Natalie Williams Summer Electronic Notebook 2015

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Summer GRNmap 2015

May 2015

May 18, 2015

Purpose: To better understand how the data used for generating the model was obtained. We are using R to normalize the microarray data from the Dahlquist Lab.
Procedure

  • Downloaded and installed R from (link to download site) and the limma package ( direct link to download zipped file)
    • The limma folder within the limma zip file must be copied into the R directory
    • Limma contains normalization procedures
    • Extracted the limma package into R's library folder
  • Downloaded the necessary files from links sent by Dr. Dahlquist

Within Array Normalization for the Ontario Chips

  1. Launch R x64 3.1.0 (make sure you are using the 64-bit version).
  2. Change the directory to the folder containing the targets file and the GPR files for the Ontario chips by selecting the menu item File > Change dir... and clicking on the appropriate directory. You will need to click on the + sign to drill down to the right directory. Once you have selected it, click OK.
  3. In R, select the menu item File > Source R code..., and select the Ontario_Chip_Within-Array_Normalization_modified_20150514.R script.
    • You will be prompted by an Open dialog for the Ontario targets file. Select the file Ontario_Targets_wt-dCIN5-dGLN3-dHAP4-dHMO1-dSWI4-dZAP1-Spar_20150514.csv and click Open.
    • Wait while R processes your files.

Within Array Normalization for the GCAT Chips and Between Array Normalization for All Chips

  1. These instructions assume that you have just completed the Within Array Normalization for the Ontario Chips in the section above.
  2. In R, select the menu item File > Source R code..., and select the Within-Array_Normalization_GCAT_and_Merged_Ontario-GCAT_Between-Chip_Normalization_modified_20150514.R script.
    • You will be prompted by an Open dialog for the GCAT targets file. Select the file GCAT_Targets.csv and click Open.
    • Wait while R processes your files.
  3. When the processing has finished, you will find two files called GCAT_and_Ontario_Within_Array_Normalization.csv and GCAT_and_Ontario_Final_Normalized_Data.csv in the same folder.
    • Save these files to LionShare and/or to a flash drive.

Visualizations of the Normalized Data
Create MA Plots and Box Plots for the GCAT Chips Input the following code, line by line, into the main R window. Press the enter key after each block of code.

GCAT.GeneList<-RGG$genes$ID
lg<-log2((RGG$R-RGG$Rb)/(RGG$G-RGG$Gb))
  • If you get a message saying "NaNs produced" this is OK, proceed to the next step.
r0<-length(lg[1,])
rx<-tapply(lg[,1],as.factor(GCAT.GeneList),mean)
r1<-length(rx)
MM<-matrix(nrow=r1,ncol=r0)
for(i in 1:r0) {MM[,i]<-tapply(lg[,i],as.factor(GCAT.GeneList),mean)}
MC<-matrix(nrow=r1,ncol=r0)
for(i in 1:r0) {MC[,i]<-dw[i]*MM[,i]}
MCD<-as.data.frame(MC)
colnames(MCD)<-chips
rownames(MCD)<-gcatID
la<-(1/2*log2((RGG$R-RGG$Rb)*(RGG$G-RGG$Gb)))
  • If you get these Warning messages, it's OK:
1: In (RGG$R - RGG$Rb) * (RGG$G - RGG$Gb) :
NAs produced by integer overflow
2: NaNs produced
r2<-length(la[1,])
ri<-tapply(la[,1],as.factor(GCAT.GeneList),mean)
r3<-length(ri)
AG<-matrix(nrow=r3,ncol=r2)
for(i in 1:r2) {AG[,i]<-tapply(la[,i],as.factor(GCAT.GeneList),mean)}
par(mfrow=c(3,3))
for(i in 1:r2) {plot(AG[,i],MC[,i],main=chips[i],xlab='A',ylab='M',ylim=c(-5,5),xlim=c(0,15))}
browser()
  • Maximize the window in which the graphs have appeared. Save the graphs as a JPEG (File>Save As>JPEG>100% quality...). Once the graphs have been saved, close the window. To continue with the rest of the code, press Enter.
    • To make sure that you save the clearest image, do not scroll in the window because a grey bar will appear if you do so.
  • The next set of code is for the generation of the GCAT boxplots for the wild-type data.
x0<-tapply(MAG$A[,1],as.factor(MAG$genes$ID),mean)
y0<-length(MAG$A[1,])
x1<-length(x0)
AAG<-matrix(nrow=x1,ncol=y0)
for(i in 1:y0) {AAG[,i]<-tapply(MAG$A[,i],as.factor(MAG$genes$ID),mean)}
par(mfrow=c(3,3))
for(i in 1:y0) {plot(AAG[,i],MG2[,i],main=chips[i],xlab='A',ylab='M',ylim=c(-5,5),xlim=c(0,15))}
browser()
  • Maximize the window in which the graphs have appeared. Save the graphs as a JPEG (File>Save As>JPEG>100% quality...). Once the graphs have been saved, close the window. To continue with the rest of the code, press Enter.
par(mfrow=c(1,3))
boxplot(MCD,main="Before Normalization",ylab='Log Fold Change',ylim=c(-5,5),xaxt='n')
axis(1,at=xy.coords(chips)$x,tick=TRUE,labels=FALSE)
text(xy.coords(chips)$x-1,par('usr')[3]-0.6,labels=chips,srt=45,cex=0.9,xpd=TRUE)
boxplot(MG2,main='After Within Array Normalization',ylab='Log Fold Change',ylim=c(-5,5),xaxt='n')
axis(1,at=xy.coords(chips)$x,labels=FALSE)
text(xy.coords(chips)$x-1,par('usr')[3]-0.6,labels=chips,srt=45,cex=0.9,xpd=TRUE)
boxplot(MAD[,Gtop$MasterList],main='After Between Array Normalization',ylab='Log Fold Change',ylim=c(-5,5),xaxt='n')
axis(1, at=xy.coords(chips)$x,labels=FALSE)
text(xy.coords(chips)$x-1,par('usr')[3]-0.6,labels=chips,srt=45,cex=0.9,xpd=TRUE)
  • Maximize the window in which the plots have appeared. You may not want to actually maximize them because you might lose the labels on the x axis, but make them as large as you can. Save the plots as a JPEG (File>Save As>JPEG>100% quality...). Once the graphs have been saved, close the window.

