Lab 1_Intro to Graphing & Statistics

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University of California, Berkeley *

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Feb 20, 2024

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Bio 1AL: General Biology Laboratory Monday, Jan 22nd Lab 1: Safety and introductions to graphing and statistics Agenda • How to be successful in Bio1AL • Descriptive statistics and graphing • T-test • Chi-squared Test
Pre-lab Assessment (3pts X 11)- drop lowest 30pts (~8%) Post-Lab Assessment (3pts X 11)-drop lowest 30pts (~8%) Lab Report (10pts X 11)-drop lowest 100pts (~26%) Lab Clean Up (2pts X 11) 22pts (~5%) Lab Flowchart (2pts X 11)-lowest score dropped 20pts (~5%) Quizzes 1-3 (pts vary)- no score dropped 85pts (~22%) Lab Exam (cumulative) 4/24 8pm-10pm 100pts (~26%) Total 387 pts Key Points From the Syllabus On Grading…
More on pre- and post-lab assessments… Pre-lab Assessments • Due Mondays at 5pm • ONLY 1 Attempt Post-lab Assessments • Due Sundays at 11:59pm • 2 Attempts—take the highest score
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In Lab 9, you will discuss a research article about human genetics and the ability to taste a bitter tastant (Bufe et al., 2005) In Lab 1, you will try to recreate one of the figures in order to learn how to make graphs and error bars in Excel Descriptive Statistics and graphing
Taste sensation relies on tastant binding receptor Bitter taste receptors are called TAS2R
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When PTC binds TAS2R38 calcium concentration increases inside the taste receptor cell PROP is another bitter tastant that binds TAS2R38
NH2 CO 2 H TAS2R38 Receptor The TAS2R38 protein has slightly different sequences in different people Two common variants in population: PAV and AVI
Approach: Express the different TAS2R38 receptor variants in cells that normally do not respond to PTC or PROP Transfect with TAS2R38 PAV DNA sequence HEK293 cells Measure [Ca +2 ] in cytoplasm using fluorescent dye. Increased Ca +2 concentration causes the dye to get brighter. Add PTC or PROP è activate TAS2R38 receptor è increased [Ca +2 ] Measure the change in fluorescence intensity. TAS2R38 Receptor
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Researchers tested the response to different concentrations of PTC They repeated the experiments three times, using the same procedures, but with a different set of HEK cells AU = arbitrary units, fluorescence intensity does not have units. But the bigger the number, the greater the change in fluorescence (the greater change in Ca +2 ) Data for making graph
Revisit figure 1A
Some TAS2R38 receptor variants don’t show any response to PTC How do we know that the cells are still alive and functioning? Test the HEK cell response to another signal called SST, which binds other receptors naturally expressed in HEK cells Normalize the response to PTC to the response to SST for each trial This also helps standardize the data and control for differences in how much dye was loaded into the cells for each trial Normalize to SST response
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Write an equation for cell B18 that will normalize that data point to the SST response. Practice Question
Make your graph and edit it so it looks like Fig 1A 1. Normalize the data of each trial to the 0.1 µM SST response 2. Average the normalized data from the three trials 3. Calculate the standard error of the mean of the normalized data 4. Graph the averaged data as an XY scatter plot with connecting lines a) Change the x-axis to a log scale b) Add custom error bars to the data, using the standard error of the mean values that you calculated c) Add axes labels and format the graph so it looks like Figure 1 Watch the Excel tutorials on bCourses before class! The best way to learn how to use Excel is just to click on things and see what happens to the graph Work with other students in your lab and help each other!
1. Understand proper laboratory and building safety procedures, including evacuation routes. 2. Know the location of safety items: exits, evacuation sites, fire alarms, fire extinguishers, first aid kits, etc. 3. Follow safe lab practices, use protective equipment, and know how to handle waste and spills. 4. Describe different data types and determine the appropriate graph type based on data type. 5. Calculate descriptive statistics (mean, standard deviation, standard error of the mean) using Excel. 6. Graph quantitative data using Excel and apply error bars appropriately. 7. Understand when to use different types of t-tests (paired vs independent, one-tailed vs two-tailed). 8. Conduct an independent sample t-test to determine if two means are significantly different. 9. Conduct a Chi Squared Test of Independence to test a hypothesis. Lab 1 Learning Objectives
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EC 50 is the half maximal effective concentration, or the concentration at which you get half of the max response Figure 1A is a type of dose response curve
Are the EC 50 values significantly different for the response to PTC and PROP? The mean EC 50 for PROP is larger than for PTC, but is the difference due to chance fluctuations or is it significant? Use a t-test to determine if the two means are significantly different
Key Idea: A t-test is an inferential statistic used to determine if there is a significant different between the means of two groups Paired/correlated or independent? One-tailed or two-tailed? Paired/correlated: data in the two groups are from the same samples (Ex. measuring cellular response in one cell before and after a drug treatment) Independent: data in the two groups are from totally different experiments (Ex. EC 50 dat for PTC and PROP) Two-tailed: You want to see if there is a difference in the means in either direction (Ex. If there a difference in the EC 50 means?) One-tailed: You want to see if there is a difference in the means in one specific direction (Ex. Is the EC 50 significantly higher for PROP than for PTC?)
