Lab 5: Data Wrangling II
Package(s)
Schedule
- 08.00 - 08.30: Recap of Lab 4
- 08.30 - 08.35: Lecture
- 08.35 - 08.45: Break
- 08.45 - 12.00: Exercises
Learning Materials
Please prepare the following materials
- Book: R4DS2e: Chapter 5 Data tidying
- Book: R4DS2e: Chapter 14 Strings
- Book: R4DS2e: Chapter 16 Factors
- Book: Chapter 19 Joins
- Video: Tidy Data and tidyr - NB! Start at 7:45 and please note:
gather()
is nowpivot_longer()
andspread()
is nowpivot_wider()
- Video: Working with Two Datasets: Binds, Set Operations, and Joins
- Video: stringr (Playlist with 7 short videos)
Unless explicitly stated, do not do the per-chapter exercises in the R4DS2e book
Learning Objectives
A student who has met the objectives of the session will be able to:
- Understand and apply the various
str_*()
functions for string manipulation - Understand and apply the family of
*_join()
functions for combining data sets - Understand and apply
pivot_wider()
andpivot_longer()
- Use factors in context with plotting categorical data using
ggplot
Exercises
Prologue
Today will not be easy! But please try to remember Hadley’s words of advice:
- “The bad news is, whenever you’re learning a new tool, for a long time, you’re going to suck! It’s gonna be very frustrating! But the good news is that that is typical and something that happens to everyone and it’s only temporary! Unfortunately, there is no way to going from knowing nothing about the subject to knowing something about a subject and being an expert in it without going through a period of great frustration and much suckiness! Keep pushing through!” - H. Wickham (dplyr tutorial at useR 2014, 4:10 - 4:48)
Intro
We are upping the game here, so expect to get stuck at some of the questions. Remember - Discuss with your group how to solve the task, revisit the materials you prepared for today and naturally, the TAs and I are happy to nudge you in the right direction. Finally, remember… Have fun!
Remember what you have worked on so far:
- RStudio
- Quarto
ggplot
filter
arrange
select
mutate
group_by
summarise
- The pipe and creating pipelines
stringr
- joining data
- pivoting data
That’s quite a lot! Well done - You’ve come quite far already! Remember to think about the above tools in the following as we will synthesise your learnings so far into an analysis!
Background
In the early 20s, the world was hit by the coronavirus disease 2019 (COVID-19) pandemic. The pandemic was caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In Denmark, the virus first confirmed case was on 27 February 2020.
While initially very little was known about the SARS-CoV-2 virus, we did know the general pathology of vira. Briefly, the virus invades the cells and hijacks the intra-cellular machinery. Using the hijacked machinery, components for new virus particles are produced, eventually being packed into the viral envelope and released from the infected cell. Some of these components, viral proteins, is broken down into smaller fragments called peptides by the proteasome. These peptides are transported into the endoplasmic reticulum by the Transporter Associated with antigen Processing (TAP) protein complex. Here, they are aided by chaperones bound to the Major Histocompatilibty Complex class I (MHC-I) and then across the Golgi apparatus they finally get displayed on the surface of the cells. Note, in humans, MHC is also called Human Leukocyte Antigen (HLA) and represents the most diverse genes. Each of us have a total of 6 HLA-alleles, 3 from the maternal and 3 from the paternal side. These are further divided into 3 classes HLA-A, HLA-B and HLA-C and the combination of these constitute the HLA-haplotype for an individual. Once the peptide is bound to the MHC class I at the cell surface and exposed, the MHC-I peptide complex can be recognised by CD8+ Cytotoxic T-Lymphocytes (CTLs) via the T-cell Receptor (TCR). If a cell displays peptides of viral origin, the CTL gets activated and via a cascade induces apoptosis (programmed cell death) of the infected cell. The process is summarised in the figure below (McCarthy and Weinberg 2015).
The data we will be working with today contains data on sequenced T-cell receptors, viral antigens, HLA-haplotypes and clinical meta data for a cohort:
- “A large-scale database of T-cell receptor beta (TCR\(\beta\)) sequences and binding associations from natural and synthetic exposure to SARS-CoV-2” (Nolan et al. 2020).
Your Task Today
Today, we will emulate the situation, where you are working as a Bioinformatician / Bio Data Scientist and you have been given the data and the task of answering these two burning questions:
- What characterises the peptides binding to the HLAs?
