg p value correction. Type dZAP1_B-H_p-value in cell F1. Type the following formula in cell F2: =(D2*6189)/E2 and press enter. Copy that equation to the entire column.
  7. Type "dZAP1_B-H_p-value" into cell G1.
  8. Type the following formula into cell G2: =IF(F2>1,1,F2) and press enter. Copy that equation to the entire column.
  9. Select columns A through G. Now sort them by your MasterIndex in Column A in ascending order.
  10. Copy column G and use Paste special > Paste values to paste it into the next column on the right of your ANOVA sheet.

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 dZAP1_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)?
      • 2485 genes had p < 0.05 (40.15%)
    • How many genes have p < 0.01? and what is the percentage (out of 6189)?
      • 1609 genes had p < 0.01 (26.00%)
    • How many genes have p < 0.001? and what is the percentage (out of 6189)?
      • 885 genes had p < 0.001 (14.30%)
    • How many genes have p < 0.0001? and what is the percentage (out of 6189)?
      • 457 genes had p < 0.0001 (7.38%)
  • 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)?
      • 210 genes had p < 0.05 (3.39%)
    • How many genes are p < 0.05 for the Benjamini and Hochberg-corrected p value? and what is the percentage (out of 6189)?
      • 1767 genes had p < 0.05 (28.55%)
  • 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 the dZAP1 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 "dZAP1_AvgLogFC_(TIME)" in step 3 of the ANOVA analysis.
    • Unadjusted p-value: 6.05673E-08
    • Bonferroni-corrected p-value: 0.000374851
    • B-H-corrected p-value: 1.04125E-05
    • average Log fold change at time:
      • t15: 3.8996
      • t30: 3.7238
      • t60: 3.962775
      • t90: -2.156
      • t120: 0.0542

Clustering and GO Term Enrichment with stem (part 2)

