Difference between revisions of "20.109(F18):Practice statistical analysis methods and complete data analysis (Day7)"

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(Test gamma-H2AX quantification on one representative image)
(Test gamma-H2AX quantification on one representative image)
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#*Go to Process -> Binary -> Convert to Mask
 
#*Go to Process -> Binary -> Convert to Mask
 
#**This makes the image black and white, where the white areas should correspond to nuclei locations.
 
#**This makes the image black and white, where the white areas should correspond to nuclei locations.
#Use the newly created mask to identify locations on the FITC channel in which to quantify the gamma-H2AX signal
+
#Use the newly created mask to identify locations on the FITC channel in which to quantify the gamma-H2AX signal [[File:Fa18OutlineExample.png|230px|thumb|right|An example of areas identified by intensity thresholds for DAPI channel in ImageJ]]
[[File:Fa18OutlineExample.png|230px|thumb|right|An example of areas identified by intensity thresholds for DAPI channel in ImageJ]]
+
 
#*Go to Analyze -> Set Measurements
 
#*Go to Analyze -> Set Measurements
 
#**In the Set Measurements window, make sure the following boxes are checked: Area, Mean gray value, Min & max gray value, Shape descriptors, Integrated density, Display label
 
#**In the Set Measurements window, make sure the following boxes are checked: Area, Mean gray value, Min & max gray value, Shape descriptors, Integrated density, Display label

Revision as of 16:12, 28 September 2018

20.109(F18): Laboratory Fundamentals of Biological Engineering

Fa18 20109 banner image.png

Fall 2018 schedule        FYI        Assignments        Homework        Class data        Communication
       1. Measuring genomic instability        2. Modulating metabolism        3. Engineering biomaterials              


Introduction

Today is the final laboratory session for Module 1! You have completed all of the bench work for your research; however, there is still data analysis to complete for your experiments. In addition to plotting and normalizing the data, you will complete statistical analysis to determine the significance of your results.

Statistics are mathematical tools used to analyze, interpret, and organize data. The specific tools that you will use are confidence intervals (CI) and the Student's t-test. To begin, review the following definitions:

  • Mean (or average) is defined as:
Sp17 20.109 M2D9 mean equation.png
  • With infinite data, the mean (χι) approaches the true mean (μ).
  • Standard deviation measures the variation in the data and is defined as:
Sp17 20.109 M2D9 stddev equation.png
  • With infinite data, the standard deviation (s) approaches the true standard deviation (σ).

Because standard deviation is only justified when sufficient data have been collected to generate a normal curve, you will use confidence intervals to report the likelihood that your results predict the true mean. A confidence interval is a defined interval that is calculated to define the true mean to a specified level of confidence. Simply, it is possible to define a range in your data set that likely contains the true mean based on the calculated mean.

  • Confidence interval is defined as:
Sp17 20.109 M2D9 CI equation.png

In your data, you should use the CI to generate error bars due the low n. Be sure to report which confidence level was used to calculate the intervals reported. So, what does this all mean in regard to the data you will report? As an example, if the calculated χι of a data set equals 80 au there is a 95% chance the μ is between 50 au and 110 au, where au = arbitrary units. And how does this relate to s? If you know the μ, the σ represents a 68% confidence interval.

Lastly, you will use Student's t test to report if your data are statistically different between treatments.

  • Student's t test is defined as:
Sp17 20.109 M2D9 tcalc equation.png

The value you calculate with the Student's t test equation is referred to as tcalculated. This tcalculated value is compared to the ttabulated value in the the t table, according to the appropriate n - 1 using the p-value for the two-tailed distribution (which assumes that you do not know how the data will shift). If the tcalculated value is greater than the ttabulated, then the data sets are significantly different at the specific p-value. So, what does this all mean in regard to the data you will report? As an example, if the tcalculated for a data set with n - 1 = 10 is 3 (given that the ttabulated is 2.228), then the data sets are different with a p-value ≤ 0.05. Which means that there is less that a 5% chance that the data sets are the same.

Part 1: Visualize H2AX assay results

  1. Make sure to have TBS solution available before you start. Aspirate the secondary antibody solution off the coverslip and immediately add 150 μL of TBS. Do not let the coverslips dry out during this process.
  2. To complete the post secondary wash, add 150 μL of TBS per coverslip, let incubate at room temperature for 3 min covered, then aspirate.
    • Repeat this step twice.
  3. Obtain glass slides from the front laboratory bench and label your slides with all of your experimental information and group name, add 5 μL of mounting media to the slide.
  4. Aspirate the final TBS wash and using tweezers place the coverslip cell-side down on the mounting media "spot" on the microscope slide. Try your best to avoid bubbles by slowly placing the coverslip over the mounting media.
    • The cell-side of the coverslide is the side that was facing up in the staining chamber.
  5. Complete Steps #3-4 for coverslips from all of the coverslips you stained.
  6. Alert the teaching faculty when all of your microscope slides are ready and you will be escorted to the microscope in the Engelward laboratory.

Part 2: Practice statistical analysis

Review these data from an experiment where cells were exposed to increasing amounts of radiation. Your goal is to determine if a statistically significant amount of DNA damage was induced. For the purpose of this exercise, the values in the spreadsheet are in arbitrary units of 'DNA damage', where the higher numbers indicate more damage.

