Murtaugh 2022 Appl Comp Genomics Tidyverse lecture.pptx-1.pptx

Charlie Murtaugh
EIHG 4420B
801-581-5958
murtaugh@genetics.utah.edu
Welcome to the Tidyverse
https://twitter.com/mostbiggestdata/status/

Recommended reading
• R for Data Science – full text is free
on web, but book is easier to read
• H. Wickham (2014) Tidy Data. J.
Stat. Software. v59.
http://dx.doi.org/10.18637/jss.v059
.i10
• K.W. Broman and K.H. Woo (2018)
Data Organization in Spreadsheets.
Am. Statistician, v72.
https://doi.org/10.1080/00031305.2
017.1375989

Outline
• Introduction to tidy data and the Tidyverse –
why bother?
• Getting started with Tidyverse functions –
playing with toy data sets
• Using Tidyverse in a real biology context –
proliferation timelapse data

What is the tidyverse?
• Collection of packages and
functions designed to enhance
visualization and analysis of data,
as well as simplify writing and
reading R code
• Installing “tidyverse” package
brings along all key sub-packages
including ggplot2, dplyr, magrittr,
stringr, readr
• Key concept: tidy data
Hadley Wickham
Chief Scientist, RStudio

Tidy data
• Wickham’s concept:
In tidy data:
1. Each variable forms a column.
2. Each observation forms a row.
3. Each type of observational unit forms a table.
Wickham, J. Stat. Software 2014

Usefulness of tidy data
• WHO tuberculosis cases per country, broken down by
gender and age of patients
• Original dataset (“top left corner” of large spreadsheet)

Usefulness of tidy data
• Tidied dataset
aka “narrow” data
(tidyverse pivot_longer() function)

Tidy data approach
• Exploratory data analysis – tools for easily examining
and visualizing your data, developing approaches for
statistical analysis, potentially changing your data-
gathering (experimental) methods

Getting started with Tidyverse functions
%>% “pipe” output from one function to another
pivot_longer convert spreadsheet-type data to tidy format
(aka gather)
separate split up single descriptor variable (e.g. spreadsheet
column head) into multiple variables
group_by organize data according to descriptor variables
summarize extract summary information from grouped data
filter isolate subsets of data

Creating a toy dataset – tibble format
• tibbles are like data.frame objects, but they look nicer
and display helpful information
• note the use of the %>% operator, which “pipes” output of
one function (bind_cols) to another (print)
library(tidyverse)
library(cowplot)
temp_df <- bind_cols(sample=c(1,2,3), temp=c(-40, 32, 98.6)) %>%
print()
## # A tibble: 3 x 2
## sample temp
## <dbl> <dbl>
## 1 1 -40
## 2 2 32
## 3 3 98.6

Piping your code for easier writing and reading
• Code involving sequential operations on the same data
can be much more readable with pipes
• Of particular use: %>% print() at the end of a line
of code will show you what that code produced
# same result, different ways to get there
test <- c(1, 2, 3, 4)
test_sqrt <- sqrt(test)
print(test_sqrt)
c(1, 2, 3, 4) %>% sqrt() %>% print()
[1] 1.000000 1.414214 1.732051 2.000000

Piping your code for easier writing and reading
• Code involving sequential operations on the same data
can be much more readable with pipes
• Of particular use: %>% print() at the end of a line
of code will show you what that code produced
# same result, different ways to get there
test <- c(1, 2, 3, 4)
test_sqrt <- sqrt(test)
print(test_sqrt)
c(1, 2, 3, 4) %>% sqrt() %>% print()
[1] 1.000000 1.414214 1.732051 2.000000
https://twitter.com/strnr/status/1047203915232661505

Creating new columns or changing
existing ones with mutate
• A nice trick of mutate: you can put multiple
sequential operations into a single call, and even refer
back to variables you just created in the same line of
code
# use "mutate" to create new column with temperature in Celsius
temp_df <- mutate(temp_df, tempC=(temp-32)*5/9) %>% print()
## sample temp tempC
## <dbl> <dbl> <dbl>
## 1 1 -40 -40
## 2 2 32 0
## 3 3 98.6 37

