Difference between revisions of "Eyoung20 journal week 12/13"
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| − | Milestone 1: Annotated Bibliography | + | == Milestones == |
| − | The Data Analysts will work with their teams to develop an annotated bibliography of papers relating to their team's assigned paper. | + | |
| − | Milestone 2: Journal Club Presentation | + | The milestones do not necessarily correspond to particular days/weeks; instead they are sets of tasks grouped together. |
| − | The Data Analysts will work with their teams to create and deliver a Journal Club presentation about to their team's assigned paper. | + | |
| − | Milestone 3: Getting the data ready for analysis | + | === Milestone 1: Annotated Bibliography === |
| − | Download and examine the microarray dataset, comparing it to the samples and experiment described in your journal club article. | + | |
| − | Barreto et al. (2012) | + | * The Data Analysts will work with their teams to develop an annotated bibliography of papers relating to their team's assigned paper. |
| − | Kitagawa et al. (2002) | + | |
| − | Thorsen et al. (2007) | + | === Milestone 2: Journal Club Presentation === |
| − | 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 | + | * The Data Analysts will work with their teams to create and deliver a Journal Club presentation about to their team's assigned paper. |
| − | For example, the Dahlquist Lab microarray data used strain_LogFC_timepoint-replicate number, as in wt_LogFC_t15-1. | + | |
| − | Organize the data in a worksheet in an Excel workbook so that: | + | === Milestone 3: Getting the data ready for analysis === |
| − | ID is in the first column | + | |
| − | Data columns are to the right, in increasing chronological order, using the column header pattern you created | + | # Download and examine the microarray dataset, comparing it to the samples and experiment described in your journal club article. |
| − | Replicates are grouped together | + | #* [https://sgd-prod-upload.s3.amazonaws.com/S000204227/Barreto_2012_PMID_23039231.zip Barreto et al. (2012)] |
| − | Milestone 4: ANOVA analysis | + | #* [https://sgd-prod-upload.s3.amazonaws.com/S000204415/Kitagawa_2002_PMID_12269742.zip Kitagawa et al. (2002)] |
| − | Perform an ANOVA analysis of the data, as you did on Week 8 for the Dahlquist lab data. | + | #* [https://sgd-prod-upload.s3.amazonaws.com/S000204367/Thorsen_2007_PMID_17327492.zip Thorsen et al. (2007)] |
| − | 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. | + | # 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. |
| − | Milestone 5: Clustering with stem and YEASTRACT | + | #* Come up with consistent column headers that summarize this information |
| − | Cluster the data with stem, as you did on Week 9. | + | #** For example, the Dahlquist Lab microarray data used strain_LogFC_timepoint-replicate number, as in wt_LogFC_t15-1. |
| − | Note that we will make some adjustments to the GO term analysis because stem was not providing GO term names. | + | # Organize the data in a worksheet in an Excel workbook so that: |
| − | Use YEASTRACT to generate a candidate gene regulatory network as you did on Week 9. | + | #* ID is in the first column |
| − | Milestone 6: Create an input workbook for GRNmap using MS Access database | + | #* Data columns are to the right, in increasing chronological order, using the column header pattern you created |
| − | Create an input workbook for GRNmap based on a Microsoft Access database that the Coder/Designer and QA's make, following protocol in Week 10 | + | #* Replicates are grouped together |
| − | Run GRNmap 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. | + | === Milestone 4: ANOVA analysis === |
| + | |||
| + | # Perform an ANOVA analysis of the data, as you did on [[Week 8]] 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. | ||
| + | |||
| + | === Milestone 5: Clustering with stem and YEASTRACT === | ||
| + | |||
| + | # Cluster the data with stem, as you did on [[Week 9]]. | ||
| + | #* Note that we will make some adjustments to the GO term analysis because stem was not providing GO term names. | ||
| + | # Use YEASTRACT to generate a candidate gene regulatory network as you did on [[Week 9]]. | ||
| + | |||
| + | === Milestone 6: Create an input workbook for GRNmap using MS Access database === | ||
| + | |||
| + | # Create an input workbook for GRNmap based on a Microsoft Access database that the Coder/Designer and QA's make, following protocol in [[Week 10]] | ||
| + | # Run GRNmap 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. | ||
Latest revision as of 15:19, 19 November 2019
Contents
Milestones
The milestones do not necessarily correspond to particular days/weeks; instead they are sets of tasks grouped together.
Milestone 1: Annotated Bibliography
- The Data Analysts will work with their teams to develop an annotated bibliography of papers relating to their team's assigned paper.
Milestone 2: Journal Club Presentation
- The Data Analysts will work with their teams to create and deliver a Journal Club presentation about to their team's assigned paper.
Milestone 3: Getting the data ready for analysis
- Download and examine the microarray dataset, comparing it to the samples and experiment described in your journal club article.
- 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
- For example, the Dahlquist Lab microarray data used strain_LogFC_timepoint-replicate number, as in wt_LogFC_t15-1.
- Come up with consistent column headers that summarize this information
- Organize the data in a worksheet in an Excel workbook so that:
- ID 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
Milestone 4: ANOVA analysis
- Perform an ANOVA analysis of the data, as you did on Week 8 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.
Milestone 5: Clustering with stem and YEASTRACT
- Cluster the data with stem, as you did on Week 9.
- Note that we will make some adjustments to the GO term analysis because stem was not providing GO term names.
- Use YEASTRACT to generate a candidate gene regulatory network as you did on Week 9.
Milestone 6: Create an input workbook for GRNmap using MS Access database
- Create an input workbook for GRNmap based on a Microsoft Access database that the Coder/Designer and QA's make, following protocol in Week 10
- Run GRNmap 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.