- What characterises T-cell Receptors binding to the pMHC-complexes?
GROUP ASSIGNMENT: Today, your assignment will be to create a micro-report on these 2 questions! (Important, see: how to)
Getting Started
First, make sure to read and discuss the feedback you got from last week’s assignment!
- Then, once again go to the R for Bio Data Science RStudio Cloud Server
- Make sure you are in your
r_for_bio_data_science
project, you can verify this in the upper right corner - In the same place as your
r_for_bio_data_science.Rproj
file and existingdata
folder, create a new folder and name itdoc
- Go to the aforementioned manuscript. Download the PDF and upload it to your new
doc
folder - Open the PDF and find the link to the data
- Go to the data site (Note, you may have to create and account to download, shouldn’t take too long) . Find and download the file
ImmuneCODE-MIRA-Release002.1.zip
(CAREFUL, do not download the superseded files) - Unpack the downloaded file
- Find the files
peptide-detail-ci.csv
andsubject-metadata.csv
and compress to.zip
files - Upload the compressed
peptide-detail-ci.csv.zip
andsubject-metadata.csv.zip
files to yourdata
folder in your RStudio Cloud session - Finally, once again, create a new Quarto document for today’s exercises, containing the sections:
- Background
- Aim
- Load Libraries
- Load Data
- Data Description
- Analysis
Creating the Micro-Report
Background
Feel free to copy paste the one stated in the background-section above
Aim
State the aim of the micro-report, i.e. what are the questions you are addressing?
Load Libraries
Load the libraries needed
Load Data
Read the two data sets into variables peptide_data
and meta_data
.
Click here for hint
Think about which Tidyverse package deals with reading data and what are the file types we want to read here?Data Description
It is customary to include a description of the data, helping the reader if the report, i.e. your stakeholder, to get an easy overview
The Subject Meta Data
Let’s take a look at the meta data:
|>
meta_data sample_n(10)
# A tibble: 10 × 30
Experiment Subject `Cell Type` `Target Type` Cohort Age Gender Race
<chr> <dbl> <chr> <chr> <chr> <dbl> <chr> <chr>
1 eJL143 15845 naive_CD8 minigene_Set1 Healthy (No … 36 F White
2 eLH42 1588 PBMC C19_cI COVID-19-Con… 63 M <NA>
3 ePD81 2922 PBMC C19_cI COVID-19-Con… 64 M <NA>
4 eQD120 6499 PBMC C19_cI COVID-19-Con… 62 F <NA>
5 eMR22 1565927 PBMC C19_cI COVID-19-Con… 65 M <NA>
6 eQD124 361 PBMC C19_cI COVID-19-B-N… 40 F White
7 eQD123 273 PBMC C19_cI COVID-19-B-N… 49 F White
8 eHH173 19829 naive_CD8 C19_cI Healthy (No … 50 M White
9 eHO141 3238 PBMC C19_cI COVID-19-Acu… NA <NA> <NA>
10 eQD115 2513 PBMC C19_cI COVID-19-Con… 48 M <NA>
# ℹ 22 more variables: `HLA-A...9` <chr>, `HLA-A...10` <chr>,
# `HLA-B...11` <chr>, `HLA-B...12` <chr>, `HLA-C...13` <chr>,
# `HLA-C...14` <chr>, DPA1...15 <chr>, DPA1...16 <chr>, DPB1...17 <chr>,
# DPB1...18 <chr>, DQA1...19 <chr>, DQA1...20 <chr>, DQB1...21 <chr>,
# DQB1...22 <chr>, DRB1...23 <chr>, DRB1...24 <chr>, DRB3...25 <chr>,
# DRB3...26 <chr>, DRB4...27 <chr>, DRB4...28 <chr>, DRB5...29 <chr>,
# DRB5...30 <chr>
Q1: How many observations of how many variables are in the data?
Q2: Are there groupings in the variables, i.e. do certain variables “go together” somehow?
T1: Re-create this plot
Read this first:
- Think about: What is on the x-axis? What is on the y-axis? And also, it looks like we need to do some
count
ing stratified byCohort
andGender
. Recall, that we can stick together adplyr
pipeline with a call toggplot
.
Does your plot look different somehow? Consider peeking at the hint…
Click here for hint
Perhaps not everyone agrees on how to denoteNA
s in data. I have seen -99
, -11
, _
and so on… Perhaps this can be dealt with in the instance we read the data from the file? I.e. in the actual function call to your read_csv()
function. Recall, how can we get information on the parameters of a ?function
- T2: Re-create this plot
Click here for hint
Perhaps there is a function, which cancut
continuous observations into a set of bins?
