Natalie Williams: Electronic Notebook: Difference between revisions

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=== Electronic Notebook ===
===[[Natalie Williams Fall Electronic Notebook 2014 |Fall 2014]]===
===[[Natalie Williams Fall Electronic Notebook 2014 |Fall 2014]]===
This contains all the procedures and tasks that I completed and the trials that I ran in Fall 2014.
This contains all the procedures and tasks that I completed and the trials that I ran in Fall 2014.
Line 21: Line 20:
===[[Natalie Williams Fall Electronic Notebook 2015 |Fall  2015]]===
===[[Natalie Williams Fall Electronic Notebook 2015 |Fall  2015]]===


===[[Natalie Williams Fall Electronic Notebook 2016 | Fall 2016]]===


==Spring 2017==


=== January 2017 ===
====Week of January 12, 2017====
Monday & Thursday: Worked on collecting sources for my thesis project. The annotated bibliography is due 20/01. I will be in Boston at that time, but I will still submit my annotated bibliography in time. We had our first lab meeting of the semester on Thursday.


====Week of January 19, 2017 ====
Monday: Worked on writing the abstract for the SCSBC at UC Irvine on Saturday, 28/01. The abstract can be found on the Dahlquist Lab repository on github.


==Fall 2016==
Thursday: Not present. Interview at Harvard Medical School.
I was abroad last spring semester, Spring 2016, so there are no notes or records of my experience in the lab at that time.


===September 2016===
====Week of January 26, 2017 ====
====September 14, 2016====
Monday: Finished most of the poster that will be presented this upcoming Saturday at the conference. I wrote much of the content and analysis and Brandon worked on the formatting. Much of the analysis done was on the optimized production and threshold b value's, a motif - Hmo1 --> Msn2 --> Cin5 --> Yhp1.


This week's job was to understand and analyze the study of Neymotin et al (2014) to derive degradation rates from the half-life values of the genes annotated.
Thursday: Went over poster during lab meeting. With Dahlquist's correction, I updated the poster and uploaded it to the github repositoryto be edited and reviewed by Dahlquist before printing.


Personally, I received feedback on my HNRS thesis abstract that is to be submitted on Sept. 30. I made the changes and sent them to Dr. Dahlquist.
=== February 2017 ===
====January 31, 2017 & February 2, 2017====
Monday: Reran the networks derived from dgln3, dhap4, and dzap1 on bouldardii 2 for consistency so that there aren't any discrepancies from running these networks on a different computer.


I have worked more of the R Tutorial that Dr. Dahlquist has issued to both Brandon and me. While Brandon has already coded a script to generate random matrices, our next task will be to come up with code to then generate the distribution of in-degree and out-degree via a bar graph.
Thursday:
*Compiled the optimized parameters into one file as well as the MSE values for individual genes in each of the networks. Each of the networks were visualized again on GRNsight just to ensure that the visualizations match with the output optimized weights for each network.
*Received feedback from Dr. Dahlquist on my annotated bibliography as well as additional sources to use for my thesis.


====September 21, 2016====
====Week of February 6, 2017====
Monday: Edited the 10 random output sheet's K. Grace Johnson ran last year to make them into input sheets to re-run on boulardii 2.
*I deleted all the output sheets: the sigmas, optimized_network_weights, optimized_expression, and the optimized production and threshold_b
*I copied the production and degradation rates from Brandon's dhap4 network into all the corresponding sheets in the random network input sheets
Worked on creating the working abstract for my talk during LMU's Undergraduate Research Symposium.
*The adjacency matrices from the random network files were then copied and pasted into the adjacency matrix of Brandon's file so that all parameters and information would be the same. The only difference was the network and the network weight sheets.


Today, Wednesday 21/9/2016, I completed my task of computing the degradation rates from Neymotin et al's article. I uploaded the file to the DahlquistLab repository where it waits to be reviewed by Dr. Dahlquist.
Thursday: I was not here due to an interview at UCSF's medical school.