Create MA Plots and Box Plots for the Ontario Chips

  • Input the following code, line by line, into the main R window. Press the enter key after each block of code.
Ontario.GeneList<-RGO$genes$Name
lr<-log2((RGO$R-RGO$Rb)/(RGO$G-RGO$Gb))
  • Warning message: "NaNs produced" is OK.
z0<-length(lr[1,])
v0<-tapply(lr[,1],as.factor(Ontario.GeneList),mean)
z1<-length(v0)
MT<-matrix(nrow=z1,ncol=z0)
for(i in 1:z0) {MT[,i]<-tapply(lr[,i],as.factor(Ontario.GeneList),mean)}
MI<-matrix(nrow=z1,ncol=z0)
for(i in 1:z0) {MI[,i]<-ds[i]*MT[,i]}
MID<-as.data.frame(MI)
colnames(MID)<-headers
rownames(MID)<-ontID
ln<-(1/2*log2((RGO$R-RGO$Rb)*(RGO$G-RGO$Gb)))
  • Warning messages are OK:
1: In (RGO$R - RGO$Rb) * (RGO$G - RGO$Gb) :
NAs produced by integer overflow
2: NaNs produced
z2<-length(ln[1,])
zi<-tapply(ln[,1],as.factor(Ontario.GeneList),mean)
z3<-length(zi)
AO<-matrix(nrow=z3,ncol=z2)
for(i in 1:z0) {AO[,i]<-tapply(ln[,i],as.factor(Ontario.GeneList),mean)}
strains<-c('wt','dCIN5','dGLN3','dHAP4','dHMO1','dSWI4','dZAP1','Spar')
  • After entering the call browser() below, maximize the window in which the graphs have appeared. Save the graphs as a JPEG (File>Save As>JPEG>100% quality...). Once the graphs have been saved, close the window and press Enter for the next set of graphs to appear.
    • The last graph to appear will be the spar graphs.
    • The graphs generated from this code are the before Ontario chips
  • Be sure to save the 8 graphs before moving on to the next step
for (i in 1:length(strains)) {
  st<-strains[i]
  lt<-which(Otargets$Strain %in% st)
  if (st=='wt') {
      par(mfrow=c(3,5))
  } else {
      par(mfrow=c(4,5))
  }
  for (i in lt) {
    plot(AO[,i],MI[,i],main=headers[i],xlab="A",ylab="M",ylim=c(-5,5),xlim=c(0,15))
  }
  browser()
} 
  • To continue generating plots, press enter.
j0<-tapply(MAO$A[,1],as.factor(MAO$genes[,5]),mean)
k0<-length(MAO$A[1,])
j1<-length(j0)
AAO<-matrix(nrow=j1,ncol=k0)
for(i in 1:k0) {AAO[,i]<-tapply(MAO$A[,i],as.factor(MAO$genes[,5]),mean)}
  • Remember, that after entering the call readline('Press Enter to continue'), maximize the window in which the graphs have appeared. Save the graphs as a JPEG (File>Save As>JPEG>100% quality...). Once the graphs have been saved, close the window and press Enter for the next set of graphs to appear.
    • Again, the last graphs to appear will be the spar graphs.
    • These graphs that are produced are for the after Ontario chips
  • Again, be sure to save 8 graphs before moving on to the next part of the code.
for (i in 1:length(strains)) {
  st<-strains[i]
  lt<-which(Otargets$Strain %in% st)
  if (st=='wt') {
      par(mfrow=c(3,5))
  } else {
      par(mfrow=c(4,5))
  }
  for (i in lt) {
    plot(AAO[,i],MD2[,i],main=headers[i],xlab="A",ylab="M",ylim=c(-5,5),xlim=c(0,15))
  }
  browser()
}
  • To continue generating plots, press enter.
for (i in 1:length(strains)) {
  par(mfrow=c(1,3))
  st<-strains[i]
  lt<-which(Otargets$Strain %in% st)
  if (st=='wt') {
      xcoord<-xy.coords(lt)$x-1
      fsize<-0.9
  } else {
      xcoord<-xy.coords(lt)$x-1.7
      fsize<-0.8
  }
  boxplot(MID[,lt],main='Before Normalization',ylab='Log Fold Change',ylim=c(-5,5),xaxt='n')
  axis(1,at=xy.coords(lt)$x,labels=FALSE)
  text(xcoord,par('usr')[3]-0.65,labels=headers[lt],srt=45,cex=fsize,xpd=TRUE)
  boxplot(MD2[,lt],main='After Within Array Normalization',ylab='Log Fold Change',ylim=c(-5,5),xaxt='n')
  axis(1,at=xy.coords(lt)$x,labels=FALSE)
  text(xcoord,par('usr')[3]-0.65,labels=headers[lt],srt=45,cex=fsize,xpd=TRUE)
  ft<-Otargets$MasterList[which(Otargets$Strain %in% st)]
  boxplot(MAD[,ft],main='After Between Array Normalization',ylab='Log Fold Change',ylim=c(-5,5),xaxt='n')
  axis(1,at=xy.coords(lt)$x,labels=FALSE)
  text(xcoord,par('usr')[3]-0.65,labels=headers[lt],srt=45,cex=fsize,xpd=TRUE)
  browser()
} 
  • To continue generating the box plots, press enter.
    • You will have to save 8 plots before you have completed the procedure. The last box plot is for spar.
  • Warnings are OK.
  • Zip the files of the plots together and upload to LionShare and/or save to a flash drive.



When doing the transformation of the data, I replaced 477 #VALUE! with a single space.
Statistical Analysis

  • For the statistical analysis, we will begin with the file "GCAT_and_Ontario_Final_Normalized_Data.csv" that you generated in the previous step.
  • Open this file in Excel and Save As an Excel Workbook *.xlsx. It is a good idea to add your initials and the date (yyyymmdd) to the filename as well.
  • Rename the worksheet with the data "Compiled_Normalized_Data".
    • Type the header "ID" in cell A1.
    • Insert a new column after column A and name it "Standard Name". Column B will contain the common names for the genes on the microarray.
      • Copy the entire column of IDs from Column A.
      • Paste the names into the "Value" field of the ORF List <-> Gene List tool in YEASTRACT. Then, click on the "Transform" button.
      • Select all of the names in the "Gene Name" column of the resulting table.
      • Copy and paste these names into column B of the *.xlsx file. Save your work.
    • Insert a new column on the very left and name it "MasterIndex". We will create a numerical index of genes so that we can always sort them back into the same order.
      • Type a "1" in cell A2 and a "2" in cell A3.
      • Select both cells. Hover your mouse over the bottom-right corner of the selection until it makes a thin black + sign. Double-click on the + sign to fill the entire column with a series of numbers from 1 to 6189 (the number of genes on the microarray).
  • Insert a new worksheet and call it "Rounded_Normalized_Data". We are going to round the normalization results to four decimal places because of slight variations seen in different runs of the normalization script.
    • Copy the first three columns of the "Compiled_Normalized_Data" sheet and paste it into the first three columns of the "Rounded_Normalized_Data" sheet.
    • Copy the first row of the "Compiled_Normalized_Data" sheet and paste it into the first row of the "Rounded_Normalized_Data" sheet.
    • In cell C2, type the equation =ROUND(Compiled_Normalized_Data!C2,4).
    • Copy and paste this equation in the rest of the cells of row 2.
    • Select all of the cells of row 2 and hover your mouse over the bottom right corner of the selection. When the cursor changes to a thin black "plus" sign, double-click on it to paste the equation to all the rows in the worksheet. Save your work.
  • Insert a new worksheet and call it "Master_Sheet".
    • Go back to the "Rounded_Normalized_Data" sheet and Select All and Copy.
    • Click on cell A1 of the "Master_Sheet" worksheet. Select Paste special > Paste values to paste the values, but not the formulas from the previous sheet. Save your work.
    • There will be some #VALUE! errors in cells where there was missing data for genes that existed on the Ontario chips, but not the GCAT chips.
      • Select the menu item Find/Replace and Find all cells with "#VALUE!" and replace them with a single space character. Record how many replacements were made to your electronic lab notebook. Save your work.
  • This will be the starting point for our statistical analysis below.