STOP! Make sure you handled how NA
s are denoted in the data before proceeding, see hint below T1
- T3: Look at the data and create yet another plot as you see fit. Also skip the redundant variables
Subject
,Cell Type
andTarget Type
|>
meta_data sample_n(10)
# A tibble: 10 × 27
Experiment Cohort Age Gender Race `HLA-A...9` `HLA-A...10` `HLA-B...11`
<chr> <chr> <dbl> <chr> <chr> <chr> <chr> <chr>
1 eQD123 COVID-19… 49 F White "A*02:01:0… "A*03:01:01" "B*07:02:01"
2 eLH59 COVID-19… NA <NA> <NA> "A*01:01:0… "A*02:01:01" "B*40:01:02"
3 eXL36 Healthy … 37 F White "A*01:01" "A*02:01" "B*15:01"
4 eHO136 COVID-19… 51 M Hisp… "" "" ""
5 eXL27 Healthy … 24 M White "A*02:01" "A*03:01" "B*27:05"
6 eJL153 COVID-19… 36 M <NA> "A*03:01:0… "A*11:01:01" "B*07:02:01"
7 eXL32 Healthy … 37 F White "A*01:01" "A*02:01" "B*15:01"
8 eLH47 COVID-19… 35 F White "A*01:01:0… "A*02:01:01" "B*07:02:01"
9 eGK111 COVID-19… 50 F <NA> "A*01:01:0… "A*01:01:01" "B*08:01:01"
10 eJL151 COVID-19… 79 F <NA> "A*24:02:0… "A*68:01:01" "B*15:01:01"
# ℹ 19 more variables: `HLA-B...12` <chr>, `HLA-C...13` <chr>,
# `HLA-C...14` <chr>, DPA1...15 <chr>, DPA1...16 <chr>, DPB1...17 <chr>,
# DPB1...18 <chr>, DQA1...19 <chr>, DQA1...20 <chr>, DQB1...21 <chr>,
# DQB1...22 <chr>, DRB1...23 <chr>, DRB1...24 <chr>, DRB3...25 <chr>,
# DRB3...26 <chr>, DRB4...27 <chr>, DRB4...28 <chr>, DRB5...29 <chr>,
# DRB5...30 <chr>
Now, a classic way of describing a cohort, i.e. the group of subjects used for the study, is the so-called table1
and while we could build this ourselves, this one time, in the interest of exercise focus and time, we are going to “cheat” and use an R-package, like so:
NB!: This may look a bit odd initially, but if you render your document, you should be all good!
library("table1") # <= Yes, this should normally go at the beginning!
|>
meta_data mutate(Gender = factor(Gender),
Cohort = factor(Cohort)) |>
table1(x = formula(~ Gender + Age + Race | Cohort),
data = _)
COVID-19-Acute (N=4) |
COVID-19-B-Non-Acute (N=8) |
COVID-19-Convalescent (N=90) |
COVID-19-Exposed (N=3) |
Healthy (No known exposure) (N=39) |
Overall (N=144) |
|
---|---|---|---|---|---|---|
Gender | ||||||
F | 1 (25.0%) | 4 (50.0%) | 33 (36.7%) | 1 (33.3%) | 17 (43.6%) | 56 (38.9%) |
M | 2 (50.0%) | 3 (37.5%) | 36 (40.0%) | 0 (0%) | 21 (53.8%) | 62 (43.1%) |
Missing | 1 (25.0%) | 1 (12.5%) | 21 (23.3%) | 2 (66.7%) | 1 (2.6%) | 26 (18.1%) |
Age | ||||||
Mean (SD) | 50.7 (17.0) | 43.7 (7.74) | 51.5 (15.3) | 35.0 (NA) | 33.3 (9.93) | 44.9 (15.7) |
Median [Min, Max] | 52.0 [33.0, 67.0] | 42.0 [33.0, 53.0] | 53.0 [21.0, 79.0] | 35.0 [35.0, 35.0] | 31.0 [21.0, 62.0] | 42.0 [21.0, 79.0] |
Missing | 1 (25.0%) | 1 (12.5%) | 21 (23.3%) | 2 (66.7%) | 0 (0%) | 25 (17.4%) |
Race | ||||||
African American | 1 (25.0%) | 0 (0%) | 0 (0%) | 0 (0%) | 1 (2.6%) | 2 (1.4%) |
White | 2 (50.0%) | 7 (87.5%) | 13 (14.4%) | 0 (0%) | 28 (71.8%) | 50 (34.7%) |
Asian | 0 (0%) | 0 (0%) | 3 (3.3%) | 0 (0%) | 2 (5.1%) | 5 (3.5%) |
Hispanic or Latino/a | 0 (0%) | 0 (0%) | 1 (1.1%) | 0 (0%) | 0 (0%) | 1 (0.7%) |
Native Hawaiian or Other Pacific Islander | 0 (0%) | 0 (0%) | 0 (0%) | 1 (33.3%) | 0 (0%) | 1 (0.7%) |
Black or African American | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 3 (7.7%) | 3 (2.1%) |
Mixed Race | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 1 (2.6%) | 1 (0.7%) |
Missing | 1 (25.0%) | 1 (12.5%) | 73 (81.1%) | 2 (66.7%) | 4 (10.3%) | 81 (56.3%) |
Note how good this looks! If you have ever done a “Table 1” before, you know how painful they can be and especially if something changes in your cohort - Dynamic reporting to the rescue!