For the completion of my task with the degradation rates, the following was done:
====Week of February 13, 2017====
#I downloaded the supplemental data (Table S5) from Neymotin et al
Monday: I generated some random networks with Brandon's R script to be run on the model. A folder was created to hold all the input and output sheets for the random networks that are run with GRNmap [https://github.com/kdahlquist/DahlquistLab/tree/master/data/bouldardii2_GRNmap_outputs/Random_network_intput_output]. For further analysis, I will also look at the distribution of the in and out degrees of all the random networks compared to the network derived from the dhap4 data.
#From Neymotin's data, I edited the following
*Distribution of weights (positive vs. negative) and the overall network
#*Alphabetized: Gene names were used for the alphabetization
*Are any motifs/connections conserved?
#**For alphabetization, I selected the entire sheet
*Any self or auto-regulators?
#**Next, I clicked the Sort button that looks like a funnel, and selected "Custom sort"
*Visualization will also be seen via GRNsight
#**For custom sort, I selected the column with the gene names, for me, Column 1
#**I, then, sorted from descending order from A -> to Z
#*Isolated Half Lives: Created a separate sheet with only Systematic & Gene Names and the thalf life
#**On this new sheet, I copied the Gene names and the thalf lifes corresponding to those genes
#**I calculated the median half life, which will used to calculate the degradation rate of any gene with missing data
#**The following Excel equation was used
#**=MEDIAN("Column Containing thalf lives")
#*Degradation Rates: Created an additional sheet for calculating the degradation rate from the half lives
#**Again, the Gene names and the thalf lives were pasted into this new sheet so that the calculations could be carried out on a single page without interfering with other information or formats
#**The following equation was used to calculate the degradation rate
#** = (ln (0.5)/ half life of specific gene)
#**For genes with missing data, the equation would be the following
#** = (ln (0.5)/ median half life)
#I used a previous file shared with me from Dr. Dahlquist to make the comparison between this work (Neymotin) and Harbison's list of 203 TFs
#I used Microsoft Access to pair the two data sets together using the systematic names in order to identify if there was missing data for the genes
##First open a new blank database.
##I imported my two excel files that contained my data
##*This act can be achieved by selecting the External Data tab and clicking the Excel icon
##*I then went through a series of instructions
##*#I browsed my computer for the file that I needed and selected it
##*#I chose the sheet that I would import, for me, this was Harbison's list of 203 TFs and the sheet with Neymotin's calculated degradation rates
##*#Depending on your sheet's format, the first row may either include headings or go directly into your data; select the box if your first row contains column headings
##*#I skipped the next question, asking about field names and the index, clicking next
##*#I then chose my own primary key - setting it to the first column with Systematic Names (not all genes have universal Gene names)
##*#I then clicked finish and import.
##*Now your data should be seen as a table in Access
##To pair the data sets together, I selected the Create tab and hit Query Design
##*When you selected Query Design, a pop-up window appears and shows all the tables within your current database. Choose the tables that you wish to pair the data for. Exit out of that pop-up window and now you should see your tables with their heading under them.
##*Select the heading that has the information you want to pair with the other file. For me it was the Systematic Names from Neymotin's data with the Systematic Names from Harbison's data
##*Drag the heading and match it to the heading for the other data. Right click on the link that forms between the two headings
##*Because I only want the data from Neymotin's that matches with Harbison's data, I would select the option that states: "Include ALL records from 'Harbison 203' and only those records from 'Neymotin degradation rates' where the joined fields are equal."
##*Press ok and you should now see a pointed arrow head towards Neymotin deg rate heading
##*Now you can drag and drop the headings with the data that you want into the field below. For my query, I selected the names of Harbison's 203 TFs and then dragged down Neymotin's Systematic names as well as the calculated degradation rate to see if any genes were missing.
##*Now that the field is full, click Run to run your query.
##A table should appear now with the data you wanted beside the heading - for me, I have the Systematic names paired together and their corresponding degradation rates in the column beside them.