One mutate, multiple operations
# create toy data, again
temp_df <- bind_cols(time=c(1,2,3), temp=c(-40, 32, 98.6))
# now let's add both Celsius and Kelvin temperatures in one command
temp_df <- mutate(temp_df, tempC=(temp-32)*5/9,
tempK=tempC+273.15) %>%
rename(tempF = temp) %>%
print()
## time tempF tempC tempK
## <dbl> <dbl> <dbl> <dbl>
## 1 1 -40 -40 233.
## 2 2 32 0 273.
## 3 3 98.6 37 310.

Summarizing data with summarize –
a toy example
• Let’s compare car models based on number of cylinders
data(mpg)
print(mpg) # just to check what the data look like
## # A tibble: 234 x 11
## manufacturer model displ year cyl trans drv cty hwy fl class
## <chr> <chr> <dbl> <int> <int> <chr> <chr> <int> <int> <chr> <chr>
## 1 audi a4 1.8 1999 4 auto… f 18 29 p comp…
## 2 audi a4 1.8 1999 4 manu… f 21 29 p comp…
## 3 audi a4 2 2008 4 manu… f 20 31 p comp…
## 4 audi a4 2 2008 4 auto… f 21 30 p comp…
## 8 audi a4 q… 1.8 1999 4 manu… 4 18 26 p comp…
## 9 audi a4 q… 1.8 1999 4 auto… 4 16 25 p comp…
## 10 audi a4 q… 2 2008 4 manu… 4 20 28 p comp…
## # … with 224 more rows
Tidyverse_lecture_proliferation_code_2022.R

## # A tibble: 234 x 11
## # Groups: cyl [4]
## manufacturer model displ year cyl trans drv cty hwy fl class
## <chr> <chr> <dbl> <int> <int> <chr> <chr> <int> <int> <chr> <chr>
## 3 audi a4 2 2008 4 manu… f 20 31 p comp…
## 4 audi a4 2 2008 4 auto… f 21 30 p comp…
## 8 audi a4 q… 1.8 1999 4 manu… 4 18 26 p comp…
## 9 audi a4 q… 1.8 1999 4 auto… 4 16 25 p comp…
## 10 audi a4 q… 2 2008 4 manu… 4 20 28 p comp…
## # … with 224 more rows
group_by: organize data based on
descriptor variable
• Can group data by as many variables as you have
mpg_by_cyl <- group_by(mpg, cyl) %>% print()

## cyl n hwy_mean hwy_sd displ_mean displ_sd
## <int> <int> <dbl> <dbl> <dbl> <dbl>
## 1 4 81 28.8 4.52 2.15 0.315
## 2 5 4 28.8 0.5 2.5 0
## 3 6 79 22.8 3.69 3.41 0.472
## 4 8 70 17.6 3.26 5.13 0.589
summarize: perform functions on
groups within dataset
• summarize returns new data frame, with grouping
variable(s) on left and function results on right
mpg_cyl_summarize <- summarize(mpg_by_cyl,
n=n(),
hwy_mean=mean(hwy),
hwy_sd=sd(hwy),
displ_mean=mean(displ),
displ_sd=sd(displ))
print(mpg_cyl_summarize)

filter to look at specific subsets of data
• Let’s find out who makes the best automatic-
transmission cars in terms of highway mileage (top 25%)
• We can call filter with logical arguments, return only
data that satisfy them
mpg_auto <- filter(mpg, str_detect(trans, 'auto'))
mpg_auto_best <- filter(mpg_auto, hwy > quantile(hwy, 0.75))
mpg_auto_best_who <- count(mpg_auto_best, manufacturer) %>% print()
## manufacturer n
## <chr> <int>
## 1 audi 4
## 2 chevrolet 3
## 3 honda 4
## 4 hyundai 3
## 5 nissan 2
## 6 pontiac 2
## 7 subaru 1
## 8 toyota 9
## 9 volkswagen 7