When interpreting the statistics, consider how you may use the information to convince someone that the DNA damage was significant. You may find this spreadsheet, originally created by Prof. Bevin Engelward and modified by the 20.109 staff, helpful for this exercise. At a minimum, you should post a bar plot of the data with 95% confidence intervals and indicate if there is a statistically significant difference (i.e. provide a p-value) between conditions in your Benchling notebook.

Part 3: Complete data analysis

Use the tools above to analyze the data for your genomic instability (CometChip) and sub-nuclear foci (gamma-H2AX) experiments. The figures / analyses in your Data summary should include measures of variability (i.e. confidence intervals) and significance (i.e. p-values).

Analyze H2AX images

Please obtain your images from the instructors. Three sets of images (i.e. image stacks) were taken per condition, and each image stack contains images from two channels--DAPI (blue) and FITC (green). Remember that the secondary antibody you used for the gamma-H2AX staining was conjugated to an Alexa488 fluorophore, which emits green light. For each image stack, you will use ImageJ to 1) identify the location of the nuclei using the DAPI channel and 2) quantify the total gamma-H2AX fluorescence in the FITC channel at locations specified by the DAPI channel.

Identify intensity thresholds for DAPI channel
Example of thresholding cell nuclei using ImageJ

The first thing you will need to do is identify intensity thresholds that will properly identify the cell nuclei in all the images. To be consistent and fair in analyzing fluorescence images, it is good practice to use the same intensity thresholds on all the images.

  1. Open ImageJ
  2. Open one image stack from the -DNAPKcs, no treatment condition.
    • The first image you see is the DAPI channel
    • If you scroll to the right, the second image in the stack is the FITC (gamma-H2AX) channel.
  3. While the image is on the DAPI channel, go to Image -> Adjust -> Threshold.
    • A threshold window should pop up
    • Check the box for "Dark Background"
    • Make sure the cell nuclei are highlighted in red.
    • Adjust the threshold values to properly identify the majority of the cells' nuclei.
    • Jot down the threshold values.
  4. Repeat this process for one image from each condition and settle on threshold values for the DAPI channel that you will then use to analyze all the images. Write these values in your notebook.
    • It is best to define the lower threshold value based on your images, and set the upper threshold value as 4095, which is the maximum possible intensity value for a 12-bit image.
    • You can type in threshold values by clicking on the "Set" button in the Threshold window.
  5. Close all open images (File -> Close All).
Test gamma-H2AX quantification on one representative image
  1. In ImageJ, open one image to test the FITC quantification protocol
  2. Split the image stack into two separate images
    • Go to Image -> Stacks -> Stack to Images
    • The DAPI image will have "-0001" as a suffix in its title
    • The FITC (gamma-H2AX) image will have "-0002" as a suffix in its title
  3. Duplicate the DAPI image and turn it into a mask to identify nuclei locations
    • Click on the DAPI image
    • Go to Image -> Duplicate, and click OK on the default title
    • Set the thresholds you chose on the duplicated DAPI image to identify nuclei
      • Go to Image -> Adjust -> Threshold
      • Check the box for "Dark Background"
      • Click on the "Set" button and type in your threshold values (use 4095 for the upper threshold level).
    • Go to Process -> Binary -> Convert to Mask
      • This makes the image black and white, where the white areas should correspond to nuclei locations.
  4. Use the newly created mask to identify locations on the FITC channel in which to quantify the gamma-H2AX signal
    An example of areas identified by intensity thresholds for DAPI channel in ImageJ
    • Go to Analyze -> Set Measurements
      • In the Set Measurements window, make sure the following boxes are checked: Area, Mean gray value, Min & max gray value, Shape descriptors, Integrated density, Display label
      • In the "Redirect to" field, scroll and select the FITC image (suffix -0002). Then press OK.
        • This will direct ImageJ to the FITC image to analyze the metrics you selected in the areas identified by your mask. This will give you information about the gamma-H2AX signal in each nucleus.
  5. Run the analysis by selecting Process -> Analyze Particles
    • In the "Size" field, type 200-Infinity. This will eliminate small, extraneous particles that do not correspond to nuclei.
    • "Circularity" can remain at default values: 0-1
    • "Show" should say "Outlines"
    • Make sure to click the following options: Display results, Exclude on edges, Summarize
    • Press OK to complete.
  6. A window will pop up showing outlines of each nucleus the software identified based on the thresholds you defined. Each identified area is labeled with a red number, corresponding to the left column of the data shown in the "Results" window.
  7. Take a look at the "Results" window to see the results of the analysis. It is good practice to validate the numerical results by comparing them to what you see in the images.
    • The definition of the various measurements performed can be found on the ImageJ website here.
    • Does the nucleus with the largest "Area" correspond to the biggest nucleus you see in the drawing? The area here is in units of square pixels.
    • The RawIntDens field is the total intensity (sum of the intensity of all the pixels) of the corresponding region. Does a region with a high total intensity value correspond to a cell with a high gamma-H2AX signal? Click on the FITC image to double check.
  8. Close the "Results" window and do not save the data, as you will run the analysis on all the files together next.
  9. Close all open windows in ImageJ (File -> Close All)
Quantify gamma-H2AX signal in all images
  1. Ensure that all of your H2AX images are in one folder.
  2. Download AnalyzeH2AX_FITCintensityBatch_Fa18 script here.

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