Zebrafish and Caffeine - Postlab assignment

docx

School

University of Minnesota-Twin Cities *

*We aren’t endorsed by this school

Course

1961

Subject

Chemistry

Date

Feb 20, 2024

Type

docx

Pages

Uploaded by CorporalOkapiPerson1027

Zebrafish and Caffeine – Post lab Assignment Questions: 1. Paste your answers to the two in-class questions from Part 1 of the Lab manual. a. Outline two possible plans for collecting 15 heart rate measurements for both your experimental and comparison conditions. i. Estimate the heart rate by taking 5 fish and visually measure the heart rate for 15 seconds and multiply that by four. Then give the 5 fish the caffeine concentration and visually measure the heart rate for 15 seconds, and multiply that by four, then wait (a certain amount of time, not sure specifically, maybe a few minutes) a visually measure again, totaling 15 measurements. This process should then be repeated for the control group. ii. Estimate the heart rate by taking 15 fish (5 of those being the control group), and visually measure the heart rate after giving the caffeine concentration, do this by visually measuring for 15 seconds and multiplying by four. b. Using the procedure included in your lab manual and discussions from class, outline the strategy that you will use for data collection. Type or write your answer below. i. Measure 5 fish and measure them three times each (for each group; experimental and control group). 2. Explain, in your own words, what the control is for this experiment (what goes into that petri dish?) and why it is an appropriate control? a. The control is embryo water. It is appropriate because it gives a baseline for zebrafish embryo heart rate. This is the solution that the zebrafish embryos developed in, both experimental and control group have experienced these conditions. 3. Based on the two plots that you made, explain the overall trend in the data if there is one. If there is no clear trend, give two ideas about why that is the case. a. It is hard to identify a trend in the data, but generally speaking the control group had a higher average heart rate compared to the experimental group. 4. Does there seem to be a difference between the heart rates of the control zebrafish and the heart rates of the caffeine-treated embryos? If so, do you think that difference is biologically significant? How do you think we can determine whether a difference is biologically significant or due to chance (natural variability between individual zebrafish)? a. The median heart rate for the control group sat at about 115 bpm while the experimental group sat at about 70 bpm. I think this is biologically significant. You could determine if the difference is biologically significant by calculating the p-value, and if it is greater than 0,05, it is considered significant. 5. Why would some researchers choose to use R for their data analysis instead of a visual spreadsheet, like Excel?

a. Researchers may chose R over other spreadsheets, due to the fact that R can organize unstructured data, while making statistical models for you. Bar Graphs showing error bars and Scatterplot: R Script: R version 4.3.2 (2023-10-31) -- "Eye Holes" Copyright (C) 2023 The R Foundation for Statistical Computing Platform: x86_64-pc-linux-gnu (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions.

Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > caffeine <- read.csv('heart_rate_data.csv') > View(caffeine) > ls(caffeine) [1] "BPM" "caffeine" "trial" > colnames(caffeine) <- c("Trial_Number","Caffeine_Concentration","BPM") > head(caffeine) Trial_Number Caffeine_Concentration BPM 1 1 0 138 2 1 0 144 3 1 0 126 4 1 0 132 5 1 0 130 6 1 0 126 > library(ggplot2) > library(Hmisc) Attaching package: ‘Hmisc’ The following objects are masked from ‘package:base’: format.pval, units > > ggplot(data=caffeine, aes(x=Caffeine_Concentration, y=BPM)) + geom_point() > > caffeine$Caffeine_Concentration <- as.factor(caffeine$Caffeine_Concentration) > > ggplot(data=caffeine, aes(x=Caffeine_Concentration, y=BPM)) + geom_point() > > ggplot(data=caffeine, aes(x=Caffeine_Concentration, y=BPM)) + geom_point(position = "jitter") > > caffeine <- subset(caffeine, caffeine$BPM > 0) > ggplot(data=caffeine, aes(x=Caffeine_Concentration, y=BPM)) + geom_point(position = "jitter") + xlab("Caffeine Concentration (mM)") + ylab("Heart Rate (bpm)") > my_first_scatter_plot <- ggplot(data=caffeine, aes(x=Caffeine_Concentration, y=BPM)) + geom_point(position = "jitter") + xlab("Caffeine Concentration (mM)") + ylab("Heart Rate (bpm)")