Making it simpler with the pipe
filter(mpg, str_detect(trans, 'auto')) %>%
filter(hwy > quantile(hwy, 0.75)) %>%
count(manufacturer) %>%
ggplot(aes(x=manufacturer, y=n)) +
geom_bar(stat='identity') +
xlab('manufacturer') +
ylab('number of models') +
theme(axis.text.x=element_text(angle = 45, hjust=1))

Making it simpler with the pipe
filter(mpg, str_detect(trans, 'auto')) %>%
filter(hwy > quantile(hwy, 0.75)) %>%
count(manufacturer) %>%
arrange(desc(n)) %>%
mutate(manufacturer=factor(manufacturer, levels=manufacturer)) %>%
ggplot(aes(x=manufacturer, y=n)) +
geom_bar(stat='identity') +
xlab('manufacturer') +
ylab('number of models') +
theme(axis.text.x=element_text(angle = 45, hjust=1))

ggplot2 package – the “grammar of
graphics”
• ggplot is extremely powerful
and extremely
complicated/esoteric function
• very nice introduction by
Joachim Goedhart, for
biologists:
https://thenode.biologists.com/
visualizing-data-one-more-time/
education/
• don’t feel bad about consulting
Google/StackExchange!

Analyzing proliferation data – wrangling,
transforming and visualizing
• Cell counting and replating assay (long-term
proliferation) of human pancreatic cancer cell lines:
Tidyverse_lecture_proliferation_code_2022.R
PDAC_3T3_data_simplified.xlsx
• Key points of this experiment are as follows:
– Data consists of counts of cells plated into 35 mm tissue
culture dish on day 0, and counts of cells harvested 3 days
later – repeated over 4-5 passages total
– Four cell lines total: Panc1, MiaPaCa2, Su8686, SW1990
– Cells express either EGFP (negative control) or Ptf1a, a TF
that we hypothesize will inhibit their proliferation, inducible
with doxycycline (DOX); untreated cells used as controls
– 2-3 independent experiments per line

“3T3 assay” – measuring cumulative
population growth over time
• 3T3 = 3 days between
splits, initially plated at
3x105
cells per 50 mm dish
• Easy and quantitative
approach for measuring
long-term effects on cell
proliferation and survival
Todaro and Green, J Cell Biol 1963

Original data in Excel spreadsheet
experiment plating sample line virus treatment plating_1 plating_2 plating_3 plating_4 plating_5 harvest_1 harvest_2 harvest_3 harvest_4 harvest_5
1 4/21/2018 1 Panc1 EGFP 1.77 1.77 1.77 1.77 8.5 9.0 7.8 9.6
1 4/21/2018 2 Panc1 EGFP dox 1.77 1.77 1.77 1.77 6.8 9.5 8.2 6.1
1 4/21/2018 3 Panc1 Ptf1a 1.77 1.77 1.77 1.77 8.8 7.9 5.7 7.2
1 4/21/2018 4 Panc1 Ptf1a dox 1.77 1.77 1.77 1.77 5.8 11.0 6.4 6.6
1 4/21/2018 5 Su8686 EGFP 1.77 1.77 1.77 1.77 6.7 8.5 7.6 5.6
1 4/21/2018 6 Su8686 EGFP dox 1.77 1.77 1.77 1.77 5.2 7.2 6.1 5.8
1 4/21/2018 7 Su8686 Ptf1a 1.77 1.77 1.77 1.77 13.0 3.8 6.4 9.2
1 4/21/2018 8 Su8686 Ptf1a dox 1.77 1.77 1.77 1.30 5.3 3.9 1.3 2.0
2 4/22/2018 1 MiaPaCa2 EGFP 1.77 1.77 1.77 2.00 2.00 5.0 4.6 6.1 8.0 9.3
2 4/22/2018 2 MiaPaCa2 EGFP dox 1.77 1.77 1.77 2.00 2.00 3.0 5.8 2.0 5.5 7.1
2 4/22/2018 3 MiaPaCa2 Ptf1a 1.77 1.77 1.77 2.00 2.00 5.8 5.5 7.2 9.7 10.0
2 4/22/2018 4 MiaPaCa2 Ptf1a dox 1.77 1.77 1.77 2.00 2.00 3.7 4.8 6.6 6.0 6.4
2 4/22/2018 5 SW1990 EGFP 1.77 1.77 1.77 1.77 2.9 5.7 3.9 5.1
2 4/22/2018 6 SW1990 EGFP dox 1.77 1.77 1.77 1.77 2.1 5.1 3.2 2.8
2 4/22/2018 7 SW1990 Ptf1a 1.77 1.77 1.77 1.77 3.6 4.7 4.6 5.4
2 4/22/2018 8 SW1990 Ptf1a dox 1.77 1.77 1.77 1.33 3.1 1.9 1.3 0.4
# cells plated at start of
first passage (x105
)
# cells present in dish at
end of first passage (3
days later) (x105
)