#*I also chose to include the calculated degradation rates from Neymotin's data in that query as well
Throughout the next couple of weeks, I will be running the random networks generated on GRNmap.
====Week of February 20, 2017====
Monday:Began to look at the MSE values of the db networks 1 & 5 (derived from wt and dhap4 data) compared to the p values from the ANOVA. For the analysis, I looked at the expression data plots categorized by number of significant p values (B&H p values) at the suggestion of Brandon. Divisions were made as follows:
*Two or more significant p values across all strains
*One significant p value across all strains
*No significant p value across all strains
I then described whether the MSE value that was matched with the p value fit well with the modeled dynamics from the expression plots. I created an excel file with these comparisons and comments about the fit of the model, which can be found in the Dahlquist Lab Repository [https://github.com/kdahlquist/DahlquistLab/blob/master/data/15-gene_networks_analysis/pvalue_MSE_comparison.xlsx pvalue_MSE_comparison].


I also revised my abstract and sent that to Dr. Dahlquist for review.
Thursday: Continued to do analysis of p values and MSE outputs by looking at the expression plots. However, during meeting, was told that this was futile and would not generate results because I should be looking at the minMSE for each gene's output. By comparing the MSE:minMSE ratio for each gene, I could see if genes with p values had better or worse fit due to the ratio.
====Week of February 27, 2017====
Monday: Continued to run the random networks on boulardii 2 (left off at random network 23). Instead of using the expression plots for analysis, I began to compare the MSE values of db network 5 (derived from hap4) and the random networks with the same number of nodes (15) and edges (28). The last random network included in the file is rand19. On every sheet, I have the MSE value output from the run in GRNmap next to the p values from the ANOVA for the dhap4 strain. Below those comparisons, we see the differences in MSE values of the random network from the db derived network 5.
*If the number is negative, it suggests that GRNmap reduced the mean square error of that individual gene in the random networks;
*however, if the number is larger, then the db derived network's individual gene saw better modeling/mean square error.
This file can be found in the pvalue_MSE_comparison excel file [https://github.com/kdahlquist/DahlquistLab/blob/master/data/15-gene_networks_analysis/pvalue_MSE_comparison.xlsx].


====September 28, 2016====
Thursday: Continued to run the random networks on boulardii 2 (random network 27 currently running). There are only three remaining random networks (28-30) that need to be run on GRNmap. I carried on with my compilation of the random network MSE value comparisons to db network 5. The last LSE:minLSE comparison made was between db 5 and random network 26. Again, the file can be found [https://github.com/kdahlquist/DahlquistLab/blob/master/data/15-gene_networks_analysis/pvalue_MSE_comparison.xlsx here] on the Dahlquist Lab Repository under the file name pvalue_MSE_comparison.xlsx.  
Today, I worked on some of the TRACE documentation (Numbers 5 & 6). For No. 5, I noted that it required descriptions of how coding was tested and implemented as well as software design. For those portions of the Implementation Verification (No. 5), I will have Eddie from the Coding team help me.


I finished edits of my Honors Thesis abstract and submitted it.
Further, on the sheet labeled dhap4, a bar graph comparing the LSE:minLSE ratios for all the GRNs run on MATLAB thus far can be found. I've begun to look at the regulatory relationships identified in the three lowest and three highest ratio random networks.
*The smallest LSE:minLSE ratios
**random networks 15, 16, and 24
*The largest LSE:minLSE ratios
**random networks 5, 7, and 12