Lastly, before we proceed, the meta_data
contains HLA data for both class I and class II (see background), but here we are only interested in class I, recall these are denoted HLA-A
, HLA-B
and HLA-C
, so make sure to remove any non-class I, i.e. the one after, denoted D
-something.
- T4: Create a new version of the
meta_data
, which with respect to allele-data only contains information on class I and also fix the odd naming, e.g.HLA-A...9
becomesA1
oandHLA-A...10
becomesA2
and so on forB1
,B2
,C1
andC2
(Think: How can werename
variables? And here, just do it “manually” per variable). Remember to assign this new data to the samemeta_data
variable
Click here for hint
Whichtidyverse
function subsets variables? Perhaps there is a function, which somehow matches
a set of variables? And perhaps for the initiated this is compatible with regular expressions (If you don’t know what this means - No worries! If you do, see if you utilise this to simplify your variable selection)
Before we proceed, this is the data we will carry on with:
|>
meta_data sample_n(10)
# A tibble: 10 × 11
Experiment Cohort Age Gender Race A1 A2 B1 B2 C1 C2
<chr> <chr> <dbl> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
1 eHO130 Healthy (N… 28 F White "A*0… "A*0… "B*0… "B*0… "C*0… "C*0…
2 eXL32 Healthy (N… 37 F White "A*0… "A*0… "B*1… "B*4… "C*0… "C*0…
3 eJL151 COVID-19-C… 79 F <NA> "A*2… "A*6… "B*1… "B*4… "C*0… "C*0…
4 eOX56 Healthy (N… 30 M Blac… "A*0… "A*3… "B*5… "B*5… "C*0… "C*0…
5 eLH49 COVID-19-C… 76 M <NA> "A*0… "A*2… "B*0… "B*4… "C*0… "C*1…
6 eHO141 COVID-19-A… NA <NA> <NA> "" "" "" "" "" ""
7 eJL148 COVID-19-C… 41 F <NA> "A*0… "A*0… "B*0… "B*1… "C*0… "C*0…
8 eHO128 COVID-19-C… 49 M <NA> "A*0… "A*0… "B*0… "B*4… "C*0… "C*0…
9 eQD132 COVID-19-C… NA <NA> <NA> "A*0… "A*1… "B*3… "B*4… "C*0… "C*0…
10 eJL161 COVID-19-C… 31 F White "A*0… "A*0… "B*0… "B*1… "C*0… "C*0…
Now, we have a beautiful tidy
dataset, recall that this entails, that each row is an observation, each column is a variable and each cell holds one value.