  1. Prepare your microarray data file for loading into STEM. Have this part completed for Tuesday, February 19.
    • Insert a new worksheet into your Excel workbook, and name it "dZAP1_stem".
    • Select all of the data from your "dZAP1_ANOVA" worksheet and Paste special > paste values into your "dZAP1_stem" worksheet.
      • Your leftmost column should have the column header "Master_Index". Rename this column to "SPOT". Column B should be named "ID". Rename this column to "Gene Symbol". Delete the column named "Standard_Name".
      • Filter the data on the B-H corrected p value to be > 0.05 (that's greater than in this case).
        • Once the data has been filtered, select all of the rows (except for your header row) and delete the rows by right-clicking and choosing "Delete Row" from the context menu. Undo the filter. This ensures that we will cluster only the genes with a "significant" change in expression and not the noise.
      • Delete all of the data columns EXCEPT for the Average Log Fold change columns for each timepoint (for example, dZAP1_AvgLogFC_t15, etc.).
      • Rename the data columns with just the time and units (for example, 15m, 30m, etc.).
      • Save your work. Then use Save As to save this spreadsheet as Text (Tab-delimited) (*.txt). Click OK to the warnings and close your file.
        • Note that you should turn on the file extensions if you have not already done so.
  2. Now download and extract the STEM software. Click here to go to the STEM web site.
    • Click on the download link and download the file to your Desktop.
    • Unzip the file. In Seaver 120, you can right click on the file icon and select the menu item 7-zip > Extract Here.
    • This will create a folder called stem.
    • Inside the folder, double-click on the stem.jar to launch the STEM program.
  3. Running STEM
    1. In section 1 (Expression Data Info) of the the main STEM interface window, click on the Browse... button to navigate to and select your file.
      • Click on the radio button No normalization/add 0.
      • Check the box next to Spot IDs included in the data file.
    2. In section 2 (Gene Info) of the main STEM interface window, leave the default selection for the three drop-down menu selections for Gene Annotation Source, Cross Reference Source, and Gene Location Source as "User provided".
    3. Click the "Browse..." button to the right of the "Gene Annotation File" item. Browse to your "stem" folder and select the file "gene_association.sgd.gz" and click Open.
    4. In section 3 (Options) of the main STEM interface window, make sure that the Clustering Method says "STEM Clustering Method" and do not change the defaults for Maximum Number of Model Profiles or Maximum Unit Change in Model Profiles between Time Points.
    5. In section 4 (Execute) click on the yellow Execute button to run STEM.
  4. Viewing and Saving STEM Results
    1. A new window will open called "All STEM Profiles (1)". Each box corresponds to a model expression profile. Colored profiles have a statistically significant number of genes assigned; they are arranged in order from most to least significant p value. Profiles with the same color belong to the same cluster of profiles. The number in each box is simply an ID number for the profile.
      • Click on the button that says "Interface Options...". At the bottom of the Interface Options window that appears below where it says "X-axis scale should be:", click on the radio button that says "Based on real time". Then close the Interface Options window.
      • Take a screenshot of this window (on a PC, simultaneously press the Alt and PrintScreen buttons to save the view in the active window to the clipboard) and paste it into a PowerPoint presentation to save your figures.
    2. Click on each of the SIGNIFICANT profiles (the colored ones) to open a window showing a more detailed plot containing all of the genes in that profile.
      • Take a screenshot of each of the individual profile windows and save the images in your PowerPoint presentation.
      • At the bottom of each profile window, there are two yellow buttons "Profile Gene Table" and "Profile GO Table". For each of the profiles, click on the "Profile Gene Table" button to see the list of genes belonging to the profile. In the window that appears, click on the "Save Table" button and save the file to your desktop as "dZAP1_profile#_genelist.txt", where you replace the number symbol with the actual profile number.
        • Upload these files to OpenWetWare and link to them on your individual journal page. (Note that it will be easier to zip all the files together and upload them as one file).
      • For each of the significant profiles, click on the "Profile GO Table" to see the list of Gene Ontology terms belonging to the profile. In the window that appears, click on the "Save Table" button and save the file to your desktop as "dZAP1_profile#_GOlist.txt", where you replace the number symbol with the actual profile number. At this point you have saved all of the primary data from the STEM software and it's time to interpret the results!
        • Upload these files to OpenWetWare and link to them on your individual journal page. (Note that it will be easier to zip all the files together and upload them as one file).
  5. Analyzing and Interpreting STEM Results
    1. Select one of the profiles you saved in the previous step for further interpretation of the data.
      • I chose profile 31.
      • Why did you select this profile? In other words, why was it interesting to you?
        • This profile was one of the top two profiles with the most genes belonging to it. The other one, Profile 13, shows an opposite pattern in terms of up-regulation and down-regulation. My partner chose to work on Profile 13.
      • How many genes belong to this profile?
        • 368 genes
      • How many genes were expected to belong to this profile?
        • 41.4 genes
      • What is the p value for the enrichment of genes in this profile? Bear in mind that we just finished computing p values to determine whether each individual gene had a significant change in gene expression at each time point. This p value determines whether the number of genes that show this particular expression profile across the time points is significantly more than expected.
        • p-value = 4.8E-225
      • Open the GO list file you saved for this profile in Excel. This list shows all of the Gene Ontology terms that are associated with genes that fit this profile. Select the third row and then choose from the menu Data > Filter > Autofilter. Filter on the "p-value" column to show only GO terms that have a p value of < 0.05. How many GO terms are associated with this profile at p < 0.05? The GO list also has a column called "Corrected p-value". This correction is needed because the software has performed thousands of significance tests. Filter on the "Corrected p-value" column to show only GO terms that have a corrected p value of < 0.05. How many GO terms are associated with this profile with a corrected p value < 0.05?
        • 235 of 753 GO terms have p-value < 0.05
        • 20 of 753 GO terms have corrected p-value < 0.05
      • Select 6 Gene Ontology terms from your filtered list (corrected p < 0.05).
        • Each member of the group will be reporting on his or her own cluster in your research presentation. You should take care to choose terms that are the most significant, but that are also not too redundant. For example, "RNA metabolism" and "RNA biosynthesis" are redundant with each other because they mean almost the same thing.
          • Note whether the same GO terms are showing up in multiple clusters.
        • Look up the definitions for each of the terms at In your research presentation, you will discuss the biological interpretation of these GO terms. In other words, why does the cell react to cold shock by changing the expression of genes associated with these GO terms? Also, what does this have to do with the transcription factor being deleted (for the Δgln3 and Δswi4 groups)?
        • To easily look up the definitions, go to
        • Copy and paste the GO ID (e.g. GO:0044848) into the search field on the left of the page.
        • In the results page, click on the button that says "Link to detailed information about <term>, in this case "biological phase"".
        • The definition will be on the next results page, e.g. here.
Selected Gene Ontology Terms (corrected p-value < 0.05)
  • GO:0071704 organic substance metabolic process
    • The chemical reactions and pathways involving an organic substance, any molecular entity containing carbon. Source: GOC:mah, CHEBI:50860
  • GO:0004386 helicase activity
    • Catalysis of the reaction: NTP + H2O = NDP + phosphate, to drive the unwinding of a DNA or RNA helix. Source: ISBN:0198506732, GOC:mah
  • GO:0043231 intracellular membrane-bounded organelle
    • Organized structure of distinctive morphology and function, bounded by a single or double lipid bilayer membrane and occurring within the cell. Includes the nucleus, mitochondria, plastids, vacuoles, and vesicles. Excludes the plasma membrane. Source: GOC:go_curators
  • GO:0042255 ribosome assembly
    • The aggregation, arrangement and bonding together of the mature ribosome and of its subunits. Source: GOC:ma
  • GO:0000154 rRNA modification
    • The covalent alteration of one or more nucleotides within an rRNA molecule to produce an rRNA molecule with a sequence that differs from that coded genetically. Source: GOC:curators
  • GO:0042623 ATPase activity, coupled
    • Catalysis of the reaction: ATP + H2O = ADP + phosphate; this reaction directly drives some other reaction, for example ion transport across a membrane. Source: GOC:jl, EC:

Using YEASTRACT to Infer which Transcription Factors Regulate a Cluster of Genes (Tuesday, Feb. 19)

In the previous analysis using STEM, we found a number of gene expression profiles (aka clusters) which grouped genes based on similarity of gene expression changes over time. The implication is that these genes share the same expression pattern because they are regulated by the same (or the same set) of transcription factors. We will explore this using the YEASTRACT database.

  1. Open the gene list in Excel for the one of the significant profiles from your stem analysis. Choose a cluster with a clear cold shock/recovery up/down or down/up pattern. You should also choose one of the largest clusters.
    • Copy the list of gene IDs onto your clipboard.
  2. Launch a web browser and go to the YEASTRACT database.
    • On the left panel of the window, click on the link to Rank by TF.
    • Paste your list of genes from your cluster into the box labeled ORFs/Genes.
    • Check the box for Check for all TFs.
    • Accept the defaults for the Regulations Filter (Documented, DNA binding plus expression evidence)
    • Do not apply a filter for "Filter Documented Regulations by environmental condition".
    • Rank genes by TF using: The % of genes in the list and in YEASTRACT regulated by each TF.
    • Click the Search button.
  3. Answer the following questions:
    • In the results window that appears, the p values colored green are considered "significant", the ones colored yellow are considered "borderline significant" and the ones colored pink are considered "not significant". How many transcription factors are green or "significant"?
      • 25 transcription factors are green.
    • Copy the table of results from the web page and paste it into a new Excel workbook to preserve the results.
      • Upload the Excel file to OWW or Box and link to it in your electronic lab notebook.
      • Are GLN3, HAP4, and/or ZAP1 on the list? If so, what is their "% in user set", "% in YEASTRACT", and "p value".
        • GLN3
          •  % in user set = 29.12%
          •  % in YEASTRACT = 8.92%
          • p-value = 0.000000000355026
        • ZAP1
          •  % in user set = 22.25%
          •  % in YEASTRACT = 5.26%
          • p-value = 0.345649529322813
        • HAP4
          •  % in user set = 15.11%
          •  % in YEASTRACT = 5.51%
          • p-value = 0.236567054959727
  4. For the mathematical model that we will build, we need to define a gene regulatory network of transcription factors that regulate other transcription factors. We can use YEASTRACT to assist us with creating the network. We want to generate a network with approximately 15-20 transcription factors in it.
    • You need to select from this list of "significant" transcription factors, which ones you will use to run the model. You will use these transcription factors and add GLN3, HAP4, and ZAP1 if they are not in your list. Explain in your electronic notebook how you decided on which transcription factors to include. Record the list and your justification in your electronic lab notebook. Each group member will select a different network (they can have some overlapping transcription factors, but some should also be different).
    • Go back to the YEASTRACT database and follow the link to Generate Regulation Matrix.
    • Copy and paste the list of transcription factors you identified (plus HAP4, GLN3, and ZAP1) into both the "Transcription factors" field and the "Target ORF/Genes" field.
    • We are going to use the "Regulations Filter" options of "Documented", "Only DNA binding evidence"
      • Click the "Generate" button.
      • In the results window that appears, click on the link to the "Regulation matrix (Semicolon Separated Values (CSV) file)" that appears and save it to your Desktop. Rename this file with a meaningful name so that you can distinguish it from the other files you will generate.
My Selected Transcription Factors