In talking with Dr. Dahlquist, Brandon and I will formulate the standard of input sheets needed for the lab. The process includes:
====Week of March 4, 2017====
*Using the genes from last year's GRNs
Spring Break this week. I was in Mammoth for the week.
*Plugging those genes into YEASTRACT to get the most up-to-date connections with those genes
Sunday: Kristen noticed that random networks 4 & 5 were identical, so I created a new random network (rand 31) to be run in GRNmap. After it was run in the model, the optimization diagnostics showed that random network 31 had a larger LSE:minLSE ratio than random network 5. Therefore, analysis will now be conducted on the following with the highest LSE:minLSE ratios:
*Uploading the matrix into GRNSight to make sure that all the genes in the GRN are connected to each other
*Random network 7, 12, and 31
*Creating the input sheets from scratch with the new degradation rates I computed, estimated production rates, and the expression rates from the microarray data
**Rand7: 1.5202
**For any missing data points, it was decided that the average of expression levels from all available time points will be used
**Rand12: 1.5080
**To ensure knowledge of missing time points, cells will be highlighted in different colors so that when GRNmap can execute with missing values, the filled in data can be removed easily
**Rand31: 1.5001
Thursday: I computed the minMSE values for the DB5 network so that I could use the information for my Symposium presentation. The following protocol was done.
#Using the log2 expression data for the specific strain in a input sheet, average the values for the same timepoints
#* i.e. for wt strain, there are four 15 time point measurements, five 30 time point measurements, and four 60 time point measurements. Therefore, the first gene's average log fold expression change is averaged across four timepoints for 15, five for 30, and four for 60.
#** ABF1 averages: 15 = -1.1878; 30 = -1.1819; 60 = -1.9142
#Next, the difference between each individual log2 expression at a given time point and the average for that given time point was found.
#* i.e for wt's ABF1 gene, we see the following formula: = t15.1 - avg15, where t15.1 is the individual log2 expression at the first 15 time point and avg15 is the average of the four observed expression changes at 15 time point replicates
#** ABF1's first 15 time point: = B2 - P2 = t15.1 - avg15 = -2.1071 - (-1.1878) = -0.9193
#Then, the differences are squared so that no negative numbers result and to account for differences seen above and below this difference
#* i.e. for wt's ABF1 gene, we see the following formula in the cell: = B20^2
#The squared differences were then summed up for all the time points and divided by the total number of time points.
#* The formula used was as follows: =SUM(B38:N38)/13 (based off wt's ABF1 gene)
#** i.e. for wt, there are 13 time points (four 15 time points + five 30 time points + four 60 time points = 13)
#** Note that the sum for all these time points differs for each individual strain, such that db4 (dGLN3) has 12 overall time points
#To ensure that these calculations were correct, I first used this procedure to calculate the MSE observed via the model. After I receive the same output values, I proceeded to calculate the actual minMSEs.


===October 2016===
====Week of March 12, 2017====
====October 5, 2016====
Monday: I worked on completing the analysis of my results. I used Brandon's regulatory relationship workbook to compare the regulatory relationships for DB5 and the three best (15, 16, 24) and worst (7, 12, 31) random networks.
*Process for isolating regulatory relationships
*#Using GRNsight, I visualized the weighted networks of interest and exported the network as a .siv file to isolate the regulatory relationships between regulator and target gene
*#Next, I opened the SIV file in Excel. In a new Excel workbook, I wrote down the relationship between the transcription factor and its target as Regulator --> Target Gene in one cell with the weight of the transcription factor's influence in the column right of it.
*#After I saved all these relationships for the seven networks (DB5, Rand7, Rand12, Rand15, Rand16, Rand24, and Rand31), I compiled all of their regulatory relationships together in a list.
*#Next, I pasted the values that corresponded to a specific node/relationship for each network into the correct cell.
*#*Reading R->L (DB5, Rand7, ..., Rand31)
*#Because Brandon's Excel file already highlighted cell's based on the weights within them, stronger activators were colored red; stronger repressors were colored blue, and grey was used for the weak influencers.
Thursday: Presented a first draft of my presentation for LMU's URS. That was the focus of lab this day.
*Finished up the first draft of my presentation
*For further analysis, I included:
**The sum of weights to identify if the network was 'overall repressive (-) or activating (+)'
**The shared nodes between DB5 and the 3 best and 3 worst networks & found that it shared more nodes with the better networks