The Peptide Details Data
Let’s start with simply having a look see:
|>
peptide_data sample_n(10)
# A tibble: 10 × 7
`TCR BioIdentity` TCR Nucleotide Seque…¹ Experiment `ORF Coverage`
<chr> <chr> <chr> <chr>
1 CSAQGTTTSTDTQYF+TCRBV20-X+T… ACCAGTGCCCATCCTGAAGAC… eEE240 ORF1ab
2 CASKGRTLLEAFF+TCRBV02-01+TC… AAGATCCGGTCCACAAAGCTG… eOX54 ORF1ab
3 CASSLPTNLRTETNYGYTF+TCRBV28… ACCAACCAGACATCTATGTAC… eXL27 ORF8
4 CASSLAGGPYNEQFF+TCRBV05-01+… AGCACCTTGGAGCTGGGGGAC… eHO126 nucleocapsid …
5 CATYSHRDTQYF+TCRBV27-01+TCR… CTGATCCTGGAGTCGCCCAGC… eQD137 ORF3a
6 CSARNSDRVDFHEQYF+TCRBV20-X+… AGTGCCCATCCTGAAGACAGC… eEE228 ORF7b
7 CSAGLNNEQFF+TCRBV29-01+TCRB… ACTCTGACTGTGAGCAACATG… eAV91 surface glyco…
8 CASSIRVKDEQYF+TCRBV19-01+TC… ACTGTGACATCGGCCCAAAAG… ePD91 nucleocapsid …
9 CASSEFVGVRETQYF+TCRBV19-01+… ACATCGGCCCAAAAGAACCCG… eHO133 nucleocapsid …
10 unproductive+TCRBV07-09+TCR… TCCAGCGCACAGAGCAGGGGG… eMR17 ORF1ab
# ℹ abbreviated name: ¹`TCR Nucleotide Sequence`
# ℹ 3 more variables: `Amino Acids` <chr>, `Start Index in Genome` <dbl>,
# `End Index in Genome` <dbl>
- Q3: How many observations of how many variables are in the data?
This is a rather big data set, so let us start with two “tricks” to handle this, first:
- Write the data back into your
data
folder, using the filenamepeptide-detail-ci.csv.gz
, note the appending of.gz
, which is automatically recognised and results in gz-compression - Now, check in your data folder, that you have two files
peptide-detail-ci.csv
andpeptide-detail-ci.csv.gz
, delete the former - Adjust your reading-the-data-code in the “Load Data”-section, to now read in the
peptide-detail-ci.csv.gz
file
Click here for hint
Just as you canread
a file, you can of course also write
a file. Note the filetype we want to write here is csv
. If you in the console type e.g. readr::wr
and then hit the Tab key, you will see the different functions for writing different filetypes
Then:
- T5: As before, let’s immediately subset the
peptide_data
to the variables of interest:TCR BioIdentity
,Experiment
andAmino Acids
. Remember to assign this new data to the samepeptide_data
variable to avoid cluttering your environment with redundant variables. Bonus: Did you know you can click theEnvironment
pane and see which variables you have?
Once again, before we proceed, this is the data we will carry on with:
|>
peptide_data sample_n(10)
# A tibble: 10 × 3
Experiment `TCR BioIdentity` `Amino Acids`
<chr> <chr> <chr>
1 eOX43 CASSQGLGTSHEQYF+TCRBV03-01/03-02+TCRBJ02-07 AFLLFLVLI,FLAFLLFLV,F…
2 eOX56 CASSLVATGNTGELFF+TCRBV07-09+TCRBJ02-02 ADAGFIKQY,AELEGIQY,LA…
3 eXL30 CASSPDRGYEQYF+TCRBV27-01+TCRBJ02-07 AFLLFLVLI,FLAFLLFLV,F…
4 eMR17 CASSFYPDTQYF+TCRBV12-03/12-04+TCRBJ02-03 SQASSRSSSR
5 ePD84 CAISQVARQSLGRFNEQFF+TCRBV10-03+TCRBJ02-01 VLAWLYAAV
6 eEE226 CASSIVHLTDEQFF+TCRBV19-01+TCRBJ02-01 FLNGSCGSV
7 eOX52 CASSYGTGYTEAFF+TCRBV06-05+TCRBJ01-01 ELYSPIFLI,LYSPIFLIV,Q…
8 eDH105 CASSQGPAGGYEQYF+TCRBV14-01+TCRBJ02-07 AYILFTRFFYV
9 eEE226 CSALRDGGPGGYGYTF+TCRBV20-X+TCRBJ01-02 AFLLFLVLI,FLAFLLFLV,F…
10 eEE226 CASSVRGGAGFSNEQFF+TCRBV05-08+TCRBJ02-01 ITDVFYKENSY,SEYKGPITD…
Q4: Is this tidy data? Why/why not?
