Prep RNAse clean bench, ice bucket, pipettes, etc Remove sample(s) from -80, take note of which samples you are extracting from. I sometimes even take an iPhone pic for reference. Place samples in ice to thaw. Turn on centrifuge to get it cooling down to 4 degrees C. Make 80% Ethanol if needed. (40ml ETOH, 10ml sterile water) in a 50ml falcon tube. Extract Cut up kidney or other tissue using clean razor Place tissue in tube containing 500uL Trizol (Trizol is in fridge)

tags: respirometry, macmanes title: Calibrating the FMS To work gas tanks: Open valve, connect tube and check flow rate before connecting to the FMS flip uper left switch up so it is at baseline, calibrate system (see below) Close tank, depressurize regulator for storage and turn off Calibrate/Setting the water vapor span:

Mission Statement The MacManes lab strives to be an internationally recognized leader in the field of ecophysiological and evolutionary genomics. To accomplish this goal, we push ourselves to be careful in our observations, broad in our questions, and vigorous in our pursuit of research funding. We are generous in the dissemination our products. Indeed, our vision for the future of science is collaborative more so than competitive. To this end, lab members should be prepared to develop the ability to: Think criticaly, and quantitatively about biological phenomena. Write code to analyze high-throughput sequence data. Treat data analytics the same way you do a wet-lab or field work - as an experiment. General Keys to Success One key to success is to make other people say no to you (rather than you saying no to yourself). Don't not apply for some fellowship/job/position because you don't feel qualified. It's good to be realistic about your qualifications, but imposter syndrome (https://www.chronicle.com/article/Impostor-Syndrome-Is/238418) is real, and powerful.

Author: Jocelyn P. Colella Hypothesis: Males and Females will have significantly different physiological responses (e.g., dependent variables) during similar experiments Plots Heatmaps of bonferonni adjusted p-values for t-tests between males and females across each experiment (in total) and across each dependent variable: males and females are MOSTLY significantly different M and F are NOT different in VO2 or EE under HOT experimental conditions Heatmaps of Bonferonni adjusted p-values for t-tests between males and females across each experiment (BL, hot, cold), day and night, and across each dependent variable:

Author: Jocelyn P. Colella library(ggplot2) library(tidyverse) library(car) library(lubridate) library(RColorBrewer) library(ggpubr) library(ggpmisc) library(gridExtra) library(rlist)

Author: Jocelyn P. Colella Almost all comparisons are SIGNIFICANT at p < 0.05 AFTER Tukey's correction for multiple hits NOT SIGNFICANT: RQ: all males BL vs. Hot (unlogged data only) H2O: all females BL vs. Cold (both logged and unlogged) VCO2: daytime females BL vs. Hot (both logged and unlogged) VO2: nighttime males BL vs. Cold (both logged and unlogged)

Author: Jocelyn P. Colella Example changepoint analysis for BL Females (energy expenditure, EE) Next step: loop this through all datasets and all dependent variables RESULTS 4 to 8 changepoints 4 changepoints clearly delineate transitions

Author: Jocelyn P. Colella two-sample t-test (95% CI) between experiment pairs Sex: F(female), M (male) Experiments: BL (baseline), hot, cold Response variables: EE, RQ, VO2, VCO2, H2Omg Time period: daytime (day), nighttime (night) [transition time periods excluded] NOTE: Did not test for differences between sexes (see Dani's script for those details)

Author: Jocelyn P. Colella R code (figures embedded, summary results at the bottom of page) # TESTING ASSUMPTIONS fOR ANCOVA # Following: https://www.datanovia.com/en/lessons/ancova-in-r/#:~:text=The%20Analysis%20of%20Covariance%20(ANCOVA,two%20or%20more%20independent%20groups.) #ASSUMPTIONS: # 1 - For each independent variable, the relationship between the dependent variable (y) and the covariate (x) is linear # 2 - VIOLATED - homogeneity of regression slopes # 3 - VIOLATED normality of residuals

Author: Jocelyn P. Colella Run on noOL dataset, despite assumption violations ALL comparisons were significant (e.g., mean's differed between treatment groups [BL, hot, cold]) except for comparisons denoted by an askerisk (*) indicating Baseline and Hot conditions did NOT differ EE data Baseline Cold

Author: Jocelyn P. Colella These exclude all outliers identified across all DVs (N 430 total, 215 each sex) Females daytime nighttime Males

Author: Jocelyn P. Colella (e.g., is weight a covariate?) It's not! No significant relationship between weight and ANY response variable. Male and Female EE vs. weight All comparisons insignificant: no relationship between weight and EE Male and Female RQ vs. weight

Author: Jocelyn P. Colella Script identifies outliers (>3 sd from the mean) and creates a new csv with outliers excluded Also manually corrects mis-typed weight prior to all downstream analyses. library(ggplot2) library(tidyverse) library(car) library(lubridate) library(ggpubr)

all_noOL_M <- read.csv("~/Box Sync/UNH/metabolic chamber/Cactus_Mouse_Physiology/data/all_noOL_M.csv") all_noOL_M$time_bucket <- as.POSIXct(round(as.numeric(as.POSIXct(paste(all_noOL_M$StartTime), format="%H:%M:%S"))/60)*60, origin='1970-01-01') meanEE_M <- all_noOL_M[ which(all_noOL_M$experiment == "baseline"),] %>% group_by(time_bucket) %>% summarize(mean(EE)) colnames(meanEE_M)<-c("time_bucket","mean_ee") meanH2O_M <- all_noOL_M[ which(all_noOL_M$experiment == "baseline"),] %>% group_by(time_bucket) %>% summarize(mean(H2Omg)) colnames(meanH2O_M)<-c("time_bucket","mean_h20")

note not publication ready and still need work to look nice. Could not get loop or function to work so I copy and pasted VO2 vo2 <- read.csv("/Users/danielleblumstein/Documents/UNH/metabolic chamber/stats/temp_experiment/VO2_t_tests.csv") #convert to long table VO2_long <- reshape(data=vo2, idvar="VO2", varying = c("BL_day_noOL_F","BL_night_noOL_F","cold_day_noOL_F","cold_night_noOL_F","hot_day_noOL_F","hot_night_noOL_F"), v.name=c("p_val"),

Cage 0 - 22.678g - 1057M Cage 1 - 22.790g - 1062M Cage 2 - 22.881g - 1053M Cage 3 - 19.431g - 1012M Cage 4 - 20.79g - 1011M

title: "temp experiment stats" author: "Dani Blumstein" date: "5/20/2020" output: html_document t test summary table variable x y t

title: "All Analysis and data" author: "Dani Blumstein" date: "3/2/2020" output: html_document bring in libs library(devtools) library(tidyverse) library(lubridate) library(readr)