Today, I created the input sheets for the two strains that I have - wild-type & dCIN5 from Kayla Jackson's file. The protocol can be found on the [https://github.com/kdahlquist/GRNmap/wiki/How-to-format-the-input-file-for-GRNmap-v1.4-and-above| Dahlquist Github repository].
====Week of March 19, 2017====
Monday: Worked on completing my powerpoint presentation for the LMU's Undergraduate Research Symposium. I sat down with Dr. Dahlquist to discuss my presentation and re-work some of the analysis that I did.  


To achieve the degradation rates and the log expression data for each strain, the Access protocol above was use. The data from one workbook was paired to the existing data in the other workbook with the log expression so that only genes in the network had their expression's noted.  
Thursday: I practiced my presentation in Sea120 before I rehearsed my presentation in front of my lab. Later, I presented my powerpoint for the symposium to my fellow researchers. I received feedback (overall positive, with minor changes to make). I listened to Kristen's presentation, too, before the end of lab.


====October 12, 2016====
====Week of March 26, 2017====
Monday: Worked on a lot of my thesis, writing my discussion


Today, I worked on a lot of documentation and cleaning up my various files that I have shared.
Thursday: Continued to work and write my thesis before the holiday (Cesar Chavez). During the lab meeting, we discussed future directions/what we should work on for the remainder of our time in the lab.
 
#The first thing I updated was the protocol to obtaining the file with the degradation rates and the calculations that I did from the half lives.
#I updated the wiki (github) with the newer protocol, which still has be to be reviewed by Dr. D.
#I tweaked a few files that I've uploaded to the [https://github.com/kdahlquist/DahlquistLab| Dahlquist Repository] on Github
 
 
The files that I edited are the following:
* wt_NEW_Input_16_Node; I changed the optimization parameters sheet to add the headings 'optimization_parameter' and 'value'
** I then updated the values for the optimization parameters, i.e. alpha and MaxIter. These values can be found under Step 11: GRNmap on Dr. Dahlquist's Microarray Data [[Dahlquist:Microarray_Data_Analysis_Workflow#Step_11:_GRNmap| Workflow]].
* dCIN5_NEW_KJ_15_Node; again, I changed the optimization parameters sheet to added the headings 'optimization_parameter' and 'value' and updated the parameters' values according to the workflow mentioned above
*Neymotin_Williams_TF_Comparison; I added an additional sheet for the rounded values that will be used for the degradation rates of the input sheets.
 
I reviewed Brandon's input sheets while he reviews mine. Because I don't know how thoroughly I should've reviewed his data, I started with the accumulation of the log expression data. After that, I will average the numbers for the missing data, to ensure that what we have calculated is convergent.
 
 
I had to reupload and recalculate the degradation rates. Instead of taking the median of the TFs from Harbison's list, I took the median of all the genes on Neymotin's list. The median calculated from all of the genes from Neymotin's data was 10.2 compared to the median from the TFs in Harbison's list, which is 7.
 
====October 19, 2016====
I had to re-format and re-upload my old Input sheets. The files on the Dahlquist Repository did not have the data from all the strains, which was requested. Further, I updated the degradation rates as well as the production rates for the files. I continued with the formatting of the cells, using Arial font @ 11.
 
I then focused on trying to find the degradation rates that I found earlier this semester. The sites were bookmarked on my computer; however, there was an issue with my laptop where it wiped most recently bookmarked websites. Unfortunately, I wasn't able to find the specific sources I had earlier.
 
