Imacarae Week 10 - Revision history

Imacarae: /* References */ added reference

2019-11-07T07:02:08Z

‎References: added reference

Imacarae: /* Conclusion */ edited

2019-11-07T07:00:19Z

‎Conclusion: edited

Imacarae: /* Conclusion */ added a few sentences

2019-11-07T06:55:51Z

‎Conclusion: added a few sentences

Imacarae: /* Dynamical Systems Modeling of your Gene Regulatory Network */ edited tense

2019-11-07T06:49:26Z

‎Dynamical Systems Modeling of your Gene Regulatory Network: edited tense

Imacarae: /* Creating the "optimization_parameters" sheet */ edited tense

2019-11-07T06:40:46Z

‎Creating the "optimization_parameters" sheet: edited tense

Imacarae: /* Creating the "network_weights" sheet */ changed tense

2019-11-07T03:23:22Z

‎Creating the "network_weights" sheet: changed tense

Imacarae: /* Creating the "network" sheet */ changed tense

2019-11-07T03:23:01Z

‎Creating the "network" sheet: changed tense

Imacarae: /* Expression Data Sheets for Individual Yeast Strains */ changed tense

2019-11-07T03:20:30Z

‎Expression Data Sheets for Individual Yeast Strains: changed tense

Imacarae: /* Creating the "degradation_rates" sheet */ edited tense

2019-11-07T03:09:42Z

‎Creating the "degradation_rates" sheet: edited tense

Imacarae: /* Creating the "production_rates" sheet */ edited

2019-11-07T03:07:08Z

‎Creating the "production_rates" sheet: edited

← Older revision		Revision as of 07:02, 7 November 2019
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	==References==		==References==

		+	*GRNmap. (2018). Downloads. Retrieved November 6, 2019, from [http://kdahlquist.github.io/GRNmap/downloads/ http://kdahlquist.github.io/GRNmap/downloads/].
		+
		+	*LMU BioDB 2019. (2019). Week 10. Retrieved November 6, 2019, from [https://xmlpipedb.cs.lmu.edu/biodb/fall2019/index.php/Week_10 https://xmlpipedb.cs.lmu.edu/biodb/fall2019/index.php/Week_10].

	[[Category:Journal Entry]]		[[Category:Journal Entry]]

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 ==Conclusion==
-For this week, there was a lot of data organization. Not only did we learn how inputing can be user friendly, but we also learned how it can be user hostile. All details must exactly fit what's in the directions or else the MATLAB will not run.
+For this week, there was a lot of data organization. Not only did we learn how inputing can be user friendly, but we also learned how it can be user hostile. All details must exactly fit what's in the directions or else the MATLAB will not run. The microarray data for Profile 45 of dCIN5 was analyzed through MATLAB. We can now organize the transcription factors in relation to each other and then determine how they are involved in the cold response.
 ==Acknowledgments==

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 ==Conclusion==
 ==Acknowledgments==
 *To [[User:Kdahlquist|Dr. Dahlquist]] for providing us with background information needed for organizing the data with Excel and Access.