Creating the Worksheet

  1. Create a new worksheet, naming it either "(STRAIN)_ANOVA" as appropriate. For example, you might call yours "wt_ANOVA"
  2. Copy all of the data from the "Master_Sheet" worksheet for your strain 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 (STRAIN)_AvgLogFC_(TIME) where (STRAIN) is your strain designation and (TIME) is 15, 30, etc.
  4. In the cell below the (STRAIN)_AvgLogFC_t15 header, type =AVERAGE(
  5. Then highlight all the data in row 2 associated with (STRAIN) 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 (STRAIN)_AvgLogFC_t120 calculation, create the column header (STRAIN)_ss_HO.
  10. In the first cell below this header, type =SUMSQ(
  11. Highlight all the LogFC data in row 2 for your (STRAIN) (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 (STRAIN)_ss_HO, create the column headers (STRAIN)_ss_(TIME) as in (3).
  13. Make a note of how many data points you have at each time point for your strain. For most of the strains, it will be 4, but for dHAP4 t90 or t120, it will be "3", and for the wild type it will be "4" or "5". Count carefully. Also, make a note of the total number of data points. Again, for most strains, this will be 20, but for example, dHAP4, this number will be 18, and for wt it should be 23 (double-check).
  14. In the first cell below the header (STRAIN)_ss_t15, type =SUMSQ(<range of cells for logFC_t15>)-<number of data points>*<AvgLogFC_t15>^2 and hit enter.
    • The phrase <range of cells for logFC_t15> should be replaced by the data range associated with t15.
    • The phrase <number of data points> should be replaced by the number of data points for that timepoint (either 3, 4, or 5).
    • 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 (STRAIN)_ss_t120, create the column header (STRAIN)_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 (STRAIN)_Fstat and (STRAIN)_p-value.
  19. Recall the number of data points from (13): call that total n.
  20. In the first cell of the (STRAIN)_Fstat column, type =((n-5)/5)*(<(STRAIN)_ss_HO>-<(STRAIN)_SS_full>)/<(STRAIN)_SS_full> and hit enter.
    • Don't actually type the n but instead use the number from (13). Also note that "5" is the number of timepoints and the dSWI4 strain has 4 timepoints (it is missing t15).
    • Replace the phrase (STRAIN)_ss_HO with the cell designation.
    • Replace the phrase <(STRAIN)_SS_full> with the cell designation.
    • Copy to the whole column.
  21. In the first cell below the (STRAIN)_p-value header, type =FDIST(<(STRAIN)_Fstat>,5,n-5) replacing the phrase <(STRAIN)_Fstat> with the cell designation and the "n" as in (13) with the number of data points total. (Again, note that the number of timepoints is actually "4" for the dSWI4 strain). 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 (STRAIN)_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.

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, (STRAIN)_Bonferroni_p-value.
  2. Type the equation =<(STRAIN)_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 (STRAIN)_Bonferroni_p-value header: =IF(r2>1,1,r2). 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 "(STRAIN)_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 C, 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 (STRAIN)_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 "STRAIN_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.
  • Upload the .xlsx file that you have just created to LionShare. Send Dr. Dahlquist an e-mail with the link to the file (e-mail kdahlquist at lmu dot edu).
  • Anu and I were assigned the wild-type strain to analyze statistically
  • The following were for numbers for our data:
    • T15: 4
    • T30: 5
    • T60: 4
    • T90: 5
    • T120: 5
    • We had a total of 23 chips
  • We had to adjust our values to account for the various blanks in the data for the various times. These adjustments were made to make sure that those specific genes had their data corrected for those blank time points.
    • 53 genes were missing data points
    • For T15, there was 1 blank
    • For T30, 60, 90, 120, there were 2 blanks per time point

Sanity Check: Number of genes significantly changed

  • 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)?
      • 2377 (38.41%)
    • How many genes have p < 0.01? and what is the percentage (out of 6189)?
      • 1531 (24.74%)
    • How many genes have p < 0.001? and what is the percentage (out of 6189)?
      • 850 (13.73%)
    • How many genes have p < 0.0001? and what is the percentage (out of 6189)?
      • 449 (7.25%)
    • How many genes are p < 0.05 for the Bonferroni-corrected p value? and what is the percentage (out of 6189)?
      • 226 (3.65%)
    • How many genes are p < 0.05 for the Benjamini and Hochberg-corrected p value? and what is the percentage (out of 6189)?
      • 1673 (31.88%)

To view the comparison of all the strains analyzed on this day, please download the following slide: ANOVA Test

May 19, 2015

Purpose: See May 18, 2015. Began with a T test as well as a between strain ANOVA to continue to obtain the final data that will be input into MATLAB.
Procedure:
Modified T test

  • Insert a new worksheet into your Excel workbook and name it "(STRAIN)_ttest", "wt_ttest"
  • Go back to the "Master_Sheet" worksheet for your strain. Copy the first three columns containing the "MasterIndex", "ID", and "Standard Name" from the "Master_Sheet" worksheet for your strain and paste it into your new worksheet. Copy the columns containing the data for your strain and paste it into your new worksheet.
  • Go to the empty columns to the right on your worksheet. Create new column headings in the top cells to label the average log fold changes that you will compute. Name them with the pattern <dHAP4>_<AvgLogFC>_<tx> where you use the appropriate text within the <> and where x is the time. For me: wt_AvgLFC_15 and so on for ever time point.
  • Compute the average log fold change for the replicates for each timepoint by typing the equation:
=AVERAGE(range of cells in the row for that timepoint)

into the second cell below the column heading. For example, your equation might read

=AVERAGE(C2:F2)

Copy this equation and paste it into the rest of the column.

  • Create the equation for the rest of the timepoints and paste it into their respective columns. Note that you can save yourself some time by completing the first equation for all of the averages and then copy and paste all the columns at once.
  • Go to the empty columns to the right on your worksheet. Create new column headings in the top cells to label the T statistic that you will compute. Name them with the pattern <dHAP4>_<Tstat>_<tx> where you use the appropriate text within the <> and where x is the time. For example, wt_Tstat_15 and so on for each time point. You will now compute a T statistic that tells you whether the normalized average log fold change is significantly different than 0 (no change in expression). Enter the equation into the second cell below the column heading:
=AVERAGE(range of cells)/(STDEV(range of cells)/SQRT(number of replicates))

For example, your equation might read:

=AVERAGE(C2:F2)/(STDEV(C2:F2)/SQRT(4))

(NOTE: in this case the number of replicates is 4. Be careful that you are using the correct number of parentheses.) Copy the equation and paste it into all rows in that column. Create the equation for the rest of the timepoints and paste it into their respective columns. Note that you can save yourself some time by completing the first equation for all of the T statistics and then copy and paste all the columns at once.