====October 26, 2016====
Updated wild-type input sheet with dHMO1 log fold expression and also changed the format such that the labels weren't capitalized for the wt input sheet. Because the dCIN5 network does not contain HMO1 in its GRN, there was no need to include the dHMO1 expression data in the workbook for the input sheet.
dCIN5_log2_expression --> dcin5_log2_expression
 
I tested GRNmap with the wt data and the Testing Report can be found [[GRNmap_Testing_Report_NEW_2016-10-26_WT|here]].
*The issue on Github can be found here: [https://github.com/kdahlquist/GRNmap/issues/265 #265]. Please note, that to download version 1.4.4 of GRNmap code, you have to go to the [http://kdahlquist.github.io/GRNmap/ GRNmap] website and click the 'Downloads' link.
*From the 'Downloads' link, you will choose to <b> Download GRNmap Source Code </b> and click the latest version of the code. For me on 2016/10/26, that version was 1.4.4.


==Documents==
==Documents==

Latest revision as of 14:00, 11 April 2017

Natalie Williams: Electronic Notebook

Protocol for MATLAB

This page will help you input and run data sets from your document into an output.

Fall 2014

This contains all the procedures and tasks that I completed and the trials that I ran in Fall 2014.

Spring 2015

This contains all the procedures and tasks that I completed and the trials that I ran in Spring 2015. Most of the activities/notes for this semester focused on creating a poster for the various conferences that we attended in the Spring.

Summer 2015

This link has all the information for what occurred over the summer. A lot of it was testing the code by changing the initial weights and the threshold b values of the input sheets.

Fall 2015

Fall 2016

Spring 2017

January 2017

Week of January 12, 2017

Monday & Thursday: Worked on collecting sources for my thesis project. The annotated bibliography is due 20/01. I will be in Boston at that time, but I will still submit my annotated bibliography in time. We had our first lab meeting of the semester on Thursday.

Week of January 19, 2017

Monday: Worked on writing the abstract for the SCSBC at UC Irvine on Saturday, 28/01. The abstract can be found on the Dahlquist Lab repository on github.

Thursday: Not present. Interview at Harvard Medical School.

Week of January 26, 2017

Monday: Finished most of the poster that will be presented this upcoming Saturday at the conference. I wrote much of the content and analysis and Brandon worked on the formatting. Much of the analysis done was on the optimized production and threshold b value's, a motif - Hmo1 --> Msn2 --> Cin5 --> Yhp1.

Thursday: Went over poster during lab meeting. With Dahlquist's correction, I updated the poster and uploaded it to the github repositoryto be edited and reviewed by Dahlquist before printing.

February 2017

January 31, 2017 & February 2, 2017

Monday: Reran the networks derived from dgln3, dhap4, and dzap1 on bouldardii 2 for consistency so that there aren't any discrepancies from running these networks on a different computer.

Thursday:

  • Compiled the optimized parameters into one file as well as the MSE values for individual genes in each of the networks. Each of the networks were visualized again on GRNsight just to ensure that the visualizations match with the output optimized weights for each network.
  • Received feedback from Dr. Dahlquist on my annotated bibliography as well as additional sources to use for my thesis.

Week of February 6, 2017

Monday: Edited the 10 random output sheet's K. Grace Johnson ran last year to make them into input sheets to re-run on boulardii 2.

  • I deleted all the output sheets: the sigmas, optimized_network_weights, optimized_expression, and the optimized production and threshold_b
  • I copied the production and degradation rates from Brandon's dhap4 network into all the corresponding sheets in the random network input sheets

Worked on creating the working abstract for my talk during LMU's Undergraduate Research Symposium.

  • The adjacency matrices from the random network files were then copied and pasted into the adjacency matrix of Brandon's file so that all parameters and information would be the same. The only difference was the network and the network weight sheets.

Thursday: I was not here due to an interview at UCSF's medical school.

Week of February 13, 2017

Monday: I generated some random networks with Brandon's R script to be run on the model. A folder was created to hold all the input and output sheets for the random networks that are run with GRNmap [1]. For further analysis, I will also look at the distribution of the in and out degrees of all the random networks compared to the network derived from the dhap4 data.