Your preview ends here

Eager to read complete document? Join bartleby learn and gain access to the full version

Access to all documents
Unlimited textbook solutions
24/7 expert homework help

> View(my_first_scatter_plot) > View(my_first_scatter_plot) > View(caffeine) > View(my_first_scatter_plot) > ggsave(filename = "Chloe_scatterplot.png", my_first_scatter_plot) Saving 5.56 x 2.89 in image > library(ggplot2). Error: unexpected symbol in "library(ggplot2)." > library(ggplot2) > ggplot(caffeine, aes(x=Caffeine_Concentration,y=BPM, fill = Caffeine_Concentration))+ + ggplot(caffeine, aes(x=Caffeine_Concentration,y=BPM, fill = Caffeine_Concentration))+ + geom_bar(position = "dodge", stat = "summary", fun = "mean") Error in `ggplot_add()`: ! Can't add `ggplot(caffeine, aes(x = Caffeine_Concentration, y = BPM, fill = Caffeine_Concentration))` to a <ggplot> object. Run ` rlang::last_trace() ` to see where the error occurred. > rlang::last_trace() <error/rlang_error> Error in `ggplot_add()` : ! Can't add `ggplot(caffeine, aes(x = Caffeine_Concentration, y = BPM, fill = Caffeine_Concentration))` to a <ggplot> object. --- Backtrace: ▆ 1. └─ggplot2:::`+.gg`(...) 2. └─ggplot2:::add_ggplot(e1, e2, e2name) 3. ├ ─ggplot2::ggplot_add(object, p, objectname) 4. └─ ggplot2:::ggplot_add.default( object, p, objectname ) Run rlang::last_trace(drop = FALSE) to see 2 hidden frames. > ggplot(caffeine, aes(x=Caffeine_Concentration,y=BPM, fill = Caffeine_Concentration))+ + geom_bar(position = "dodge", stat = "summary", fun = "mean") > > ggplot(caffeine, aes(x=Caffeine_Concentration,y=BPM, fill = Caffeine_Concentration))+ + geom_bar(position = "dodge", stat = "summary", fun = "mean")+ + xlab("Caffeine Concentration (mM)") + + ylab("Heart Rate (bpm)") > > ggplot(caffeine, aes(x=Caffeine_Concentration,y=BPM, fill = Caffeine_Concentration))+ + geom_bar(position = "dodge", stat = "summary", fun = "mean")+ + xlab("Caffeine Concentration (mM)") + + ylab("Heart Rate (bpm)") + + stat_summary(fun.data = mean_sdl, fun.args = list(mult = 1), show.legend = FALSE) >

> ggplot(caffeine, aes(x=Caffeine_Concentration,y=BPM, fill = Caffeine_Concentration))+ + geom_bar(position = "dodge", stat = "summary", fun = "mean")+ + xlab("Caffeine Concentration (mM)") + + ylab("Heart Rate (bpm)") + + stat_summary(fun.data = mean_se, show.legend = FALSE) > > ggplot(caffeine, aes(x=Caffeine_Concentration,y=BPM, fill = Caffeine_Concentration))+ + geom_bar(position = "dodge", stat = "summary", fun = "median") > > ggplot(caffeine, aes(x=Caffeine_Concentration,y=BPM, fill = Caffeine_Concentration)) + + geom_bar(position = "dodge", stat = "summary", fun = "median") + + xlab("Caffeine Concentration (mM)") + + ylab("Median `Heart Rate (bpm)") > > ggsave(filename = "Chloe_bargraph.png", mean_bargraph) Saving 2.65 x 2.85 in image Error: object 'mean_bargraph' not found > View(my_first_scatter_plot) > View(my_first_scatter_plot) > View(my_first_scatter_plot)