I chose the 15 transcription factors with the smallest p-values, and then I added HAP4, and ZAP1.

  • Sfp1p
  • Stb5p
  • Yhp1p
  • Yox1p
  • Ace2p
  • Swi5p
  • Gln3p
  • Yap1p
  • Asg1p
  • Tup1p
  • Msn2p
  • Gcr2p
  • Gat3p
  • Pdr3p
  • Ndt80p
  • Hap4p
  • Zap1p

Visualizing Your Gene Regulatory Networks with GRNsight (Tuesday, Feb. 21)

We will analyze the regulatory matrix files you generated above in Microsoft Excel and visualize them using GRNsight to determine which one will be appropriate to pursue further in the modeling.

  1. First we need to properly format the output files from YEASTRACT.
    • Open the file in Excel. It will not open properly in Excel because a semicolon was used as the column delimiter instead of a comma. To fix this, Select the entire Column A. Then go to the "Data" tab and select "Text to columns". In the Wizard that appears, select "Delimited" and click "Next". In the next window, select "Semicolon", and click "Next". In the next window, leave the data format at "General", and click "Finish". This should now look like a table with the names of the transcription factors across the top and down the first column and all of the zeros and ones distributed throughout the rows and columns. This is called an "adjacency matrix." If there is a "1" in the cell, that means there is a connection between the trancription factor in that row with that column.
    • Save this file in Microsoft Excel workbook format (.xlsx).
    • For this adjacency matrix to be usable in GRNmap (the modeling software) and GRNsight (the visualization software), we need to transpose the matrix. Insert a new worksheet into your Excel file and name it "network". Go back to the previous sheet and select the entire matrix and copy it. Go to you new worksheet and click on the A1 cell in the upper left. Select "Paste special" from the "Home" tab. In the window that appears, check the box for "Transpose". This will paste your data with the columns transposed to rows and vice versa. This is necessary because we want the transcription factors that are the "regulatORS" across the top and the "regulatEES" along the side.
    • The labels for the genes in the columns and rows need to match. Thus, delete the "p" from each of the gene names in the columns. Adjust the case of the labels to make them all upper case.
    • In cell A1, copy and paste the text "rows genes affected/cols genes controlling".
    • Finally, for ease of working with the adjacency matrix in Excel, we want to alphabatize the gene labels both across the top and side.
      • Select the area of the entire adjacency matrix.
      • Click the Data tab and click the custom sort button.
      • Sort Column A alphabetically, being sure to exclude the header row.
      • Now sort row 1 from left to right, excluding cell A1. In the Custom Sort window, click on the options button and select sort left to right, excluding column 1.
    • Name the worksheet containing your organized adjacency matrix "network" and Save.
  2. Now we will visualize what these gene regulatory networks look like with the GRNsight software.
    • Go to the GRNsight home page.
    • Select the menu item File > Open and select the regulation matrix .xlsx file that has the "network" worksheet in it that you formatted above. If the file has been formatted properly, GRNsight should automatically create a graph of your network. You can click the "Grid Layout" button to arrange the nodes in a grid, or you can click and drag the nodes (genes) around until you get a layout that you like and take a screenshot of the results. Paste it into your PowerPoint presentation.
      • If you have nodes (genes) floating around in the display that are not connected to any other nodes, we need to delete them from the network for the modeling to work properly. Go back to the Excel workbook and network sheet and delete both the row and column with the floating gene's name. Then re-upload the edited file to GRNsight to visualize it. Use this final version in your PowerPoint and subsequent modeling.