T6: See if you can find a way to create the below data, from the above
|>
peptide_data sample_n(size = 10)
# A tibble: 10 × 5
Experiment CDR3b V_gene J_gene `Amino Acids`
<chr> <chr> <chr> <chr> <chr>
1 eMR16 unproductive TCRBV07-09 TCRBJ02-02 YLQPRTFL,YLQPRTFLL,YYVGYL…
2 eOX46 CASTLGGEKLFF TCRBV04-01 TCRBJ01-04 AFLLFLVLI,FLAFLLFLV,FYLCF…
3 eQD126 CASSDRSDVDEQFF TCRBV27-01 TCRBJ02-01 HTTDPSFLGRY
4 eEE240 CASGLANEQYF TCRBV06-06 TCRBJ02-07 AFPFTIYSL,GYINVFAFPF,INVF…
5 eQD125 CASSLSHSYNEQFF TCRBV27-01 TCRBJ02-01 HTTDPSFLGRY
6 eXL37 CASSVGVGTEAFF TCRBV09-01 TCRBJ01-01 AFLLFLVLI,FLAFLLFLV,FYLCF…
7 eXL31 CASSLEMGRTGGYEQYF TCRBV11-02 TCRBJ02-07 AFLLFLVLI,FLAFLLFLV,FYLCF…
8 eOX54 CASSETGVPYSPLHF TCRBV06-01 TCRBJ01-06 AFPFTIYSL,GYINVFAFPF,INVF…
9 eEE226 CARLRRGVYNEQFF TCRBV06-05 TCRBJ02-01 TVLSFCAFA,VLSFCAFAV
10 eOX52 CASSTGLVLYEQYF TCRBV05-05 TCRBJ02-07 FVDGVPFVV
Click here for hint
First: Compare the two datasets and identify what happened? Did any variables “disappear” and did any “appear”? Ok, so this is a bit tricky, but perhaps there is a function toseparate
a composite (untidy) col
umn into
a set of new variables based on a sep
arator? But what is a sep
arator? Just like when you read a file with C
omma S
eparated V
alues, a separator denotes how a composite string is divided into fields. So, look for such a repeated value, which seem to indeed separate such fields. Also, be aware, that character, which can mean more than one thing, may need to be “escaped” using an initial two backslashed, i.e. “\x”, where x denotes the character needing to be “escaped”
- T7: Add a variable, which counts how many peptides are in each observation of
Amino Acids
Click here for hint
We have been working with thestringr
package, perhaps the contains a function to somehow count the number of occurrences of a given character in a string? Again, remember you can type e.g. stringr::str_
and then hit the Tab key to see relevant functions
|>
peptide_data sample_n(size = 10)
# A tibble: 10 × 6
Experiment CDR3b V_gene J_gene `Amino Acids` n_peptides
<chr> <chr> <chr> <chr> <chr> <dbl>
1 eOX49 CASSSSLLQGAQHF TCRBV27-01 TCRBJ01… TLIGDCATV 1
2 eOX46 CSARDQALGSYEQYF TCRBV20-X TCRBJ02… FPNITNLCPF,Q… 6
3 eQD111 CASSLAGDPGETQYF TCRBV27-01 TCRBJ02… HTTDPSFLGRY 1
4 ePD84 CASSQGLAGVFF TCRBV12-03/12-04 TCRBJ02… FLWLLWPVT,FL… 7
5 eOX43 CASNLVGGSYNEQFF TCRBV25-01 TCRBJ02… AFLLFLVLI,FL… 11
6 ePD85 CASTVGAGLDNEQFF TCRBV19-01 TCRBJ02… SEHDYQIGGYTE… 3
7 eEE240 CSASREQETQYF TCRBV20-01 TCRBJ02… AFLLFLVLI,FL… 11
8 eXL27 CASSQGRSSYEQYF TCRBV03-01/03-02 TCRBJ02… AFLLFLVLI,FL… 11
9 eQD111 CSVEGPGSYEQYF TCRBV29-01 TCRBJ02… HTTDPSFLGRY 1
10 eXL30 CASSQESTGELFF TCRBV04-01 TCRBJ02… AFLLFLVLI,FL… 11
- T8: Re-create the following plot
Q4: What is the maximum number of peptides assigned to one observation?
T9: Using the
str_c()
and theseq()
functions, re-create the below
[1] "peptide_1" "peptide_2" "peptide_3" "peptide_4" "peptide_5"
Click here for hint
If you’re uncertain on how a function works, try going into the console and in this case e.g. typestr_c("a", "b")
and seq(from = 1, to = 3)
and see if you combine these?
