Difference between revisions of "Team:CUNY Kingsborough/Results"

 
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<h1 class="title-padding">Results</h1>
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<h2 class="low-rise-padding">Introduction</h2>
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<p class="low-rise-padding">To better characterize the standard curve for the EtBr-DNA Dot Blot, we are interested first whether the amount of EtBr significantly affects the fluorescent when the DNA concentration is  varied between different amounts of EtBr and demonstrate there is significant difference in fluorescent between. We almost must characterize the predictive ability of the standard curve using some known DNA concentration that is not used for the curve. Kasap et al. (2006) has shown that it is possible to approximate the concentration of DNA using Imagej.  In this study we will use a linear regression model to find:</p>
  
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<h1>Results</h1>
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<p>Here you can describe the results of your project and your future plans. </p>
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<h3>What should this page contain?</h3>
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<li> Clearly and objectively describe the results of your work.</li>
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<li>Is there a measurable pixel difference?</li>
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<li> Future plans for the project. </li>
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<li>What are the factors that affect pixel intensity?</li>
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<li> Considerations for replicating the experiments. </li>
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<h2 class="default-padding">Methods</h2>
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<p class="low-rise-padding">Statistical analysis was done with R-studio and the Standard Curve was generated using Excel. Mass action model was done using Wolfram Mathematica. Data was entered into Excel and standardized using the following fomula:
  
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StandardPixel = (Value-MIN)\div(MAX-MIN)</p>
  
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<h2 class="default-padding">Results</h2>
  
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<h3 class="default-padding">Predicted Standard Curves</u></h2>
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<h3>Describe what your results mean </h3>
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<center><img src="https://static.igem.org/mediawiki/2018/7/75/T--CUNY_Kingsborough--iGEMLinearStandardCurve.jpg" width="50%"></center>
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<li> Interpretation of the results obtained during your project. Don't just show a plot/figure/graph/other, tell us what you think the data means. This is an important part of your project that the judges will look for. </li>
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<li> Show data, but remember all measurement and characterization data must be on part pages in the Registry. </li>
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<li> Consider including an analysis summary section to discuss what your results mean. Judges like to read what you think your data means, beyond all the data you have acquired during your project. </li>
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<p class="low-rise-padding">We examined the correlation of 125ng DNA and 16ng DNA under varied conditions of EtBr. We found that there is a low correlation when between the amount of EtBr and fluorescent.</p>
  
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<h3 class="default-padding" style:"color: black">125 ng of DNA vs EtBr</h3>
  
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<img src="https://static.igem.org/mediawiki/2018/a/a8/T--CUNY_Kingsborough--iGEM125ngDNAvsEtBr.jpg" width="50%">
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<figcaption><small>Fig 1. Scatter plot of 125ng DNA with varied amount of EtBr. r=0.23</small></figcaption>
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<h3 class="default-padding" style:"color: black">16 ng of DNA vs EtBr</h3>
  
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<h3> Project Achievements </h3>
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<img src="https://static.igem.org/mediawiki/2018/c/c7/T--CUNY_Kingsborough--iGEM16ngDNAvsEtBr.jpg" width="50%">
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<figcaption><small>Fig 2. Scatter plot of 16ng DNA with varied amount of EtBr. r=0.20</small></figcaption>
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<p>You can also include a list of bullet points (and links) of the successes and failures you have had over your summer. It is a quick reference page for the judges to see what you achieved during your summer.</p>
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<p class="low-rise-padding">When number of DNA was varied, we found that  125ng of DNA (β = 43.0, p < .001), and 16 ng of DNA (β=41.9, p < .001) were significant predictors of pixel intensity. Zero ng of DNA was used as the reference variable. The overall model fit was R^2 = 0.57, F-statistics = 41.02, p<.001.</p>
  
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<ul>
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<p class="low-rise-padding">This goes in contrast to our Mass-Action model which shows that the amount of EtBr has a significant effect on the pixel intensity. However, it is obvious that EtBr is needed for DNA to fluorescent therefore a future study should examine at what concentration of EtBr do we see no fluorescent when the amount of DNA is varied.</p>
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<li>A list of linked bullet points of the successful results during your project</li>
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<li>A list of linked bullet points of the unsuccessful results during your project. This is about being scientifically honest. If you worked on an area for a long time with no success, tell us so we know where you put your effort.</li>
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<img src="https://static.igem.org/mediawiki/2018/6/6a/T--CUNY_Kingsborough--resultsfig1.png" width="50%">
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<figcaption><small>Fig 3. Linear model results of lab data</small></figcaption>
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<p class="no-rise-padding">Figure 3 shows the linear model results for our own data. We found that amount of DNA (β=20.99, p<0.05) and EtBr (β=-0.38, p<0.05) are significant factors to pixel intensity. Background is not a significant contributor to pixel intensity. The overall model is significant (R2=0.387, F-statistics = 14.28, p<0.01).</p>
  