Data and Files

All data and files can be found in our BIOL388_Spring2019 Box.

For this week, I have created two files:

Profile Lists:

Scientific Conclusion

The statistical analysis appears to show that our results are statistically significant when looking at the unadjusted p-value; however, when we use the Bonferroni adjustment, our results do not appear to show any variation other than what can reasonably be expected at a p = 0.05 confidence level. Thus, we should conclude for now that the cold treatment did not have any effect on the gene expression if we use the Bonferroni adjustment. However, that correction can be very stringent, and so it appears to be more appropriate to use the Benjamini & Hochberg correction instead. Using this method, we see p-values > 0.05 about 28% of the time, which should be considered statistically significant. Thus, I would conclude with fairly strong confidence that the cold treatment did affect the gene transcription of the yeast mutant Δzap1.

We used the STEM software to analyze our data and group genes into clusters of similar gene expression changes over the course of the treatment. STEM found that 8 of the 50 profiles contained enough genes to be statistically significant. Each of these profiles showed various reactions to cold shock over time - some experience up-regulation, some down-regulation, and some remained virtually unchanged. Within each profile, we could look at the gene ontology and see which gene groups (and functions) seemed to be affected most by cold shock. I focused on Profile 31, which contained 368 genes (p-value = 4.8E-225) and showed a period of up-regulation followed by down-regulation overall. The functions of the genes in this profile were mostly metabolic processes, helicase activity, organelle and ribosome assembly, rRNA modification, and ATPase activity. Judging by the graph created in STEM for this profile, these organic processes would be up-regulated immediately after cold shock, but eventually down-regulated during the recovery process.

After identifying the genes in Profile 31, we entered them into YEASTRACT and were able to identify the transcription factors behind each gene. This gave us an idea of which TF's were important for which functions. Once we found significant TF's, we were able to enter them into GRNsight and create a gene regulatory network that shows us the interactions between various TF's. By observing this graph, we can identify which transcription factors and genes regulate one another.


My lab partner, Edward Talatala and I texted a few different times about our general format of the journal and the specific numbers we got for our data. We clarified confusing instructions with each other and compared our final figures. We worked together in class on Tues. Feb. 12, 2019 to finish the ANOVA part of the assignment. We worked together in class on Tues. Feb. 19 on the stem portion of the assignment.

I visited to learn how to cite datasets in APA format.

My methods section is based on those described in the Week 4 Assignment Page.

Except for what is noted above, this individual journal entry was completed by me and not copied from another source.

Alison S King (talk) 19:45, 19 February 2019 (PST)


Dahlquist, K. D. (2019) [Data file]. Retrieved from

Institute for Bioengineering and Biosciences (2017) Yeastract. Retrieved from on 19 February 2019.

Loyola Marymount University (12 February 2019) BIOL388/S19:Week 4. Retrieved from on 12 February 2019.

Loyola Marymount University (19 February 2019) BIOL388/S19:Week 5. Retrieved from on 19 February 2019.

Loyola Marymount University (2018) GRNsight. Retrieved from on 19 February 2019.

Open Biological Ontologies Foundry (2019) The Gene Ontology Resource. Retrieved from on 19 February 2019.

Short Time-series Expression Miner (STEM) (Version 1.3.11) [Computer software]. (2006). Retrieved from


Alison S King

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