Imacarae Week 8 - Revision history

Imacarae: /* Conclusion */ wrote paragraph

2019-10-24T06:52:01Z

‎Conclusion: wrote paragraph

Imacarae: /* Methods and Results */ added header

2019-10-24T06:48:33Z

‎Methods and Results: added header

Imacarae: /* Sanity Check: Number of genes significantly changed */ added statements

2019-10-24T06:47:49Z

‎Sanity Check: Number of genes significantly changed: added statements

Imacarae: /* Acknowledgments */ added statement

2019-10-24T06:42:02Z

‎Acknowledgments: added statement

Imacarae: /* Sanity Check: Number of genes significantly changed */ added NSR1 data

2019-10-24T06:40:30Z

‎Sanity Check: Number of genes significantly changed: added NSR1 data

Imacarae: /* Sanity Check: Number of genes significantly changed */ added favorite gene values

2019-10-24T06:32:09Z

‎Sanity Check: Number of genes significantly changed: added favorite gene values

Imacarae: pasted procedure from week7

2019-10-24T06:22:41Z

pasted procedure from week7

Imacarae: /* Clustering and GO Term Enrichment with stem (part 2) */ modified tense

2019-10-24T06:19:07Z

‎Clustering and GO Term Enrichment with stem (part 2): modified tense

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

2019-10-24T06:12:30Z

‎Sanity Check: Number of genes significantly changed: modified tense

Imacarae: /* Acknowledgments */ statement and signed

2019-10-24T01:42:48Z

‎Acknowledgments: statement and signed

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 ==Conclusion==
 ==Acknowledgments==
 *To [[User:Kdahlquist|Dr. Dahlquist]] for walking us through analyzing the data with Excel.

@@ Line 23: / Line 23: @@
 #*Saved original Excel file under original name: BIOL367_F19_microarray-data_dCIN5.
 #*Saved as a new name: BIOL367_F19_microarray-data_dCIN5_IM
 ==== Statistical Analysis Part 1: ANOVA ====

@@ Line 108: / Line 108: @@
 **::-t=90: -2.901
 **::-t=120: -0.9315
 * 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?
 **My Favorite Gene: YGL073
@@ Line 119: / Line 120: @@
 ***::-t=90: -0.0757
 ***::-t=120: 0.4227
 ==== Clustering and GO Term Enrichment with stem (part 2)====

@@ Line 160: / Line 160: @@
 ==Conclusion==
 ==Acknowledgments==
 *To [[User:Kdahlquist|Dr. Dahlquist]] for walking us through analyzing the data with Excel.
 *To my group members, [[User:Dmadere|DeLisa]], [[User:Msamdars|Mihir]], and [[User:Eyoung20|Emma]]. We met during class time to make sure that we had the right values and to help each other with specific steps of the procedure.
 *Except for what is noted above, this individual journal entry was completed by me and not copied from another source.

@@ Line 99: / Line 99: @@
 ** 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?
 **My Favorite Gene: YGL073
@@ Line 109: / Line 118: @@
 ***::-t=60: -1.500
 ***::-t=90: -0.0757
-***::-t=120: 0.422675
+***::-t=120: 0.4227
 ==== Clustering and GO Term Enrichment with stem (part 2)====

← Older revision		Revision as of 06:22, 24 October 2019
Line 7:		Line 7:

	==Methods and Results==		==Methods and Results==
		+	===Background, Week 7 procedure===
		+	#Open Excel spreadsheet for dCIN5 data:
		+	#*Opened the worksheet labeled "Master_Sheet_<dCIN5>".
		+	#*In this worksheet, each row contains the data for one gene (one spot on the microarray).
		+	#*The first column contains the "MasterIndex", which numbers all of the rows sequentially in the worksheet so that we can always use it to sort the genes into the order they were in when we started.
		+	#*The second column (labeled "ID") contains the Systematic Name (gene identifier) from the Saccharomyces Genome Database.
		+	#*The third column contains the Standard Name for each of the genes.
		+	#*Each subsequent column contains the log2 ratio of the red/green fluorescence from each microarray hybridized in the experiment, for each strain starting with wild type and proceeding in alphabetical order by strain deletion.
		+	#*Each of the column headings from the data begin with the experiment name ("wt" for wild type S. cerevisiae data, "dCIN5" for the Δcin5 data, etc.). "LogFC" stands for "Log2 Fold Change" which is the Log2 red/green ratio. The timepoints are designated as "t" followed by a number in minutes. Replicates are numbered as "-0", "-1", "-2", etc. after the timepoint.
		+	#*The timepoints are t15, t30, t60 (cold shock at 13°C) and t90 and t120 (cold shock at 13°C followed by 30 or 60 minutes of recovery at 30°C).
		+	#Counting replicates
		+	#*Strain: dCIN5
		+	#*There are 4 replicates for each timepoint in this strain.
		+	#Saving File:
		+	#*Saved original Excel file under original name: BIOL367_F19_microarray-data_dCIN5.
		+	#*Saved as a new name: BIOL367_F19_microarray-data_dCIN5_IM
	==== Statistical Analysis Part 1: ANOVA ====		==== Statistical Analysis Part 1: ANOVA ====

