Week 8 - Revision history

Kdahlquist: Undo revision 4860 by Imacarae (talk)undo the edits made by Ivy because she made them on the wrong pge

2020-04-22T17:41:21Z

Undo revision 4860 by Imacarae (talk)undo the edits made by Ivy because she made them on the wrong pge

Imacarae: /* Sanity Check: Number of genes significantly changed */ modified tense

2019-10-24T00:31:43Z

‎Sanity Check: Number of genes significantly changed: modified tense

Kdahlquist: /* Clustering and GO Term Enrichment with stem (part 2) */ add instructions to deal with div/0 errors

2019-10-22T23:38:40Z

‎Clustering and GO Term Enrichment with stem (part 2): add instructions to deal with div/0 errors

Kdahlquist: /* Data and Files */ comment out stem results files

2019-10-22T23:00:47Z

‎Data and Files: comment out stem results files

Kdahlquist: /* Clustering and GO Term Enrichment with stem (part 2) */ put in stopping point

2019-10-22T23:00:06Z

‎Clustering and GO Term Enrichment with stem (part 2): put in stopping point

Icrespin: /* Sanity Check: Number of genes significantly changed */ accidentally placed my answers

2019-10-22T22:21:44Z

‎Sanity Check: Number of genes significantly changed: accidentally placed my answers

Icrespin: /* Sanity Check: Number of genes significantly changed */ entered answers

2019-10-22T22:15:26Z

‎Sanity Check: Number of genes significantly changed: entered answers

Kdahlquist: /* Calculate the Bonferroni and p value Correction */ note about undoing calculations

2019-10-22T20:17:13Z

‎Calculate the Bonferroni and p value Correction: note about undoing calculations

Kdahlquist: /* Statistical Analysis Part 1: ANOVA */ undo filter

2019-10-22T20:16:37Z

‎Statistical Analysis Part 1: ANOVA: undo filter

Kdahlquist: /* Background */ add yeastract reference

2019-10-17T17:57:50Z

‎Background: add yeastract reference

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 ==== Sanity Check: Number of genes significantly changed ====
-* We went to our dCIN5_ANOVA worksheet.
+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.
-* We selected row 1 and selected the menu item Data > Filter > Autofilter.  Little drop-down arrows appeared at the top of each column. This will enabled us to filter the data according to criteria we set.
-* We click on the drop-down arrow for the unadjusted p value and set a criterion that filtered the data so that the p value is less than 0.05. These results are also reported in the slide.
+* Go to your (STRAIN)_ANOVA worksheet.
 ** '''''How many genes have p < 0.05?  and what is the percentage (out of 6189)?'''''
 ** '''''How many genes have p < 0.01? and what is the percentage (out of 6189)?'''''
 ** '''''How many genes have p < 0.001? and what is the percentage (out of 6189)?'''''
 ** '''''How many genes have p < 0.0001? and what is the percentage (out of 6189)?'''''
-** We created a new worksheet in our workbook to record the answers to these questions so that we can write a formula in Excel to automatically calculate the percentage for you.
+** Note that it is a good idea to create a new worksheet in your workbook to record the answers to these questions.  Then you can write a formula in Excel to automatically calculate the percentage for you.
-* When we used a p value cut-off of p < 0.05, what we are saying is that we would have seen a gene expression change that deviates this far from zero by chance less than 5% of the time.
+* 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, we filtered the data to determine the following:
+* We have just performed 6189 hypothesis tests.  Another way to state what we are seeing with p < 0.05 is that we would expect to see this a gene expression change for at least one of the timepoints by chance in about 5% of our tests, or 309 times.  Since we have more than 309 genes that pass this cut off, we know that some genes are significantly changed.  However, we don't know ''which'' ones.  To apply a more stringent criterion to our p values, we performed the Bonferroni and Benjamini and Hochberg corrections to these unadjusted p values.  The Bonferroni correction is very stringent.  The Benjamini-Hochberg correction is less stringent.  To see this relationship, filter your data to determine the following:
 ** '''''How many genes are p < 0.05 for the Bonferroni-corrected p value? and what is the percentage (out of 6189)?'''''
 ** '''''How many genes are p < 0.05 for the Benjamini and Hochberg-corrected p value? and what is the percentage (out of 6189)?'''''
 * In summary, the p value cut-off should not be thought of as some magical number at which data becomes "significant".  Instead, it is a moveable confidence level.  If we want to be very confident of our data, use a small p value cut-off.  If we are OK with being less confident about a gene expression change and want to include more genes in our analysis, we can use a larger p value cut-off.
-* We compared the numbers we get between the wild type strain and the other strains studied, organized as a table.  We used [[Media:BIOL367_F19_sample_p-value_slide.pptx | sample PowerPoint slide]] to see how our table should be formatted. ''''' We uploaded our slide to the wiki.'''''
+* We will compare the numbers we get between the wild type strain and the other strains studied, organized as a table.  Use this [[Media:BIOL367_F19_sample_p-value_slide.pptx | sample PowerPoint slide]] to see how your table should be formatted. '''''Upload your slide to the wiki.'''''
-** Since the wild type data is being analyzed by one of the groups in the class, it will be sufficient for this week to supply just the data for our strain. We will do the comparison with wild type at a later date.
+** Note that since the wild type data is being analyzed by one of the groups in the class, it will be sufficient for this week to supply just the data for your strain.  We will do the comparison with wild type at a later date.
 * Comparing results with known data:  the expression of the gene ''NSR1'' (ID: YGR159C)is known to be induced by cold shock. '''''Find ''NSR1'' in your dataset.  What is its unadjusted, Bonferroni-corrected, and B-H-corrected p values?  What is its average Log fold change at each of the timepoints in the experiment?'''''  Note that the average Log fold change is what we called "STRAIN)_AvgLogFC_(TIME)" in step 3 of the ANOVA analysis. Does ''NSR1'' change expression due to cold shock in this experiment?
 * For fun, find "your favorite gene" (from your [[Week 3]] assignment) in the 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?'''''  Does your favorite gene change expression due to cold shock in this experiment?