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<figure>
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<img src="https://static.igem.org/mediawiki/2018/9/98/T--CUNY_Kingsborough--resultsfig2.png" width="50%">
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<figcaption><small>Fig 4. Residual plot of figure 3 results</small></figcaption>
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<p class="no-rise-padding">Figure 4 shows the residual (Residual = Observed-Predicted). According to the residual, our model seems to under predict observed values and over predict lower values. This suggests that our model has not account for all the possible variables.</p>
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<h3>Inspiration</h3>
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<p>See how other teams presented their results.</p>
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<li><a href="https://2014.igem.org/Team:TU_Darmstadt/Results/Pathway">2014 TU Darmstadt </a></li>
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<li><a href="https://2014.igem.org/Team:Imperial/Results">2014 Imperial </a></li>
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<li><a href="https://2014.igem.org/Team:Paris_Bettencourt/Results">2014 Paris Bettencourt </a></li>
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<figure>
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<img src="https://static.igem.org/mediawiki/2018/f/fa/T--CUNY_Kingsborough--resultsfig3.png" width="50%">
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<figcaption><small>Fig 5. Linear model from other teams</small></figcaption>
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</figure>
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</center>
  
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<p class="no-rise-padding">Figure 5 is the linear model for the DNA-EtBr sample sent by other iGEM teams. DNA concentration (β=5.08, p<0.05) and background (β=-28.38, p<0.05) are significant contributors to pixel intensity. The results in Figure 3 suggests that the background-though we subtracted the background pixel intensity-is still a significant contributor to overall pixel intensity.</p>
  
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Latest revision as of 04:00, 8 December 2018

Background Design Experiments Results
Overview Modeling 101 Light Operon Turing Patterns CRISPR-Cas13a
Human Practices Public Engagement
Team Members Collaborations Attributions

Results

Introduction

To better characterize the standard curve for the EtBr-DNA Dot Blot, we are interested first whether the amount of EtBr significantly affects the fluorescent when the DNA concentration is varied between different amounts of EtBr and demonstrate there is significant difference in fluorescent between. We almost must characterize the predictive ability of the standard curve using some known DNA concentration that is not used for the curve. Kasap et al. (2006) has shown that it is possible to approximate the concentration of DNA using Imagej. In this study we will use a linear regression model to find:

  • Is there a measurable pixel difference?
  • What are the factors that affect pixel intensity?

Methods

Statistical analysis was done with R-studio and the Standard Curve was generated using Excel. Mass action model was done using Wolfram Mathematica. Data was entered into Excel and standardized using the following fomula: StandardPixel = (Value-MIN)\div(MAX-MIN)

Results

Predicted Standard Curves

We examined the correlation of 125ng DNA and 16ng DNA under varied conditions of EtBr. We found that there is a low correlation when between the amount of EtBr and fluorescent.

125 ng of DNA vs EtBr

Fig 1. Scatter plot of 125ng DNA with varied amount of EtBr. r=0.23

16 ng of DNA vs EtBr

Fig 2. Scatter plot of 16ng DNA with varied amount of EtBr. r=0.20

When number of DNA was varied, we found that 125ng of DNA (β = 43.0, p < .001), and 16 ng of DNA (β=41.9, p < .001) were significant predictors of pixel intensity. Zero ng of DNA was used as the reference variable. The overall model fit was R^2 = 0.57, F-statistics = 41.02, p<.001.

This goes in contrast to our Mass-Action model which shows that the amount of EtBr has a significant effect on the pixel intensity. However, it is obvious that EtBr is needed for DNA to fluorescent therefore a future study should examine at what concentration of EtBr do we see no fluorescent when the amount of DNA is varied.

Fig 3. Linear model results of lab data

Figure 3 shows the linear model results for our own data. We found that amount of DNA (β=20.99, p<0.05) and EtBr (β=-0.38, p<0.05) are significant factors to pixel intensity. Background is not a significant contributor to pixel intensity. The overall model is significant (R2=0.387, F-statistics = 14.28, p<0.01).

Fig 4. Residual plot of figure 3 results

Figure 4 shows the residual (Residual = Observed-Predicted). According to the residual, our model seems to under predict observed values and over predict lower values. This suggests that our model has not account for all the possible variables.

Fig 5. Linear model from other teams

Figure 5 is the linear model for the DNA-EtBr sample sent by other iGEM teams. DNA concentration (β=5.08, p<0.05) and background (β=-28.38, p<0.05) are significant contributors to pixel intensity. The results in Figure 3 suggests that the background-though we subtracted the background pixel intensity-is still a significant contributor to overall pixel intensity.