Load data into R and take a quick look
pdac <- read_excel('PDAC_3T3_data_simplified.xlsx') %>% print()
• read_excel (like other tidyverse read functions)
automatically converts data into tibble format
pdac <- mutate(pdac, treatment = replace_na(treatment, 'untreated'))
pdac <- mutate(pdac, treatment=factor(treatment, levels =
c('untreated', 'dox')),
virus=factor(virus, levels = c('EGFP', 'Ptf1a')))

convert data from wide format to
narrow/tidy with pivot_longer*
pdac_tidy <- pivot_longer(pdac,
contains(c('plating_', 'harvest_')),
names_to='observation',
values_to='cell_num') %>% print()
* function formerly known as gather

convert data from wide format to
narrow/tidy with pivot_longer*
pdac_tidy <- pivot_longer(pdac,
contains(c('plating_', 'harvest_')),
names_to='observation',
values_to='cell_num') %>% print()
* function formerly known as gather
# get rid of unnecessary variables
pdac_tidy <- select(pdac_tidy, -plating, -sample)
# remove any missing elements
pdac_tidy <- filter(pdac_tidy, !is.na(cell_num)) %>% print()

Split observation variable into
multiple variables with separate
pdac_tidy <- separate(pdac_tidy, observation,
into=c('observation', 'passage_num'),
convert=T) %>% print()

Let’s make a graph (Figure 1)
• Plotting every harvest number as a point, with
lines connecting serial observations over time
palette <- c('green3', 'orangered’)
# nice palette for graphing GFP (green) vs Ptf1a (orange)
filter(pdac_tidy, observation=='harvest') %>%
ggplot(aes(x=passage_num, y=cell_num,
group=interaction(experiment, virus, treatment),
col=virus, lty=treatment, pch=treatment)) +
geom_line(size=0.75) +
geom_point(alpha=0.4) +
scale_color_manual(values=palette) +
scale_shape_manual(values=c(1,16)) +
scale_linetype_manual(values=c(3,1)) +
facet_wrap(~line, scales='free') +
theme_bw()

• Wow, much lines, very mess

Let’s convert from absolute cell number to
fold-increase (relative to # plated)
# let's make the data wider again, temporarily
pdac_fold <- pivot_wider(pdac_tidy, names_from=observation,
values_from=cell_num) %>% print()
pdac_fold <- mutate(pdac_fold,
fold_change=harvest/plating, .keep='unused’)
# let's convert fold-change to population doublings, via log2
pdac_fold <- mutate(pdac_fold, doublings=log2(fold_change)) %>%
print()

ggplot(pdac_fold, aes(x=passage_num, y=doublings,
col=virus, lty=treatment)) +
theme_bw()