@@ Line 162: / Line 162: @@
 ==== 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.
+* We went to our dCIN5_ANOVA worksheet.
+* We selected row 1 and selected the menu item Data > Filter > Autofilter.  Little drop-down arrows appeared at the top of each column. This will enabled us to filter the data according to criteria we set.
-* Go to your (STRAIN)_ANOVA worksheet.
+* We click on the drop-down arrow for the unadjusted p value and set a criterion that filtered the data so that the p value is less than 0.05. These results are also reported in the slide.
 ** '''''How many genes have p < 0.05?  and what is the percentage (out of 6189)?'''''
 ** '''''How many genes have p < 0.01? and what is the percentage (out of 6189)?'''''
 ** '''''How many genes have p < 0.001? and what is the percentage (out of 6189)?'''''
 ** '''''How many genes have p < 0.0001? and what is the percentage (out of 6189)?'''''
-** Note that it is a good idea to create a new worksheet in your workbook to record the answers to these questions.  Then you can write a formula in Excel to automatically calculate the percentage for you.
+** We created a new worksheet in our workbook to record the answers to these questions so that we can write a formula in Excel to automatically calculate the percentage for you.
-* 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.
+* When we used a p value cut-off of p < 0.05, what we are saying is that we 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:
+* 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, we filtered the data to determine the following:
 ** '''''How many genes are p < 0.05 for the Bonferroni-corrected p value? and what is the percentage (out of 6189)?'''''
 ** '''''How many genes are p < 0.05 for the Benjamini and Hochberg-corrected p value? and what is the percentage (out of 6189)?'''''
 * In summary, the p value cut-off should not be thought of as some magical number at which data becomes "significant".  Instead, it is a moveable confidence level.  If we want to be very confident of our data, use a small p value cut-off.  If we are OK with being less confident about a gene expression change and want to include more genes in our analysis, we can use a larger p value cut-off.
-* We will compare the numbers we get between the wild type strain and the other strains studied, organized as a table.  Use this [[Media:BIOL367_F19_sample_p-value_slide.pptx | sample PowerPoint slide]] to see how your table should be formatted. '''''Upload your slide to the wiki.'''''
+* We compared the numbers we get between the wild type strain and the other strains studied, organized as a table.  We used [[Media:BIOL367_F19_sample_p-value_slide.pptx | sample PowerPoint slide]] to see how our table should be formatted. ''''' We uploaded our slide to the wiki.'''''
-** Note that since the wild type data is being analyzed by one of the groups in the class, it will be sufficient for this week to supply just the data for your strain.  We will do the comparison with wild type at a later date.
+** Since the wild type data is being analyzed by one of the groups in the class, it will be sufficient for this week to supply just the data for our strain. We will do the comparison with wild type at a later date.
 * Comparing results with known data:  the expression of the gene ''NSR1'' (ID: YGR159C)is known to be induced by cold shock. '''''Find ''NSR1'' in your dataset.  What is its unadjusted, Bonferroni-corrected, and B-H-corrected p values?  What is its average Log fold change at each of the timepoints in the experiment?'''''  Note that the average Log fold change is what we called "STRAIN)_AvgLogFC_(TIME)" in step 3 of the ANOVA analysis. Does ''NSR1'' change expression due to cold shock in this experiment?
 * For fun, find "your favorite gene" (from your [[Week 3]] assignment) in the 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?'''''  Does your favorite gene change expression due to cold shock in this experiment?