@@ Line 87: / Line 87: @@
 ==== Clustering and GO Term Enrichment with stem (part 2)====
-# '''Prepare your microarray data file for loading into STEM.'''
+# '''Preparing the microarray data file for loading into STEM.'''
-#* Insert a new worksheet into your Excel workbook, and name it "(STRAIN)_stem".
+#* We inserted a new worksheet into the Excel workbook and named it "dCIN5_stem".
-#* Select all of the data from your "(STRAIN)_ANOVA" worksheet and Paste special > paste values into your "(STRAIN)_stem" worksheet.
+#* We selected all of the data from the "dCIN5_ANOVA" worksheet and Paste special > paste values into our "dCIN5_stem" worksheet.
-#** Your leftmost column should have the column header "Master_Index".  Rename this column to "SPOT".  Column B should be named "ID".  Rename this column to "Gene Symbol".  Delete the column named "Standard_Name".
+#** The leftmost column should have the column header "Master_Index". We renamed this column to "SPOT". Column B should be named "ID" and we renamed this column to "Gene Symbol". Then we deleted the column named "Standard_Name".
-#** Filter the data on the B-H corrected p value to be > 0.05 (that's '''greater than''' in this case).
+#** We filtered 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.
+#*** Once the data has been filtered, we selected all of the rows (except for your header row) and deleted the rows by right-clicking and choosing "Delete Row" from the context menu.  Undo the filter.  This ensured 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.).
+#** We deleted 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.).
+#** We renamed 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.
+#** Then, we saved our work. We used ''Save As'' to save this spreadsheet as Text (Tab-delimited) (*.txt), and clicked OK to the warnings and close your file.
-#*** Note that you should turn on the file extensions if you have not already done so.
+#*** Note: we turned on the file extensions if necessary.
-# '''Now download and extract the STEM software.'''  [http://www.cs.cmu.edu/~jernst/stem/ Click here to go to the STEM web site].
+# '''Now we downloaded and extracted the STEM software.'''  [http://www.cs.cmu.edu/~jernst/stem/ Here].
 #* Click on the [http://www.sb.cs.cmu.edu/stem/stem.zip download link] and download the <code>stem.zip</code> 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''.
+#* We unzipped the file.
-#* This will create a folder called <code>stem</code>.
+#* This created a folder called <code>stem</code>.
-#** You now need to download the Gene Ontology and yeast GO annotations and place them in this folder.
+#** We downloaded the Gene Ontology and yeast GO annotations and place them in this folder.
 #** Click here to download the file [https://lmu.box.com/s/t8i5s1z1munrcfxzzs7nv7q2edsktxgl "gene_ontology.obo"].
 #** Click here to download the file [https://lmu.box.com/s/zlr1s8fjogfssa1wl59d5shyybtm1d49 "gene_association.sgd.gz"].
-#*Inside the folder, double-click on the <code>stem.jar</code> to launch the STEM program.
+#*Inside the folder, we double-clicked on the <code>stem.jar</code> to launch the STEM program.
 # '''Running STEM'''
-## 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.
+## In section 1 (Expression Data Info) of the the main STEM interface window, we clicked on the ''Browse...'' button to navigate to and selected our file.
-##* Click on the radio button ''No normalization/add 0''.
+##* Clicked on the radio button ''No normalization/add 0''.
-##* Check the box next to ''Spot IDs included in the data file''.[[Image:DCIN5_stem_IM.png|thumb|dCIN5 Stem Program View]]
+##* Checked the box next to ''Spot IDs included in the data file''.[[Image:DCIN5_stem_IM.png|thumb|dCIN5 Stem Program View]]
 ## In section 2 (Gene Info) of the main STEM interface window, leave the default selection for the three drop-down menu selections for Gene Annotation Source, Cross Reference Source, and Gene Location Source as "User provided".
-## Click the "Browse..." button to the right of the "Gene Annotation File" item.  Browse to your "stem" folder and select the file "gene_association.sgd.gz" and click Open.
+## Clicked the "Browse..." button to the right of the "Gene Annotation File" item.  Browse to our "stem" folder and select the file "gene_association.sgd.gz" and clicked Open.
 ## 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.
+## In section 4 (Execute) we clicked on the yellow Execute button to run STEM.
-##* If you get an error, there are some known reasons why stem might not work.  If you had #DIV/0! errors in your input file, it will cause problems. Re-open your file and open the Find/Replace dialog.  Search for #DIV/0!, but don't put anything in the replace field.  Click "Replace all" to remove the #DIV/0! errors.  Then save your file and try again with stem.
+##* If we got an error, there are some known reasons why stem might not work.  If we had #DIV/0! errors in our input file, it caused problems. We re-opened your file and opened the Find/Replace dialog, searched for #DIV/0!, but don't put anything in the replace field, and click "Replace all" to remove the #DIV/0! errors.  Then we saved our file and try again with stem.
-##* '''''This is the stopping point for the Week 8 assignment.'''''
+##* '''''This is where we stopped for the Week 8 assignment.'''''
 ==Data and Files==