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 === Dynamical Systems Modeling of your Gene Regulatory Network ===
+The worksheet is ready to run the model and analyze the results. The software I used is called GRNmap, which stands for Gene Regulatory Network Modeling and Parameter Estimation. It is written in MATLAB and can be run from code or run as a stand-alone executable if I didn't have MATLAB installed.  However, it can only be run in Windows, not on Macs.
-Now you are ready to run the model and analyze the results.  The software we will use is called GRNmap, which stands for Gene Regulatory Network Modeling and Parameter Estimation.  It is written in MATLAB and can be run from code or run as a stand-alone executable if you don't have MATLAB installed.  However, it can only be run in Windows, not on Macs.
+* To run GRNmap from code, I ran MATLAB R2014b.
-* To run GRNmap from code, you must have MATLAB R2014b installed on your computer.
+*# I downloaded the GRNmap v1.10 code from the [http://kdahlquist.github.io/GRNmap/downloads/ GRNmap Downloads page].
 *#* [https://github.com/kdahlquist/GRNmap/archive/v1.10.zip This is a direct link to start downloading (81 MB).]
-*# Unzip the file. (Right-click, 7-zip > Extract here)
+*# I unzipped the file. (Right-click, 7-zip > Extract here)
-*# Launch MATLAB R2014b.
+*# I launched MATLAB R2014b.
-*# Open GRNmodel.m, which will be in the directory that you unzipped GRNmap-1.10 > matlab
+*# I opened GRNmodel.m, which will be in the directory that I unzipped GRNmap-1.10 > matlab
-*# Click the Run button (green "play" arrow).
+*# I clicked the Run button (green "play" arrow).
-*# You will be prompted to select your input workbook.
+*# I selected the input workbook.
-<!--* To run the GRNmap executable, you must have administrator rights on your computer for installing software.
+*# I saw an optimization diagnostics graphic that shows the progress of the estimation.
-** Download the GRNmap v1.10 executable from the [http://kdahlquist.github.io/GRNmap/downloads/ GRNmap Downloads page].
+*# Expression plots will display when the run was over.
-** [https://github.com/kdahlquist/GRNmap/releases/download/v1.10/GRNmap-v1.10.zip This is a direct link to start downloading (688 MB)]-->
+*# Output .xlsx and .mat files was saved in the same folder as my input folder, along with .jpg files containing the optimization diagnostic and individual expression plots. I saved these files.
-*# You will see an optimization diagnostics graphic that shows the progress of the estimation.
+*# Note that if I needed to run GRNmap again, I did not use the same directory for the input file.  Currently, GRNmap will overwrite previous output.
-*# When the run is over, expression plots will display.
+* I uploaded my output .xlsx file into [http://dondi.github.io/GRNsight/ GRNsight] to visualize the results!
 ==Data and Files==

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 ==== Creating the "optimization_parameters" sheet ====
-* The optimization_parameters sheet should have two columns (from left to right) entitled, "optimization_parameter" and "value".
+* The optimization_parameters sheet has two columns (from left to right) entitled, "optimization_parameter" and "value".
-* You should copy this worksheet from the [https://github.com/kdahlquist/DahlquistLab/raw/master/data/Spring2019/15-genes_28-edges_sample_Sigmoid_estimation_2019.xlsx sample workbook] provided.  The only row that you need to modify is row 15, "Strain".  Include just the strain designations for which you have a corresponding STRAIN_log2_expression sheet.  If you don't have the dgln3, dhap4, or dcin5 expression sheets, then you will delete those from this row.  If you do so, make sure that you don't leave any gaps between cells.
+* I copied this worksheet from the [https://github.com/kdahlquist/DahlquistLab/raw/master/data/Spring2019/15-genes_28-edges_sample_Sigmoid_estimation_2019.xlsx sample workbook] provided.  The only row that I modified is row 15, "Strain".  I included just the dgln3, dhap4, and dcin5 strain designations.
 * What follows below is an explanation of what the optimization_parameters mean.
 ** alpha: Penalty term weighting (from the L-curve analysis)

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 ==== Creating the "network_weights" sheet ====
-* These are the initial guesses for the estimation of the weight parameters, ''w''.
+* These were the initial guesses for the estimation of the weight parameters, ''w''.
-* Since these weights are initial guesses which will be optimized by GRNmap, the content of this sheet can be identical to the "network" sheet.
+* Since these weights were initial guesses which will be optimized by GRNmap, the content of this sheet was identical to the "network" sheet.
 ==== Creating the "optimization_parameters" sheet ====