  • Go to the empty columns to the right on your worksheet. Create new column headings in the top cells to label the P value that you will compute. Name them with the pattern <dHAP4>_<Pval>_<tx> where you use the appropriate text within the <> and where x is the time. For example, "dHAP4_Pval_t15". In the cell below the label, enter the equation:
=TDIST(ABS(cell containing T statistic),degrees of freedom,2)

For example, your equation might read:

=TDIST(ABS(AE2),3,2)
  • The number of degrees of freedom is the number of replicates minus one. Copy the equation and paste it into all rows in that column.
    • The degrees of freedom used were 3 and 4
  • As with the ANOVA, we encounter the multiple testing problem here as well.
  • P-values less than 0.05
    • T15: 1078
      • The adjust p-value: 1075
    • T30: 1600
      • The adjusted p-value: 1587
    • T60: 1837
      • The adjusted p-value: 1814
    • T90: 759
      • The adjusted p-value: 749
    • T120: 513
      • The adjusted p-value: 509
  • Adjustments had to be made to the genes that had missing data points. A total of 53 had blanks.
    • The square roots for the Tstat were changed: 15 was altered to 3; 30 was altered to 3; 60 had 2; 90 was altered to 3 and 120 was altered to 3
    • The Degrees of Freedom were changed to figure out the p-values: 15, 30, 90, and 120 all had 2 while 60 had 1.

Bonferroni Correction

  1. Now we will perform adjustments to the p value to correct for the multiple testing problem. Label the columns to the right with the label, (STRAIN)_Bonferroni-Pval_tx (do this twice in a row).
  2. Type the equation =<(STRAIN)_Pval_tx>*6189, Upon completion of this single computation, use the 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 (STRAIN)_Bonferroni-Pval_tx header: =IF(r2>1,1,r2). Use the trick to copy the formula throughout the column.within-strain ANOVA.
  • The resulting p-values less than 0.05
    • T15: 0
    • T30: 1
    • T60: 0
    • T90: 0
    • T120: 1

Benjamini & Hochberg Correction

  1. Insert a new worksheet named "(STRAIN)_ttest_B-H". You will need to perform the procedure below for the p values for each timepoint. Do them individually one at a time to avoid confusion.
  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 the first timepoint from your ttest 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 C, 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 (STRAIN)_B-H_Pval_tx 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 "STRAIN_B-H_Pval_tx" 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 ttest sheet.
  • The Resulting P-values less than 0.05
    • T15: 0
    • T30: 85
    • T60: 0
    • T90: 0
    • T120: 1


Between Strain ANOVA

The detailed description of how this is done can be found on this page. A brief version of the protocol appears below.

  • All two strain comparisons were performed in MATLAB using the script Two_strain_compare_corrected_20140813_3pm.zip (within a zip file):
    • Download the zipped script file, extract it to the folder that contains your Excel file with the worksheet named "Master_Sheet". (The script and Excel file must be in the same folder to work.)
    • Launch MATLAB version 2014b.
    • In MATLAB, you will need to navigate to the folder containing the script and the Excel file.
      • Near the top of the page, you will see a a field that contains the path to the working directory. Just to the left of it, there is an icon that looks like a folder opening with a green down arrow. Click on this icon to open a dialog box where you can choose your folder containing the script and Excel file.
      • Once you have selected your folder, the left-hand pane should display the contents of that folder. To open the MATLAB script, you can double-click on it from that pane. The code for the script will appear in the center pane.
  • You will need to make a few edits to the code, depending on which strain comparison you want to make.
    • For the first block of code, the user must input the name of the Excel file (*.xls or .xlsx) to be imported as the variable "filename", the sheet from which the data will be imported as the variable "sheetname", and the two strains that will be compared as the variables "strain1" and "strain2".
      • MATLAB will read either .xls or .xlsx
      • Also note that this script will not work for any comparison involving dSWI4 because it has been hard-coded to expect 5 timepoints instead of 4.
  • The user does not have to modify any of the code from here on.
  • The next two lines of code ask the user whether or not they would like to see plots for each gene with an unadjusted p-value < 0.05. If the user does want to see these plots, they enter "1". If they would not like to see these plots, the user enters "0". When prompted, enter a "1" to see the plots displayed.

I ran the wild-type vs. Spar, which is for S. paradoxus. S. paradoxus was the yeast strain that I studied for a class assignment in BIOL398-04/S15.

  • p<0.05 >> 1498 genes
  • B&H p<0.05 >> 703 genes


Sanity Check
To view the comparisons of the individual time points against each other, please view the following powerpoint. This powerpoint contains the slide from yesterday as well as the T test analysis done today.
Statistical Analysis Powerpoint

Clustering the Genes with STEM

  1. Prepare your microarray data file for loading into STEM.
    • Insert a new worksheet into your Excel workbook, and name it "(STRAIN)_stem".
    • Select all of the data from your "(STRAIN)_ANOVA" worksheet and Paste special > paste values into your "(STRAIN)_stem" worksheet.
      • Your leftmost column should have the column header "MasterIndex". Rename this column to "SPOT". Column B should be named "ID". Rename this column to "Gene Symbol". Delete the column named "StandardName".
      • 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, wt_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, register, and download the stem.zip 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, select Saccharomyces cerevisiae (SGD), from the drop-down menu for Gene Annotation Source. Select No cross references, from the Cross Reference Source drop-down menu. Select No Gene Locations from the Gene Location Source drop-down menu.
    3. 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.
    4. 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. Make your filename descriptive of the contents, e.g. "wt_profile#_genelist.txt", where you replace the number symbol with the actual profile number.
      • 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. Make your filename descriptive of the contents, e.g. "wt_profile#_GOlist.txt", where you use "wt", "dGLN3", etc. to indicate the dataset and 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!
  5. Analyzing and Interpreting STEM Results
    1. Select one of the profiles you saved in the previous step for further intepretation of the data. I suggest that you choose one that has a pattern of up- or down-regulated genes at the early (first three) timepoints. You and your partner will choose the same profile so that you can compare your results between the two strains. Answer the following:
      • Why did you select this profile? In other words, why was it interesting to you?
      • How many genes belong to this profile?
      • How many genes were expected to belong to this profile?
      • 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.
      • 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?
      • Select 10 Gene Ontology terms from your filtered list (either p < 0.05 or corrected p < 0.05).
        • Since you and your partner are going to compare the results from each strain for the same cluster, you can either:
          • Choose the same 10 terms that are in common between strains.
          • Choose 10 terms that are different between the strains (5 or so from each).
          • Choose some that are the same and some that are different.
        • Look up the definitions for each of the terms at http://geneontology.org. For your final lab report, 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 HAP4 being deleted?
        • To easily look up the definitions, go to http://geneontology.org.
        • Copy and paste the GO ID (e.g. GO:0044848) into the search field at the upper left of the page called "Search GO Data".
        • 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.