@@ Line 65: / Line 65: @@
 ==== 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.
+Before we move on to further analysis of the data, we wanted to perform a more extensive sanity check to make sure that we performed our data analysis correctly. We found out the number of genes that are significantly changed at various p value cut-offs.
-* Go to your (STRAIN)_ANOVA worksheet.
+* We went to our dCIN5_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.
+* We selected row 1 and selected the menu item Data > Filter > Autofilter. Little drop-down arrows should appear at the top of each column. This will enables 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.
+* We clicked on the drop-down arrow for the unadjusted p value, and set a criterion that will filter your data so that the p value was be less than 0.05.
 ** '''''How many genes have p < 0.05?  and what is the percentage (out of 6189)?'''''
 ** '''''How many genes have p < 0.01? and what is the percentage (out of 6189)?'''''
 ** '''''How many genes have p < 0.001? and what is the percentage (out of 6189)?'''''
 ** '''''How many genes have p < 0.0001? and what is the percentage (out of 6189)?'''''
-** 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. Then, we wrote a formula in Excel to automatically calculate the percentage.
 * When we use a p value cut-off of p < 0.05, what we are saying is that you would have seen a gene expression change that deviates this far from zero by chance less than 5% of the time.
 * We have just performed 6189 hypothesis tests.  Another way to state what we are seeing with p < 0.05 is that we would expect to see this a gene expression change for at least one of the timepoints by chance in about 5% of our tests, or 309 times.  Since we have more than 309 genes that pass this cut off, we know that some genes are significantly changed.  However, we don't know ''which'' ones.  To apply a more stringent criterion to our p values, we performed the Bonferroni and Benjamini and Hochberg corrections to these unadjusted p values.  The Bonferroni correction is very stringent.  The Benjamini-Hochberg correction is less stringent.  To see this relationship, filter your data to determine the following:
@@ Line 81: / Line 81: @@
 * 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.'''''
-** 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 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 127: / Line 127: @@
 *To [[User:Kdahlquist|Dr. Dahlquist]] for walking us through analyzing the data with Excel.
 *To my group members, [[User:Dmadere|DeLisa]], [[User:Msamdars|Mihir]], and [[User:Eyoung20|Emma]]. We met during class time to make sure that we had the right values and to help each other with specific steps of the procedure.
 ==References==
 *LMU BioDB 2019. (2019). Week 8. Retrieved October 15, 2019, from [https://xmlpipedb.cs.lmu.edu/biodb/fall2019/index.php/Week_8 https://xmlpipedb.cs.lmu.edu/biodb/fall2019/index.php/Week_8].