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 ==== Creating the "network" sheet ====
-* The network you derived from the YEASTRACT database for the [[Week 9]] assignment can be copied and pasted into this sheet directly.  You may need to edit the contents of cell A1, but the rest should be good to go (especially since you previewed it in GRNsight). The description below just explains what is already in this worksheet.
+* The network you derived from the YEASTRACT database for the [[Week 9]] assignment was copied and pasted into this sheet directly. The description below just explains what is already in this worksheet.
-** This sheet contains an adjacency matrix representation of the gene regulatory network.
+** This sheet contained an adjacency matrix representation of the gene regulatory network.
-** The columns correspond to the transcription factors and the rows correspond to the target genes controlled by those transcription factors.
+** The columns corresponded to the transcription factors and the rows corresponded to the target genes controlled by those transcription factors.
 ** A “1” means there is an edge connecting them and a “0” means that there is no edge connecting them.
-** The upper-left cell (A1) should contain the text “cols regulators/rows targets”. This text is there as a reminder of the direction of the regulatory relationships specified by the adjacency matrix.
+** The upper-left cell (A1) contained the text “cols regulators/rows targets”. This text is there as a reminder of the direction of the regulatory relationships specified by the adjacency matrix.
 ** The rest of row 1 should contain the names of the transcription factors that are controlling the other genes in the network, one transcription factor name per column.
 ** The rest of column A should contain the names of the target genes that are being controlled by the transcription factors heading each of the columns in the matrix, one target gene name per row.
-** The transcription factor names should correspond to the "id" in the other sheets in the workbook.  They should be capitalized the same way and occur in the same order along the top and side of the matrix.  The matrix needs to be symmetric, i.e., the same transcription factors should appear along the top and left side of the matrix.  The genes should be listed in the same order in all the sheets in the Excel workbook.
+** The transcription factor names corresponded to the "id" in the other sheets in the workbook.  They should be capitalized the same way and occur in the same order along the top and side of the matrix.  The matrix needs to be symmetric, i.e., the same transcription factors should appear along the top and left side of the matrix.  The genes should be listed in the same order in all the sheets in the Excel workbook.
 ** Each cell in the matrix should then contain a zero (0) if there is no regulatory relationship between those two transcription factors, or a one (1) if there is a regulatory relationship between them. Again, the columns correspond to the transcription factors and the rows correspond to the target genes controlled by those transcription factors.

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 ==== Expression Data Sheets for Individual Yeast Strains ====
-* Expression data can be provided for either a single strain or multiple strains of yeast (for example, the wild type strain and a transcription factor deletion strain).
+* Expression data was provided for either a single strain or multiple strains of yeast (for example, the wild type strain and a transcription factor deletion strain).
-** Each strain will have its own sheet in the workbook.
+** Each strain had its own sheet in the workbook.
-** Each sheet should be given a unique name that follows the convention "STRAIN_log2_expression", where the word "STRAIN" is replaced by the strain designation, which will appear in the optimization_diagnostics sheet.
+** Each sheet was given a unique name that follows the convention "STRAIN_log2_expression", where the word "STRAIN" was replaced by the strain designation, which appeared in the optimization_diagnostics sheet.
-*** Everyone in the class will have at least one expression worksheet called "wt_log2_expression".
+*** Everyone in the class had at least one expression worksheet called "wt_log2_expression".
-*** You should have included the transcription factors GLN3, HAP4, and CIN5 in your network.  Thus, we will use the expression data from the dGLN3, dHAP4, dCIN5 deletion strains in our workbooks as well, naming the worksheets "dgln3_log2_expression", "dhap4_log2_expression", and "dcin5_expression".
+*** I included the transcription factors GLN3, HAP4, and CIN5 in your network.  I used the expression data from the dGLN3, dHAP4, dCIN5 deletion strains in my workbooks as well, naming the worksheets "dgln3_log2_expression", "dhap4_log2_expression", and "dcin5_log2_expression".
-**** If, for some reason, you don't have all three of those genes in your network, only include expression data for the wild type and the genes out of those three that you have in your network.
+* The sheet had the following columns in this order:
 *# "id": list of all genes. The genes should be listed in the same order in all the sheets in the Excel workbook.
-*# The next series of columns should contain the expression data for each gene at a given timepoint given as log2 ratios (log2 fold changes).  The column header should be the time at which the data were collected, without any units.  For example, the 15 minute timepoint would have a column header "15" and the 30 minute timepoint would have the column header "30".  GRNmap supports replicate data for each of the timepoints.  Replicate data for the same timepoint should be in columns immediately next to each other and have the same column headers.  For example, three replicates of the 15 minute timepoint would have "15", "15", "15" as the column headers.
+*# The next series of columns contained the expression data for each gene at a given timepoint given as log2 ratios (log2 fold changes).  The column header was the time at which the data were collected, without any units. For example, the 15 minute timepoint would have a column header "15" and the 30 minute timepoint would have the column header "30". GRNmap supports replicate data for each of the timepoints. Replicate data for the same timepoint were in columns immediately next to each other and had the same column headers. For example, three replicates of the 15 minute timepoint would have "15", "15", "15" as the column headers.
-*# If data are provided for multiple strains, each strain should have data for the same timepoints, although the number of replicates can vary.
+*# If data are provided for multiple strains, each strain had data for the same timepoints, although the number of replicates can vary.
-* Include the data for the 15, 30, and 60 minute timepoints, but not the 90 or 120 minute timepoints.
+* I included the data for the 15, 30, and 60 minute timepoints, but not the 90 or 120 minute timepoints.
-* The data you will be using is contained in the [https://github.com/kdahlquist/DahlquistLab/raw/master/data/Spring2019/Expression-and-Degradation-rate-database_2019.accdb Expression-and-Degradation-rate-database_2019.accdb] file that you used to obtain the production and degradation rates.
+* The data I used is contained in the [https://github.com/kdahlquist/DahlquistLab/raw/master/data/Spring2019/Expression-and-Degradation-rate-database_2019.accdb Expression-and-Degradation-rate-database_2019.accdb] file that I used to obtain the production and degradation rates.
-* It is tedious to copy and paste all of these data by hand, so we will execute a query in Microsoft Access to do it for you.  Follow the steps listed for the "production_rates" sheet for each strains expression data.  After you import the data into Excel, you will need to change the column headers to "15", "15", etc., as described above.
+* I executed a query in Microsoft Access. I followed the steps listed for the "production_rates" sheet for each strains expression data.  After I imported the data into Excel, I changed the column headers to "15", "15", etc., as described above.
-* Missing values in the expression data sheets are OK; you don't need to put any values there like you did for the production_rates or degradation_rates sheets.
+* Missing values in the expression data sheets are OK.
 ==== Creating the "network" sheet ====

