Data Analysis - Revision history

Kdahlquist: /* Milestone 5: Create a candidate gene regulatory network and input workbook for GRNmap using MS Access database */ paste in instructions for database queries

2024-04-26T16:53:10Z

‎Milestone 5: Create a candidate gene regulatory network and input workbook for GRNmap using MS Access database: paste in instructions for database queries

Kdahlquist: /* Milestone 2: Getting the data ready for analysis */ delete eweight and gweight

2024-04-11T05:00:28Z

‎Milestone 2: Getting the data ready for analysis: delete eweight and gweight

Kdahlquist: /* Milestones */ add note about keeping notes for reproducible research

2024-04-11T04:45:20Z

‎Milestones: add note about keeping notes for reproducible research

Kdahlquist: /* Milestone 4: Clustering with stem and YEASTRACT */ clarify the CHP-treated data

2024-04-11T04:13:25Z

‎Milestone 4: Clustering with stem and YEASTRACT: clarify the CHP-treated data

Kdahlquist: /* Milestone 5: Create an input workbook for GRNmap using MS Access database */ clarify the worksheets

2024-04-11T04:13:01Z

‎Milestone 5: Create an input workbook for GRNmap using MS Access database: clarify the worksheets

Kdahlquist: /* Milestone 4: Clustering with stem and YEASTRACT */ modify to specify how many clusters

2024-04-11T04:07:53Z

‎Milestone 4: Clustering with stem and YEASTRACT: modify to specify how many clusters

Kdahlquist: /* Milestone 4: Clustering with stem and YEASTRACT */ compare to Sha paper

2024-04-11T04:05:53Z

‎Milestone 4: Clustering with stem and YEASTRACT: compare to Sha paper

Kdahlquist: /* Milestone 3: ANOVA analysis */ provide more detail about the ANOVA and p-value sanity check

2024-04-11T04:03:33Z

‎Milestone 3: ANOVA analysis: provide more detail about the ANOVA and p-value sanity check

Kdahlquist: /* Milestone 2: Getting the data ready for analysis */ instructions to convert to log2 fold changes

2024-04-11T03:59:03Z

‎Milestone 2: Getting the data ready for analysis: instructions to convert to log2 fold changes

Kdahlquist: /* Milestone 2: Getting the data ready for analysis */ add some notes about using SGD data and grouping of data

2024-04-11T03:51:45Z

‎Milestone 2: Getting the data ready for analysis: add some notes about using SGD data and grouping of data

@@ Line 71: / Line 71: @@
 # Run GRNmap in Dr. Dahlquist's research lab (make appointment) and interpret data.
 # As the end-user of the Access database, the Data Analysts will provide feedback to the QAs and Coder/Designer about the usability of database.
 {{Final Project Links}}
 [[Category:Team Project]]

@@ Line 32: / Line 32: @@
 #* Treatments are grouped together
 #* Replicates are grouped together
 # These data are from an Affymetrix, single-color chip.  So, instead of ratios, the values are raw intensity values.  We need to convert the data into Log<sub>2</sub> fold changes (LogFC).
 #* The raw intensity values have been log-transformed, which we need to undo before we calculate the ratios.

← Older revision		Revision as of 04:45, 11 April 2024
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	* Data Analysts can have a shared ''individual'' journal entry. Both students will be given the same grade and are expected to contribute equally to the electronic lab notebook.		* Data Analysts can have a shared ''individual'' journal entry. Both students will be given the same grade and are expected to contribute equally to the electronic lab notebook.
		+	* Detailed notes should be taken throughout consistent with reproducible research and contributing to the final deliverables.

	=== Milestone 1: Journal Club Presentation ===		=== Milestone 1: Journal Club Presentation ===

@@ Line 49: / Line 49: @@
 === Milestone 4:  Clustering with stem and YEASTRACT ===
-# Cluster the data with stem, as you did on [[Week 10]].  Collect the screenshots of the main clustering results and the plots of all significant clusters.  What are the main patterns that you see?  Are they similar to what was reported in the Sha et al. (2013) paper?
+# Cluster the CHP-treated data with stem, as you did on [[Week 10]].  Collect the screenshots of the main clustering results and the plots of all significant clusters.  What are the main patterns that you see?  Are they similar to what was reported in the Sha et al. (2013) paper?
 #* Note that we will make some adjustments to the GO term analysis because stem was not providing GO term names.  We are going to use the GO enrichment tool at GeneOntology.org instead.
 ## Go to [http://geneontology.org/ http://geneontology.org/].