@@ Line 210: / Line 210: @@
 ## 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.
 ## In section 4 (Execute) click on the yellow Execute button to run STEM.
 ##* '''''This is the stopping point for the Week 8 assignment.'''''
 # '''Viewing and Saving STEM Results'''

@@ Line 242: / Line 242: @@
 * Your Excel workbook with all of your calculations.
 ** Note that you will be working with this workbook for the next week or two, adding computations to it.  Save the new versions to the wiki with the '''same filename'''.  The wiki will store each version of the file so you can always go back to a previous version, if need be.
-* Your PowerPoint slide with a summary table of p values, updated with the screenshots from the stem software.
+* Your PowerPoint slide with a summary table of p values.<!--, updated with the screenshots from the stem software.-->
 ** You will also be adding to the PowerPoint presentation during subsequent steps in the analysis.
 * The input .txt file that you used to run stem.
-* The zipped together genelist and GOlist files for each of your significant profiles.
+<!--* The zipped together genelist and GOlist files for each of your significant profiles.-->
 ==== Conclusion (Summary Paragraph) ====

@@ Line 210: / Line 210: @@
 ## 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.
 ## In section 4 (Execute) click on the yellow Execute button to run STEM.
 # '''Viewing and Saving STEM Results'''
 ## 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.

@@ Line 138: / Line 138: @@
 ==== Calculate the Bonferroni and p value Correction ====
 # Now we will perform adjustments to the p value to correct for the [https://xkcd.com/882/ multiple testing problem].  Label the next two columns to the right with the same label, (STRAIN)_Bonferroni_p-value.
 # Type the equation <code>=<(STRAIN)_p-value>*6189</code>, Upon completion of this single computation, use the Step (10) trick to copy the formula throughout the column.

@@ Line 134: / Line 134: @@
 #* 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 ====

@@ Line 60: / Line 60: @@
 #* We will use software called STEM for the clustering and mapping
 #* [https://academic.oup.com/bioinformatics/article/21/suppl_1/i159/203147 Ernst, J., & Bar-Joseph, Z. (2006). STEM: a tool for the analysis of short time series gene expression data. ''BMC bioinformatics'', ''7''(1), 191. DOI: 10.1093/bioinformatics/bti1022]
-# Identifying regulatory transcription factors responsible for observed changes in gene expression
+# Identifying regulatory transcription factors responsible for observed changes in gene expression (YEASTRACT)
 # Dynamical systems modeling of the gene regulatory network ([http://kdahlquist.github.io/GRNmap/ GRNmap])
 #* [https://link.springer.com/article/10.1007/s11538-015-0092-6 Dahlquist, K. D., Fitzpatrick, B. G., Camacho, E. T., Entzminger, S. D., & Wanner, N. C. (2015). Parameter estimation for gene regulatory networks from microarray data: cold shock response in ''Saccharomyces cerevisiae''. ''Bulletin of mathematical biology'', ''77''(8), 1457-1492. DOI: 10.1007/s11538-015-0092-6]
 # Viewing modeling results in [http://dondi.github.io/GRNsight/ GRNsight]
-#* [https://peerj.com/articles/cs-85/ Dahlquist, K. D., Dionisio, J. D. N., Fitzpatrick, B. G., Anguiano, N. A., Varshneya, A., Southwick, B. J., & Samdarshi, M. (2016). GRNsight: a web application and service for visualizing models of small-to medium-scale gene regulatory networks. PeerJ Computer Science, 2, e85. DOI: 10.7717/peerj-cs.85]
+#* [https://peerj.com/articles/cs-85/ Dahlquist, K. D., Dionisio, J. D. N., Fitzpatrick, B. G., Anguiano, N. A., Varshneya, A., Southwick, B. J., & Samdarshi, M. (2016). GRNsight: a web application and service for visualizing models of small-to medium-scale gene regulatory networks. ''PeerJ Computer Science'', ''2'', e85. DOI: 10.7717/peerj-cs.85]
 ==== Experimental Design and Getting Ready ====