Let’s generate an actual growth curve by
calculating the cumulative sum of
population doublings (Figure 3)
pdac_fold <- group_by(pdac_fold, line, experiment, virus, treatment) %>%
mutate(cuml_doublings=cumsum(doublings)) %>% ungroup() %>% print()
# how does this look when plotted?
ggplot(pdac_fold, aes(x=passage_num, y=cuml_doublings,
theme_bw()

Let’s generate an actual growth curve by
calculating the cumulative sum of
population doublings (Figure 3)

Instead of plotting individual lines for
each experiment, let’s plot means of
independent experiments
# now let's calculate the mean cumulative doublings (and std deviation) for
each cell line, across experiments
pdac_mean <- group_by(pdac_fold, line, virus, treatment, passage_num) %>%
summarize(cuml_mean=mean(cuml_doublings),
cuml_sd=sd(cuml_doublings),
.groups='drop') %>%
print()

Now: plot the mean population growth as line,
with error bars indicating SDs (Figure 4)
ggplot(pdac_mean, aes(x=passage_num, y=cuml_mean,
group=interaction(virus, treatment),
geom_errorbar(aes(ymin=cuml_mean-cuml_sd,
ymax=cuml_mean+cuml_sd), width=0.1) +
theme_bw()

Now: plot the mean population growth as line,
with error bars indicating SDs (Figure 4)
• Instead of error bars, could we plot individual
observations as points?

Problem: our individual point values and
our mean calculations are in different
data tables
pdac_mean
pdac_fold

ggplot can combine elements with
coordinates specified by multiple data sources
ggplot(pdac_mean, aes(x=passage_num, y=cuml_mean,
group=interaction(virus, treatment),
geom_point(data=pdac_fold, aes(x=passage_num, y=cuml_doublings),
alpha=0.4) +
theme_bw()

Figure 4 – mean growth curves together with
individual data points
• How to assess statistical significance?

Endpoint analysis: analyze interaction between
cell line, virus and treatment at last timepoint
pdac_end <- group_by(pdac_fold, line) %>%
filter(passage_num==max(passage_num)) %>% ungroup() %>%
print()

Create nested tibble with each cell line’s data
separated out
pdac_fold_nest <- group_by(pdac_end, line) %>% nest() %>%
ungroup() %>% print()
# look inside the first one (Panc1)
print(pdac_fold_nest$data[[1]])

Analyze each dataset via ANOVA followed by
TukeyHSD, using map function
# for simplicity, create function for ANOVA modeling each data set,
and returning Tukey HSD results (cleaned up with "tidy)
pd_aov <- function(df) {
aov(cuml_doublings ~ interaction(virus, treatment), data=df) %>%
TukeyHSD() %>% tidy()
}
# call "pd_aov" on each cell line's dataset, using "map" function
pdac_fold_nest <- mutate(pdac_fold_nest,
aov_tukey=map(data, pd_aov)) %>% print()

What do the results look like?
# let's look at the first one (Panc1)
pdac_fold_nest$aov_tukey[[1]]

# what are the p-values for Ptf1a + DOX vs. EGFP + DOX?
pdac_anova_results <- unnest(pdac_fold_nest, cols=aov_tukey) %>%
filter(contrast=='Ptf1a.dox-EGFP.dox') %>%
print()
p=0.986 p=0.0485 p=0.0021 p=0.0093

# correct for multiple comparisons (4 cell lines)
mutate(pdac_anova_results, p.corrected=p.adjust(adj.p.value,
method='bonferroni'))
p=1.0 p=0.194 p= 0.00839 p=0.0372
print()

# correct for multiple comparisons (4 cell lines)
mutate(pdac_anova_results, p.corrected=p.adjust(adj.p.value,
method='bonferroni'))
p=1.0 p=0.194 p= 0.00839 p=0.0372
print()
Is there a better statistical method to
analyze data like this?

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