May 20, 2015

Purpose: See May 18. Continuation of using the results from the statistical analysis to obtain the data that will be further analyzed to construct a GRN as well as run the model in MATLAB .
Procedure
GenMAPP & MAPP Finder
Preparing the GenMAPP Input File

  • Insert a new worksheet and name it STRAIN_GenMAPP.
  • Go back to the "ANOVA" worksheet for your strain and Select All and Copy.
  • Go to your new sheet and click on cell A1 and select Paste Special, click on the Values radio button, and click OK.
    • Delete the columns containing the "ss" calculations, just retaining the individual log fold change data, the average log fold change data, and the p values. For the Bonferroni and B&H p values, just keep the one column where we replaced all values > 1 with 1.
  • Now go to your "_ttest" worksheet. Copy just the columns containing the P values for the individual timepoints and Paste special > Paste values into your GenMAPP worksheet to the right of the previous data. For the Bonferroni and B&H p values, just keep one column where we replaced all values > 1 with 1.
  • It will be useful if we arrange the columns in a slightly different order: all individual log fold change data, then the ANOVA p values, then the AvgLogFC and p values for the individual timepoints clustered together (e.g., all t15 data together).
  • Select all of the columns containing Fold Changes. Select the menu item Format > Cells. Under the number tab, select 2 decimal places. Click OK.
  • Select all of the columns containing T statistics or P values. Select the menu item Format > Cells. Under the number tab, select 4 decimal places. Click OK.
  • We will now format this file for use with GenMAPP.
    • Currently, the "MasterIndex" column is the first column in the worksheet. We need the "ID" column to be the first column. Select Column B and Cut. Right-click on Cell A1 and select "Insert cut cells". This will reverse the position of the columns.
    • Insert a new empty column in Column B. Type "SystemCode" in the first cell and "D" in the second cell of this column. Use our trick to fill this entire column with "D".
    • Make sure to save this work as your .xlsx file. Now save this worksheet as a tab-delimited text file for use with GenMAPP in the next section.
  • Formatting
    • All the p-values were extended to have 4 decimal points
    • All the individual LFC time points as well as their averages had 2 decimal points

Running GenMAPP
Each time you launch GenMAPP, you need to make sure that the correct Gene Database (.gdb) is loaded.

  • Look in the lower left-hand corner of the window to see which Gene Database has been selected.
  • If you need to change the Gene Database, select Data > Choose Gene Database. Navigate to the directory C:\GenMAPP 2 Data\Gene Databases and choose the correct one for your species.
  • For the exercise today, if the yeast Gene Database is not present on your computer, you will need to download it. Click this link to download the yeast Gene Database.
  • Unzip the file and save it, Sc-Std_20060526.gdb, to the folder C:\GenMAPP 2 Data\Gene Databases.

GenMAPP Expression Dataset Manager Procedure

  • Launch the GenMAPP Program. Check to make sure the correct Gene Database is loaded.
  • Select the Data menu from the main Drafting Board window and choose Expression Dataset Manager from the drop-down list. The Expression Dataset Manager window will open.
  • Select New Dataset from the Expression Datasets menu. Select the tab-delimited text file that you formatted for GenMAPP (.txt) in the procedure above from the file dialog box that appears.
  • The Data Type Specification window will appear. GenMAPP is expecting that you are providing numerical data. If any of your columns has text (character) data, check the box next to the field (column) name.
    • The column StandardName has text data in it, but none of the rest do.
  • Allow the Expression Dataset Manager to convert your data.
    • This may take a few minutes depending on the size of the dataset and the computer’s memory and processor speed. When the process is complete, the converted dataset will be active in the Expression Dataset Manager window and the file will be saved in the same folder the raw data file was in, named the same except with a .gex extension; for example, MyExperiment.gex.
    • A message may appear saying that the Expression Dataset Manager could not convert one or more lines of data. Lines that generate an error during the conversion of a raw data file are not added to the Expression Dataset. Instead, an exception file is created. The exception file is given the same name as your raw data file with .EX before the extension (e.g., MyExperiment.EX.txt). The exception file will contain all of your raw data, with the addition of a column named ~Error~. This column contains either error messages or, if the program finds no errors, a single space character.
      • Record the number of errors in your lab notebook.
      • 97 errors were found in running the program. This result could be due to the fact that the gene was not found or matched in the database due to the age of the program.
  • Customize the new Expression Dataset by creating new Color Sets which contain the instructions to GenMAPP for displaying data on MAPPs.
    • Color Sets contain the instructions to GenMAPP for displaying data from an Expression Dataset on MAPPs. Create a Color Set by filling in the following different fields in the Color Set area of the Expression Dataset Manager: a name for the Color Set, the gene value, and the criteria that determine how a gene object is colored on the MAPP. Enter a name in the Color Set Name field that is 20 characters or fewer. You will have one Color Set per strain per time point.
    • The Gene Value is the data displayed next to the gene box on a MAPP. Select the column of data to be used as the Gene Value from the drop down list or select [none]. We will use "Avg_LogFC_" for the the appropriate time point.
    • Activate the Criteria Builder by clicking the New button.
    • Enter a name for the criterion in the Label in Legend field.
    • Choose a color for the criterion by left-clicking on the Color box. Choose a color from the Color window that appears and click OK.
    • State the criterion for color-coding a gene in the Criterion field.
      • A criterion is stated with relationships such as "this column greater than this value" or "that column less than or equal to that value". Individual relationships can be combined using as many ANDs and ORs as needed. A typical relationship is
[ColumnName] RelationalOperator Value

with the column name always enclosed in brackets and character values enclosed in single quotes. For example:

[Fold Change] >= 2
[p value] < 0.05
[Quality] = 'high'

This is the equivalent to queries that you performed on the command line when working with the PostgreSQL movie database. GenMAPP is using a graphical user interface (GUI) to help the user format the queries correctly. The easiest and safest way to create criteria is by choosing items from the Columns and Ops (operators) lists shown in the Criteria Builder. The Columns list contains all of the column headings from your Expression Dataset. To choose a column from the list, click on the column heading. It will appear at the location of the cursor in the Criterion box. The Criteria Builder surrounds the column names with brackets.

The Ops (operators) list contains the relational operators that may be used in the criteria: equals ( = ) greater than ( > ), less than ( < ), greater than or equal to ( >= ), less than or equal to ( <= ), is not equal to ( <> ). To choose an operator from the list, click on the symbol. It will appear at the location of the insertion bar (cursor) in the Criterion box. The Criteria Builder automatically surrounds the operators with spaces. The Ops list also contains the conjunctions AND and OR, which may be used to make compound criteria. For example:

[Fold Change] > 1.2 AND [p value] <= 0.05

Parentheses control the order of evaluation. Anything in parentheses is evaluated first. Parentheses may be nested. For example:

[Control Average] = 100 AND ([Exp1 Average] > 100 OR [Exp2 Average] > 100)

Column names may be used anywhere a value can, for example:

[Control Average] < [Experiment Average]
  • After completing a new criterion, add the criterion entry (label, criterion, and color) to the Criteria List by clicking the Add button.
    • For the yeast dataset, you will create two criterion for each Color Set. "Increased" will be [<strain>_Avg_LogFC_<timepoint>] > 0.25 AND [<strain>Pval_<timepoint>] < 0.05 and "Decreased will be [<strain>_Avg_LogFC_<timepoint>] < -0.25 AND [<strain>Pval_<timepoint>] < 0.05. Make sure that the increased and decreased average log fold change values match the timepoint of the Color Set.
    • You may continue to add criteria to the Color Set by using the previous steps.
      • The buttons to the right of the list represent actions that can be performed on individual criteria. To modify a criterion label, color, or the criterion itself, first select the criterion in the list by left-clicking on it, and then click the Edit button. This puts the selected criterion into the Criteria Builder to be modified. Click the Save button to save changes to the modified criterion; click the Add button to add it to the list as a separate criterion. To remove a criterion from the list, left-click on the criterion to select it, and then click on the Delete button. The order of Criteria in the list has significance to GenMAPP. When applying an Expression Dataset and Color Set to a MAPP, GenMAPP examines the expression data for a particular gene object and applies the color for the first criterion in the list that is true. Therefore, it is imperative that when criteria overlap the user put the most important or least inclusive criteria in the list first. To change the order of the criteria in the list, left-click on the criterion to select it and then click the Move Up or Move Down buttons. No criteria met and Not found are always the last two positions in the list.
    • These are the colors used:
      • Increased: Salmon/light red
      • Decreased: Light baby blue/cyan
  • You will also create a ColorSets to view the within-strain ANOVA p values for your strain, with criteria for viewing the unadjusted, Bonferroni-corrected, and B&H corrected p values.
    • These were the colors used for the ANOVA:
      • Bonferroni p-value: light pink
      • B&H p-value: light orange/tangerine
      • Unadjusted p-value: dirty yellow/pale yellow (so it's not too rough on the eyes)
  • Finally, you will create a ColorSet to view the between-strain ANOVA p values for your wt v. STRAIN comparison.
    • The colors used were:
      • B&H p-value: light orange/tangerine
      • Unadjusted p-value: dirty yellow/pale yellow
  • Save the entire Expression Dataset by selecting Save from the Expression Dataset menu. Changes made to a Color Set are not saved until you do this.
  • Exit the Expression Dataset Manager to view the Color Sets on a MAPP. Choose Exit from the Expression Dataset menu or click the close box in the upper right hand corner of the window.
  • Upload your .gex file to Lionshare and share it with Dr. Dahlquist. E-mail the link to the file to Dr. Dahlquist.
  • Click here to download a zipped set of MAPPs with which to view your Expression Dataset.

Genes that were shown to have significant changes in expression level when comparing wild-types S.cerevisiae and S. paradoxus

Orange Yellow
YAP1 YHP1
RTG3 YOX1
TBF1 MET28
PHD1 COM2
NRG1
  • Orange (their B&H p-values < 0.05) & Yellow (the unadjusted p-values < 0.05)
  • Further comments/observations about the comparison of wt vs. spar:
    • YHP1 is odd. Its expression level doesn't seem to increase or decrease with the specific criteria when looking at the time points, yet its overall unadjusted p-value is significant.
    • Only up-regulation: YAP1, RTG3, YOX1, PHD1,
    • Again in looking at TBF1, its expression level doesn't meet the criteria, but its B&H p-value is significant.
    • Only down-regulation: MET28, COM2
    • NRG2 initially up-regulated and then during recovery time it was down-regulated.

For Within Strain Which strain to hybridize microarrays:

  1. OPI1: did not have any significance
  2. Unadjusted:
    • INO2: p-value of 0.018
  3. B&H:
    • YAP1: p-value of 0.0001
  4. Bonferroni:
    • None from list

To test for growth impairment in cold using the Within Strain ANOVA

    • ARG80, TBF1, YHP1, and NRG1 did not have any significant values
  1. Unadjusted:
    • None here
  2. B&H:
    • RSF2: p-value of 0.0126
    • RTG3: p-value of 0.0062
    • YOX1: p-value of 0.0004
    • PHD1: p-value of 0.0001
  3. Bonferroni:
    • None here

Yeastract
Using YEASTRACT to Infer which Transcription Factors Regulate a Cluster of Genes
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"?
    • List the "significant" transcription factors on your wiki page, along with the corresponding "% in user set", "% in YEASTRACT", and "p value".
      • To view this list, please download the following Excel Workbook: Significant TFs Workbook
      • Are CIN5, GLN3, HAP4, HMO1, SWI4, and ZAP1 on the list?
        • Profile #45: None of those were identified
        • Profile #22: CIN5, HAP4, SWI4, ZAP1 were seen
        • Profile #9: None of those were identified
        • Profile #28: None of those were identified
        • Profile #48: YEASTRACT did not identify any of the genes on this list as significant with its p-value criterion.
  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 15-30 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 CIN5, GLN3, HAP4, HMO1, SWI4, 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.
    • 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 CIN5, HAP4, GLN3, HMO1, SWI4, and ZAP1) into both the "Transcription factors" field and the "Target ORF/Genes" field.
    • We are going to generate several regulation matrices, with different "Regulations Filter" options.
      • For the first one, accept the defaults: "Documented", "DNA binding plus expression 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.
      • Repeat these steps to generate a second regulation matrix, this time applying the Regulations Filter "Documented", "Only DNA binding evidence".
      • Repeat these steps a third time to generate a third regulation matrix, this time applying the Regulations Filter "Documented", DNA binding and expression evidence".
May 25, 2015

Purpose: Creating the Input sheet to be run in MATLAB
Procedure:

  1. My file was similar to the file "21-genes_50-edges_Dahlquist-data_Sigmoid_estimation.xls", but with your expression data and network. You should download this file, change the name, and edit it to include your data. Make sure to give it a meaningful filename that includes your last name or initials. Click this link to download the sample file from the GRNmap GitHub repository.)
  2. The first thing you need to do is determine the transcription factors that you are including in your network. You are going to use the "transposed" Regulation Matrix that you generated from YEASTRACT in the previous section.
    • Copy the transposed matrix from your "network" sheet and paste it into the worksheets called "network" and "network_weights".
    • Note that the transcription factor names have to be in the same order and same format across the top row and first column. CIN5 does not match Cin5p, so the latter will need to be changed to CIN5 if you have not already done so.
    • It may be easier for you if you put the transcription factors in alphabetical order (using the sort feature in Excel), but whether you leave your list the same as it is from the YEASTRACT assignment or in alphabetical order, make sure it is the same order for all of the worksheets.
  3. The next worksheet to edit is the one called "degradation_rates".
    • Paste your list of transcription factors from your "network" sheet into the column named "StandardName". You will need to look up the "SystematicName" of your genes. YEASTRACT has a feature that will allow you to paste your list of standard names in to retrieve the systematic names here.
    • Next, you will need to look up the degradation rates for your list of transcription factors. These rates have been calculated from protein half-life data from a paper by Belle et al. (2006). Look up the rates for your transcription factors from this file and include them in your "degradation_rates" worksheet.
    • If a transcription factor does not appear in the file above, use the value "0.027182242" for the degradation rate.
  4. The next worksheet to edit is the one called "production_rates".
    • Paste the "SystematicName" and "StandardName" columns from your "degradation_rates" sheet into the "production_rates" sheet.
    • The initial guesses for the production rates we are using for the model are two times the degradation rate. Compute these values from your degradation rates and paste the values into the column titled "ProductionRate".
  5. I then inputted two sheets that held the wild type information and the S. paradoxus (spar) information. S. paradoxus is a different species of yeast that has been known to do well in the cold.
    • Put the wild type data in the sheet called "wt".
    • The sample spreadsheet has a worksheet named "dcin5", which I changed to spar. The instructions below should be followed for each strain sheet.
    • Paste the SystematicName and StandardName columns from one of your previous sheets into this one.
    • This data in this sheet is the Log Fold Changes for each replicate and each timepoint from the "Rounded_Normalized_Data" worksheet from the big Excel workbook in which you computed the statistics. We are only going to use the cold shock timepoints for the modeling. Thus your column headings for the data should be "15", "30", and "60". There will be multiple columns for each timepoint (typically 4) to represent the replicate data, but they will all have the same name. For example, you may have four columns with the header "15".
    • Copy and paste the data from your spreadsheet into this one. You need to include only the data for the genes in your network. Make sure that the genes are in the same order as in the other sheets.
  6. The "optimization_parameters" worksheet should have the following values:
    • alpha should be 0.01
    • kk_max should be 1
    • MaxIter should be 1e08 (one hundred million in plain English)
    • TolFun should be 1e-6
    • MaxFunEval should be 1e08 (one hundred million in plain English)
    • TolX should be 1e-6
    • Sigmoid should be 1
    • estimateParams should be 1
    • makeGraphs should be 1
    • fix_P should be 0
    • fix_b should be 1
    • For the parameter "time" (Cell A13), we should have "15", "30", and "60", since these are the timepoints we have in our data.
    • For the parameter "Strain" (Cell A14), replace "dcin5" with the name of the second strain you are using, making sure that the capitalizaiton and spelling is the same as what you named the worksheet containing that strain's expression data. We are only going to compare two strains, so you can delete the other strain information.
    • For the parameter "Sheet" (Cell A15), give the number of the worksheet from left to right that your "Strain" log2 expression data is in. Delete any extra numbers because we are only comparing two strains.
  7. For the parameter "Deletion", leave the zero in cell B15 (corresponding to wt). In cell C15, put a number corresponding to the position in the list of gene names that the gene that was deleted appears. In the sample file, CIN5 is number 3 in the list. Note, disregard the column header in this count and only consider the actual gene names themselves.
    • For the parameter, "simtime", you perform the forward simulation of the expression in five minute increments from 0 to 60 minutes. Thus, this row should read: simtime should be 0, 5, <...fill by steps of 5...>, 60, each number in a different cell.
  8. The last sheet you will need to modify is called "network_b".
    • Paste in the list of standard names for your transcription factors from one of your previous sheets. Note that this sheet does not have a column for the Systematic Name.
    • Cell A1 in the sample files has the text "rows genes affected/cols genes controlling". I believe you can either have this text in cell A1 or "StandardName".
    • The "threshold" value for each gene should be "0".
  9. When you have completed the modifications to your file, upload it to LionShare and send Dr. Dahlquist an e-mail with a link to the file.
Appendix: Full explanation of the "optimization_parameters" sheet
alpha: Penalty term weighting (from an L-curve analysis)
kk_max: Number of times to re-run the optimization loop: in some cases re-starting the optimization loop can improve performance of the estimation.
MaxIter: Number of times MATLAB iterates through the optimization scheme. If this is set too low, MATLAB will stop before the parameters are optimized.
TolFun: How different two least squares evaluations should be before it says it's not making any improvement
MaxFunEval: maximum number of times it will evaluate the least squares cost
TolX: How close successive least squares cost evaluations should be before MATLAB determines that it is not making any improvement.
Sigmoid: =1 if sigmoidal model, =0 if Michaelis-Menten model
estimateParams: =1 if want to estimate parameters and =0 if the user wants to do just one forward run
makeGraphs: =1 to output graphs; =0 to not output graphs
fix_P: =1 if the user does not want to estimate the production rate, P, parameter, use initial guess and never change; =0 to estimate
fix_b: =1 if the user does not want to estimate the b parameter, use initial guess and never change; =0 to estimate
time: A row containing a list of the time points when the data was collected experimentally. Should correspond to the timepoint column headers in the expression sheets.
Strain: A row containing a list of all of the strains for which there is expression data in the workbook. Should correspond to the names of the sheets for each strain.
Sheet: A row where each entry is the order number of the sheet (left to right) that corresponds to the list of strains above.
Deletion: Gives the index of the gene in the network sheet that has been deleted in each strain listed above. For example, if data has been provided for the CIN5 deletion strain, then give the index number from the network sheet corresponding to CIN5.
simtime: A list of times for which the forward simulation should be evaluated.
May 26, 2015

Purpose: Running GRNmap and the GRNmodel in MATLAb
Procedure:

  1. Download the current version of GRNmap from GitHub. Version 1.0.6 can be downloaded by following this link.
    • For the sake of organization, save it into a new folder called "GRNmap" either on your Desktop or within your "Microarray Analysis" folder.
    • Unzip the file by right-clicking on it and choosing 7-zip > Extract here.
  2. Open the "GRNmap-1.0.6" folder and open the "matlab" subfolder. Double-click on the file "GRNmodel.m" to open GRNmap in MATLAB 2014b.
  3. Click on the green triangle "Run" button to run the model.
    • You will be prompted by an Open dialog to find your input file that you created in the previous section. Browse and select this input file and click OK.
    • Note that the Open dialog will default to show files of *.xlsx only. If your file is saved as *.xls, you will need to select the drop-down menu to show all files.
    • A window called "Figure 1" will appear. The counter is showing the number of iterations of the least squares optimization algorithm. The top plot is showing the values of all the parameters being estimated. You should see some movement of the diamonds each time the counter iterates.
  4. Once the model has completed its run, plots showing the expression over time for all of the genes in the network will appear. Since we selected "makeGraphs = 1" these will automatically be saved as *.jpg files in the same folder as your input file. Compile the figures into a single PowerPoint file. Please label things clearly, placing an appropriate number of graphs on each page for a readable visual. Take some care to make sure that the graphs are the same size and the aspect ratio has not been changed.
  5. Create a new workbook for analyzing the weight data. In this workbook, create a new sheet: call it estimated_weights. In this new worksheet, create a column of labels of the form ControllerGeneA -> TargetGeneB, replacing these generic names with the standard gene names for each regulatory pair in your network. Remember that columns represent Controllers and rows represent Targets in your network and network_weights sheets.
  6. Extract the non-zero optimized weights from their worksheet and put them in a single column next to the corresponding ControllerGeneA -> TargetGeneB label.
  7. Now we will run the model a second time, this time estimating the threshold parameters, b. Save the input workbook that you previously created as a new file with a meaningful name (e.g. append "estimate-b" to the previous filename), and change fix_b to 0 in the "optimization_parameters" worksheet, so that the thresholds will be estimated. Rerun GRNmodel with the new input sheet.
  8. Repeat Parts (4) through (6) with the new output.
  9. Create an empty excel workbook, and copy both sets of weights into a worksheet.
  10. Create a bar chart in order to compare the "fixed b" and "estimated b" weights.
  11. Create bar charts to compare the production rates from each run.
  12. Copy the two bar charts into your powerpoint.
  13. Visualize the output of each of your model runs with GRNsight.
    • In order for this to work, you need to alter your output workbook slightly. You need to change the name of the sheet called "out_network_optimized_weights" to "network_optimized_weights"; i.e., delete the "out_" from that sheet name.
    • Arrange the genes in the same order you used to display them previously when you visualized the networks from YEASTRACT for both of your model output runs. Take a screenshot of each of the results and paste it into your PowerPoint presentation. Clearly label which screenshot belongs to which run.
    • Note that GRNsight will display differently now that you have estimated the weights. For positive weights > 0, the edge will be given a regular (pointy) arrowhead to indicate an activation relationship between the two nodes. For negative weights < 0, the edge will be given a blunt arrowhead (a line segment perpendicular to the edge direction) to indicate a repression relationship between the two nodes. The thickness of the edge will vary based on the magnitude of the absolute value of the weight. Larger magnitudes will have thicker edges and smaller magnitudes will have thinner edges. The way that GRNsight determines the edge thickness is as follows. GRNsight divides all weight values by the absolute value of the maximum weight in the matrix to normalize all the values to between zero and 1. GRNsight then adjusts the thickness of the lines to vary continuously from the minimum thickness (for normalized weights near zero) to maximum thickness (normalized weights of 1). The color of the edge also imparts information about the regulatory relationship. Edges with positive normalized weight values from 0.05 to 1 are colored magenta; edges with negative normalized weight values from -0.05 to -1 are colored cyan. Edges with normalized weight values between -0.05 and 0.05 are colored grey to emphasize that their normalized magnitude is near zero and that they have a weak influence on the target gene.
  14. Upload your PowerPoint, your two input workbooks, and your two output workbooks and link to them in your individual journal. Also upload the workbook where you made the bar charts comparing the weights from both runs.
    • Interpret the results of the model simulation.
      • Examine the graphs that were output by each of the runs. Which genes in the model have the closest fit between the model data and actual data? Which genes have the worst fit between the model and actual data? Why do you think that is? (Hint: how many inputs do these genes have?) How does this help you to interpret the microarray data?
      • Which genes showed the largest dynamics over the timecourse? In other words, which genes had a log fold change that is different than zero at one or more timepoints. The p values from the Week 11 ANOVA analysis are informative here. Does this seem to have an effect on the goodness of fit (see question above)?
      • Which genes showed differences in dynamics between the wild type and the other strain your group is using? Does the model adequately capture these differences? Given the connections in your network (see the visualization in GRNsight), does this make sense? Why or why not?
      • Examine the bar charts comparing the weights and production rates between the two runs. Were there any major differences between the two runs? Why do you think that was? Given the connections in your network (see the visualization in GRNsight), does this make sense? Why or why not?
      • Finally, based on the results of your entire project, which transcription factors are most likely to regulate the cold shock response and why?
    • Based on these results, what future directions do you want to take?
May 27, 2015

Testing of GRNmap was done today. Please click this link: GRNmap Testing Report: Strain Run Comparisons 2015-05-27

  • It was noted that what was thought to be estimation of individual strains, excluding wt, were actually forward run simulations of the individual deletion strains
    • We were not able to compare the weights between the various individual strains due to this discrepancy. Each regulator had the same weight, which would be interesting if the parameters were set to estimate, but they were not.
  • Comparison of wild type data was done by looking at the two MATLAB versions - 2014a vs. 2014b to see if the outputs were the same
    • The outputs for the optimized threshold values and the network weights had negligible differences.
May 28, 2015

Continuation of testing of GRNmap. Follow this link from above.

Glenn Johnson-Grau came into the lab today for a workshop on searching the literature.

Testing of Non-One weights began today. To view the progress and results, follow this link: GRNmap Testing Report: Non-1 Initial Weight Guesses 2015-05-28

June 2015

June 1, 2015

Continuation of GRNmap testing. To view the progress and results, visit: GRNmap Testing Report: Non-1 Initial Weight Guesses 2015-05-28

June 2, 2015

Continuation of GRNmap testing. To view the progress and results, click: GRNmap Testing Report: Non-1 Initial Weight Guesses 2015-05-28

June 3, 2015

Continuation of GRNmap testing and analysis of the graphical outputs from running the code.
GRNmap Testing Report: Non-1 Initial Weight Guesses 2015-05-28

Presented today in Journal Club with K. McGee. To view our powerpoint, please click: Media:KM NW Journal Club CGRNds.pptx
Our paper was Geistlinger et al. (2013)

June 8, 2015

Analysis of all the runs we did last week will begin. We will analyze the individual strain outputs in comparison to its wt + dMutation. After those interpretations are completed, we will look at the three-strain run vs. the four-strain run vs. the all strain run on MATLAB.

June 9, 2015

We analyzed genes with no inputs and self-regulating genes with not other inputs. I focused on the self-regulating genes and their outputs to see if we can better understand how the model gives the results that we have seen. The analysis can be found at the bottom of the page of GRNmap Testing Report: Strain Run Comparisons 2015-05-27.
In addition to that analysis, I worked with MATLAB to have a grasp of what it does when giving the outputs for when more than one strain's data is used to obtain the parameters. I rewrote script that I had received from my Biomathematical modeling course in Spring 2015. The script I used called for the input of each individual strain's .mat file, which contains the optimized parameters and the equations/lines used to model that individual strain's data. I reran the script so that each individual strain's graphs were overlain on each other.
Results:
It appears that MATLAB uses all the data provided to find the optimized line for both runs rather than using the individual strain's data to model the data for the specific run. Instead of seeing distinct lines, an average of all the lines, excluding the specific deletion as well as slopes with great dynamical differences, are produced in the output figures. To see the results, please view the last slide of the powerpoint on the bottom page of GRNmap Testing Report: Strain Run Comparisons 2015-05-27

June 10, 2015

I am continuing to analyze self-regulating genes. However, these genes have at least one other regulator. I am looking at these because it seems that auto-regulation plays a role in the parameter values between different runs of the model in MATLAB.

June 15, 2015

Today, I will be classifying the genes by similar characteristics to better understanding of the results.

  • The labels are:
    1. No_Inputs
    2. Self_Reg_No_Others
    3. Self_Reg_Others
    4. No_Outputs

Another goal of mine is to look at the p-values of only controlling genes to see if their significance has an effect on the target genes.

June 16, 2015

I continued what I did yesterday with looking at the p-values and their relationship to the graphical outputs of the model.
I first looked at the wt controller p-values.

  • 9/14 had insignificant p-values and together they controlled 15/21 genes
  • In looking at the controllers, many of them had slight/minimal dynamics (either up- or down-regulated)
    • However, the genes that they regulated seemed to have greater dynamics
    • The dynamical change could have resulted from having more than one regulator, with one of the regulators have statistical significance
June 17, 2015

I continued to look at p-values and their relationship to the output figures. I am continuing with dGLN3 p-values of the regulators to see if that influences the target genes expression levels. To download the excel sheet with the data, please click: Media:20150617 NW Classification.xlsx
I also changed the characteristic categories. Instead of heaving No_Outputs, the labels were changed to the number of inputs the specific target gene has:

  • One_Input
  • Two_Inputs
  • Three_Inputs

With these new labels, I will look at the graphical outputs again to see if anything interesting comes up.
To download the documents that I have been taking notes on, please see the following:

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