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 ==== Creating the "degradation_rates" sheet ====
-* This sheet contains degradation rates for all genes in the network, which are provided by the user.
+* This sheet contained degradation rates for all genes in the network, which are provided by the user.
 * Currently, the Dahlquist Lab is using data based on published mRNA half-life data from [http://rnajournal.cshlp.org/content/20/10/1645.full Neymotin et al. (2006)].
-** We converted the half-life data values to the degradation rates by taking the natural log of the half-life and dividing by 2.
+** They converted the half-life data values to the degradation rates by taking the natural log of the half-life and dividing by 2.
-* The sheet should contain two columns (from left to right) entitled "id", and "degradation_rate".
+* The sheet contained two columns (from left to right) entitled "id" and "degradation_rate".
 ** The id is an identifier that the user will use to identify a particular gene.
-** The "degradation_rate" column should then contain the absolute value of the degradation rate for the corresponding gene as described above, rounded to four decimal places.
+** The "degradation_rate" column contained the absolute value of the degradation rate for the corresponding gene as described above, rounded to four decimal places.
-*** To obtain these values, you will use the same file, [https://github.com/kdahlquist/DahlquistLab/raw/master/data/Spring2019/Expression-and-Degradation-rate-database_2019.accdb Microsoft Access database] that you used to obtain the production rates in the first worksheet.  Again, you can copy and paste the values one-by-one or you can follow the instructions to execute a query, substituting the appropriate "degradation_rates" table in the query.  Note that you don't need to re-import your "network" table, you just need to create and execute the query.
+*** To obtain these values, I used the same file, [https://github.com/kdahlquist/DahlquistLab/raw/master/data/Spring2019/Expression-and-Degradation-rate-database_2019.accdb Microsoft Access database] that I used to obtain the production rates in the first worksheet.  Again, I followed the instructions to execute a query, substituting the appropriate "degradation_rates" table in the query. I didn't need to re-import my "network" table, I just created and executed the query.
-* Again note, the genes should be listed in the same order in all the sheets in the Excel workbook.
+* Again, the genes were listed in the same order in all the sheets in the Excel workbook.
-* If there are missing values, substitute the value <code>0.0990</code> for the missing degradation rates.
+* If there are missing values, I substituted the value <code>0.0990</code> for the missing degradation rates.
 ==== Expression Data Sheets for Individual Yeast Strains ====

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 ***# I copied the data in this table and pasted it back into your Excel workbook.  I made sure that when I pasted that, I used "Paste Special > Paste values" so that the Access formatting doesn't get carried along.  I also chose to export this table to Excel going to the "External Data" tab and selecting the Excel icon with the arrow pointing to the right.  Select the workbook you want to export the table to, making sure that "Preserve Access formatting" is '''not''' checked.  Click "OK", click "Close".
 * If there are missing values, substitute the value <code>0.1980</code> for the missing production rates.
-* Note that the genes should be listed in the same order in all the sheets in the Excel workbook.
+* The genes should be listed in the same order in all the sheets in the Excel workbook.
 ==== Creating the "degradation_rates" sheet ====