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Hypothesis Testing Null hypothesis (H 0 ): The means of the two groups are the same. In other words, any difference we see in the means is due to random chance. The results of the t-test will be a p-value (between 0-1) The p-value is the probability of obtaining our results if the null hypothesis is correct (differences are due to chance) Ap-value of 0.02 means that there is a 2% chance of obtaining our results due to chance We typically reject the null hypothesis when p < 0.05 and say that there is a significant difference in the means
Practice question I have tracked the number of hours I have been sleeping each night during winter break and for the month before winter break (during the fall semester). I’m curious if there was a difference in the average hours of sleep per night during the end of fall semester compared to during winter break. What is the null hypothesis? What type of t-test should I do? The results of my t-test are p-value = 0.15. How should I interpret these results?
1. Understand proper laboratory and building safety procedures, including evacuation routes. 2. Know the location of safety items: exits, evacuation sites, fire alarms, fire extinguishers, first aid kits, etc. 3. Follow safe lab practices, use protective equipment, and know how to handle waste and spills. 4. Describe different data types and determine the appropriate graph type based on data type. 5. Calculate descriptive statistics (mean, standard deviation, standard error of the mean) using Excel. 6. Graph quantitative data using Excel and apply error bars appropriately. 7. Understand when to use different types of t-tests (paired vs independent, one- tailed vs two-tailed). 8. Conduct an independent sample t-test to determine if two means are significantly different. 9. Conduct a Chi Squared Test of Independence to test a hypothesis. Lab 1 Learning Objectives
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PAV vs AVI TAS2R38 receptor variants and ratings of PTC intensity 0 PTC concentration (μM) Taste intensity Does having two copies of the PAV orAVI variant affect how a subject perceives the taste of PTC? Subjects were given different concentrations of PTC dissolved in water and asked to rate the taste intensity on a scale of 0-100 At the highest concentration of PTC we separated the subjects into two groups “Intense”– subjects who rated the high PTC concentration as > 50 “Not intense” – subjects who rated the high PTC concentration as < 50
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Key Idea: Chi-square test is a statistical test primarily used to whether two categorical variables are likely to be related or not We have two variables 1. Receptor type (PAV/PAV or AVI/AVI) 2. PTC taste intensity (Intense or not intense) Are these two variables independent or associated with each other? In other words, does having a certain receptor variant make a person more likely to have a certain taste perception? Null hypothesis: PTC taste perception is independent of what receptor variant the subjects have. Any differences we see between the two receptor variants id sure to change and is not significant
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Perform a Chi squared test on the data
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This example is on bCourses page 1F Contingency table Normal airways Hypoxic Total Proportion Hearing 135 72 Deaf 82 111 Total Proportion Observed Expected Obs - Exp (O-E) 2 /E Hearing/normal 135 Hearing/hypoxic 72 Deaf/normal 82 Deaf/hypoxic 111 Calculation table Example Chi squared test
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Observed Expected Obs-Exp (O-E) 2 /E Hearing/Normal 135 112.2975 22.7025 4.5896258 Hearing/Hypoxic 72 94.7025 -22.7025 5.4423432 Deaf/Normal 82 104.7025 -22.7025 4.9225521 Deaf/hypoxic 111 88.2975 22.7025 5.8371246 X 2 = 20.791646 Calculation Table Contigency Table Normal Airways Hypoxic Total Proportion Hearing 135 72 207 0.5175 Deaf 82 111 193 0.4825 Total 217 183 400 Proportion 0.5425 0.4575 Chi-square value
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ν (nu) = degrees of freedom = (# columns – 1) x (# rows – 1) Use Chi squared distribution table to determine p-value =(2-1) x (2-1) =1 p<0.001 Reject the null hypothesis X 2 = 20.79
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Flowcharts Flowcharts help you think through the steps of an experiment and diagram out the procedures, so you will have an easier time doing the experiment in class Flowcharts are due before 5pm on Mondays
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Three examples of flowcharts: Google results for “PCR flowchart”
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Preparing plant material fore PCR
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Flowchart from Lab 5 procedures
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How to make Dr. Lones morning coffee
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Lab 1 Objectives Attend your Lab 1 section, be on time and ready to work • Work with PAV receptor dose response data to recreate Figure1A o Remember to add SEM error bars and label axes • Perform t-test on EC 50 data for PTC and PROP • Perform Chi-squared test for PTC intensity data • Work on post-lab 1 while you are in lab and submit by the end of lab (and no later than 4 days after your lab section start time) • Lab 1 post-lab assessment is due Sunday, 1/28 at 11:59pm
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