  • Distribution of weights (positive vs. negative) and the overall network
  • Are any motifs/connections conserved?
  • Any self or auto-regulators?
  • Visualization will also be seen via GRNsight

Throughout the next couple of weeks, I will be running the random networks generated on GRNmap.

Week of February 20, 2017

Monday:Began to look at the MSE values of the db networks 1 & 5 (derived from wt and dhap4 data) compared to the p values from the ANOVA. For the analysis, I looked at the expression data plots categorized by number of significant p values (B&H p values) at the suggestion of Brandon. Divisions were made as follows:

  • Two or more significant p values across all strains
  • One significant p value across all strains
  • No significant p value across all strains

I then described whether the MSE value that was matched with the p value fit well with the modeled dynamics from the expression plots. I created an excel file with these comparisons and comments about the fit of the model, which can be found in the Dahlquist Lab Repository pvalue_MSE_comparison.

Thursday: Continued to do analysis of p values and MSE outputs by looking at the expression plots. However, during meeting, was told that this was futile and would not generate results because I should be looking at the minMSE for each gene's output. By comparing the MSE:minMSE ratio for each gene, I could see if genes with p values had better or worse fit due to the ratio.

Week of February 27, 2017

Monday: Continued to run the random networks on boulardii 2 (left off at random network 23). Instead of using the expression plots for analysis, I began to compare the MSE values of db network 5 (derived from hap4) and the random networks with the same number of nodes (15) and edges (28). The last random network included in the file is rand19. On every sheet, I have the MSE value output from the run in GRNmap next to the p values from the ANOVA for the dhap4 strain. Below those comparisons, we see the differences in MSE values of the random network from the db derived network 5.

  • If the number is negative, it suggests that GRNmap reduced the mean square error of that individual gene in the random networks;
  • however, if the number is larger, then the db derived network's individual gene saw better modeling/mean square error.

This file can be found in the pvalue_MSE_comparison excel file [2].

Thursday: Continued to run the random networks on boulardii 2 (random network 27 currently running). There are only three remaining random networks (28-30) that need to be run on GRNmap. I carried on with my compilation of the random network MSE value comparisons to db network 5. The last LSE:minLSE comparison made was between db 5 and random network 26. Again, the file can be found here on the Dahlquist Lab Repository under the file name pvalue_MSE_comparison.xlsx.

Further, on the sheet labeled dhap4, a bar graph comparing the LSE:minLSE ratios for all the GRNs run on MATLAB thus far can be found. I've begun to look at the regulatory relationships identified in the three lowest and three highest ratio random networks.

  • The smallest LSE:minLSE ratios
    • random networks 15, 16, and 24
  • The largest LSE:minLSE ratios
    • random networks 5, 7, and 12

Week of March 4, 2017

Spring Break this week. I was in Mammoth for the week. Sunday: Kristen noticed that random networks 4 & 5 were identical, so I created a new random network (rand 31) to be run in GRNmap. After it was run in the model, the optimization diagnostics showed that random network 31 had a larger LSE:minLSE ratio than random network 5. Therefore, analysis will now be conducted on the following with the highest LSE:minLSE ratios:

  • Random network 7, 12, and 31
    • Rand7: 1.5202
    • Rand12: 1.5080
    • Rand31: 1.5001

Thursday: I computed the minMSE values for the DB5 network so that I could use the information for my Symposium presentation. The following protocol was done.