@@ Line 60: / Line 60: @@
 # Use YEASTRACT to generate a list of candidate regulatory transcription factors in [[Week 10]].
-=== Milestone 5:  Create an input workbook for GRNmap using MS Access database ===
+=== Milestone 5:  Create a candidate gene regulatory network and input workbook for GRNmap using MS Access database ===
-# Create an input workbook for GRNmap using GRNsight and the Microsoft Access database that the Coder/Designer and QA's make, protocol ''TBA''.
+# Create an input workbook for GRNmap using queries to the Microsoft Access database that the Coder/Designer and QA's make.  The main worksheets you will need to create are as follows:
 # Run GRNmap in Dr. Dahlquist's research lab (make appointment) and interpret data.
 # As the end-user of the Access database, the Data Analysts will provide feedback to the QAs and Coder/Designer about the usability of database.

@@ Line 49: / Line 49: @@
 === Milestone 4:  Clustering with stem and YEASTRACT ===
-# Cluster the data with stem, as you did on [[Week 10]].  Collect the screenshots of the main clustering results and the plots of all significant clusters.  What are the main patterns that you see?  Are they similar to what was reported in the Sha et al. (20xx) paper?
+# Cluster the data with stem, as you did on [[Week 10]].  Collect the screenshots of the main clustering results and the plots of all significant clusters.  What are the main patterns that you see?  Are they similar to what was reported in the Sha et al. (2013) paper?
 #* Note that we will make some adjustments to the GO term analysis because stem was not providing GO term names.  We are going to use the GO enrichment tool at GeneOntology.org instead.
 ## Go to [http://geneontology.org/ http://geneontology.org/].
-## For the cluster you want to analyze, open the gene list and copy the list of genes.
+## Select two clusters with different patterns that have a reasonably large number of genes.  For each cluster you want to analyze, open the gene list and copy the list of genes.
 ## Paste the list of genes into the "Go Enrichment Analysis" box on the right hand side of the GeneOntology.org page.
 ## Select "Saccharomyces cerevisiae" from the species drop-down menu.
@@ Line 58: / Line 58: @@
 ## Near the bottom of the results page, click on the button to Export "Table".
 ## This will prompt you to save a .txt file that can be opened in Excel to view your results.
-# Use YEASTRACT to generate a candidate gene regulatory network as you did on [[Week 10]].
+# Use YEASTRACT to generate a list of candidate regulatory transcription factors in [[Week 10]].
 === Milestone 5:  Create an input workbook for GRNmap using MS Access database ===

@@ Line 41: / Line 41: @@
 === Milestone 3:  ANOVA analysis ===
-# Perform an ANOVA analysis of the data, as you did on [[Week 9]] for the Dahlquist lab data.
+# Perform an ANOVA analysis of the data (including the Bonferroni and Benjamini and Hochberg corrections), as you did on [[Week 9]] for the Dahlquist lab data.
 #* Note that you will need to adjust your formulas to take into account the different number of timepoints and replicates in your article's dataset.
-#* Also note that you will need to consult with Dr. Dahlquist on how to convert the Affymetrix data from the paper into Log2 fold changes.
+# Perform a "Sanity Check" and create a table of p value counts like you did for the [[Week 9]] assignment.  This time, do the Sanity Check on the Benjamini-Hochberg corrected p values instead of the unadjusted p values.
 === Milestone 4:  Clustering with stem and YEASTRACT ===

@@ Line 21: / Line 21: @@
 # Download and examine the microarray dataset, comparing it to the samples and experiment described in your journal club article.
 #* [https://sgd-prod-upload.s3.amazonaws.com/S000204389/Sha_2013_PMID_24073228.zip Link to processed dataset from SGD.]
-#* [https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE26169 GEO Database: GSE26169]
+#* For your reference, this is the link to the dataset at the GEO Database:[https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE26169 GSE26169].  However, we will use the dataset processed by SGD.
 # Along with the QA's, make a "sample-data relationship table" that lists all of the samples (microarray chips), noting the treatment, time point, and replicate number.
 #* Come up with consistent column headers that summarize this information
@@ Line 27: / Line 27: @@
 #** Do not use any special characters except for "-" or "_" (e.g., no commas, etc.)
 # Organize the data in a worksheet in an Excel workbook so that:
-#* ID is in the first column
+#* ID (SGD systematic name) is in the first column
 #* Data columns are to the right, in increasing chronological order, using the column header pattern you created
 #* Replicates are grouped together