  1. Using the log2 expression data for the specific strain in a input sheet, average the values for the same timepoints
    • i.e. for wt strain, there are four 15 time point measurements, five 30 time point measurements, and four 60 time point measurements. Therefore, the first gene's average log fold expression change is averaged across four timepoints for 15, five for 30, and four for 60.
      • ABF1 averages: 15 = -1.1878; 30 = -1.1819; 60 = -1.9142
  2. Next, the difference between each individual log2 expression at a given time point and the average for that given time point was found.
    • i.e for wt's ABF1 gene, we see the following formula: = t15.1 - avg15, where t15.1 is the individual log2 expression at the first 15 time point and avg15 is the average of the four observed expression changes at 15 time point replicates
      • ABF1's first 15 time point: = B2 - P2 = t15.1 - avg15 = -2.1071 - (-1.1878) = -0.9193
  3. Then, the differences are squared so that no negative numbers result and to account for differences seen above and below this difference
    • i.e. for wt's ABF1 gene, we see the following formula in the cell: = B20^2
  4. The squared differences were then summed up for all the time points and divided by the total number of time points.
    • The formula used was as follows: =SUM(B38:N38)/13 (based off wt's ABF1 gene)
      • i.e. for wt, there are 13 time points (four 15 time points + five 30 time points + four 60 time points = 13)
      • Note that the sum for all these time points differs for each individual strain, such that db4 (dGLN3) has 12 overall time points
  5. To ensure that these calculations were correct, I first used this procedure to calculate the MSE observed via the model. After I receive the same output values, I proceeded to calculate the actual minMSEs.

Week of March 12, 2017

Monday: I worked on completing the analysis of my results. I used Brandon's regulatory relationship workbook to compare the regulatory relationships for DB5 and the three best (15, 16, 24) and worst (7, 12, 31) random networks.

  • Process for isolating regulatory relationships
    1. Using GRNsight, I visualized the weighted networks of interest and exported the network as a .siv file to isolate the regulatory relationships between regulator and target gene
    2. Next, I opened the SIV file in Excel. In a new Excel workbook, I wrote down the relationship between the transcription factor and its target as Regulator --> Target Gene in one cell with the weight of the transcription factor's influence in the column right of it.
    3. After I saved all these relationships for the seven networks (DB5, Rand7, Rand12, Rand15, Rand16, Rand24, and Rand31), I compiled all of their regulatory relationships together in a list.
    4. Next, I pasted the values that corresponded to a specific node/relationship for each network into the correct cell.
      • Reading R->L (DB5, Rand7, ..., Rand31)
    5. Because Brandon's Excel file already highlighted cell's based on the weights within them, stronger activators were colored red; stronger repressors were colored blue, and grey was used for the weak influencers.

Thursday: Presented a first draft of my presentation for LMU's URS. That was the focus of lab this day.

  • Finished up the first draft of my presentation
  • For further analysis, I included:
    • The sum of weights to identify if the network was 'overall repressive (-) or activating (+)'
    • The shared nodes between DB5 and the 3 best and 3 worst networks & found that it shared more nodes with the better networks

Week of March 19, 2017

Monday: Worked on completing my powerpoint presentation for the LMU's Undergraduate Research Symposium. I sat down with Dr. Dahlquist to discuss my presentation and re-work some of the analysis that I did.

Thursday: I practiced my presentation in Sea120 before I rehearsed my presentation in front of my lab. Later, I presented my powerpoint for the symposium to my fellow researchers. I received feedback (overall positive, with minor changes to make). I listened to Kristen's presentation, too, before the end of lab.

Week of March 26, 2017

Monday: Worked on a lot of my thesis, writing my discussion

Thursday: Continued to work and write my thesis before the holiday (Cesar Chavez). During the lab meeting, we discussed future directions/what we should work on for the remainder of our time in the lab.

Documents

Summer 2015

To view the most updated powerpoint click here
To see the input sheet that was run for the fixed b trial, please click this link
To view the output file from this fixed b trial, click here
To see the input sheet that was run from the estimated b, please click this
To view the output file from the estimated b, click here
The powerpoint that reviews and analyzes the outputs can be viewed here

GRNmap Testings

This is the template for future reports: GRNmap Testing Report
GRNmap Testing Report: Strain Run Comparisons 2015-05-27
GRNmap Testing Report: Non-1 Initial Weight Guesses 2015-05-28

Other Links

Back to User:Natalie Williams
To visit the Dahlquist Lab: click here
To see K. Grace J's Notebook: click here

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