- T10: Use, what you learned about separating in T6 and the vector-of-strings you created in T9 adjusted to the number from Q4 to create the below data
Click here for hint
In the console, write?separate
and think about how you used it earlier. Perhaps you can not only specify a vector to separate into
, but also specify a function, which returns a vector?
|>
peptide_data sample_n(size = 10)
# A tibble: 10 × 18
Experiment CDR3b V_gene J_gene peptide_1 peptide_2 peptide_3 peptide_4
<chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
1 eOX52 CAIRIRAGADT… TCRBV… TCRBJ… VLWAHGFEL <NA> <NA> <NA>
2 eEE226 CASSLGLAGGK… TCRBV… TCRBJ… TPINLVRDL <NA> <NA> <NA>
3 eEE240 CASSLGYYEQYF TCRBV… TCRBJ… AFPFTIYSL GYINVFAF… INVFAFPF… MGYINVFAF
4 eEE226 CASSSVRGAYE… TCRBV… TCRBJ… FLNGSCGSV <NA> <NA> <NA>
5 eEE228 CASSSGPYRLD… TCRBV… TCRBJ… IMLIIFWF… MLIIFWFSL <NA> <NA>
6 eJL148 CSARLTGLAGK… TCRBV… TCRBJ… LSPRWYFYY SPRWYFYYL <NA> <NA>
7 eEE226 CSASTDNNEQFF TCRBV… TCRBJ… AFLLFLVLI FLAFLLFLV FYLCFLAFL FYLCFLAF…
8 eMR14 CASSYDRVEAFF TCRBV… TCRBJ… FLQSINFVR FLQSINFV… FLYLYALV… GLEAPFLY…
9 eMR15 CASSLTGGASD… TCRBV… TCRBJ… LSPRWYFYY SPRWYFYYL <NA> <NA>
10 eEE228 CASSLEVDMNT… TCRBV… TCRBJ… FVCNLLLL… LLFVTVYS… TVYSHLLLV <NA>
# ℹ 10 more variables: peptide_5 <chr>, peptide_6 <chr>, peptide_7 <chr>,
# peptide_8 <chr>, peptide_9 <chr>, peptide_10 <chr>, peptide_11 <chr>,
# peptide_12 <chr>, peptide_13 <chr>, n_peptides <dbl>
Q5: Now, presumable you got a warning, discuss in your group why that is?
Q6: With respect to
peptide_n
, discuss in your group, if this is wide- or long-data?
Now, finally we will use the what we prepared for today, data pivoting. There are two functions, namely pivot_wider()
and pivot_longer()
. Also, now, we will use a trick when developing ones data pipeline, while working with new functions, that on might not be completely comfortable with. You have seen the sample_n()
function several times above and we can use that to randomly sample n
observations from data. This we can utilise to work with a smaller data set in the development face and once we are ready, we can increase this n
gradually to see if everything continues to work as anticipated.
T11: Using the
peptide_data
, run a fewsample_n()
calls with varying degree ofn
to make sure, that you get a feeling for what is going onT12: From the
peptide_data
data above, with peptide_1, peptide_2, etc. create this data set using one of the data pivoting functions. Remember to start initially with sampling a smaller data set and then work on that first! Also, once you’re sure you’re good to go, reuse thepeptide_data
variable as we don’t want huge redundant data sets floating around in our environment
Click here for hint
If the pivoting is not clear at all, then do what I do, create some example data:
<- tibble(
my_data id = str_c("id_", 1:10),
var_1 = round(rnorm(10),1),
var_2 = round(rnorm(10),1),
var_3 = round(rnorm(10),1))
…and then play around with that. A small set like the one above is easy to handle, so perhaps start with that and then pivot back and forth a few times using pivot_wider()
/pivot_longer()
. Use View()
to inspect and get a better overview of the results of pivoting.
|>
peptide_data sample_n(10)
# A tibble: 10 × 7
Experiment CDR3b V_gene J_gene n_peptides peptide_n peptide
<chr> <chr> <chr> <chr> <dbl> <chr> <chr>
1 ePD84 CASSFSSSYEQYF TCRBV27-01 TCRBJ… 2 peptide_1 QLMCQP…
2 eEE240 CASSSAGTTQYF TCRBV24-01 TCRBJ… 7 peptide_7 WPVTLA…
3 eEE224 CASSLDTGGTGELFF TCRBV05-01 TCRBJ… 1 peptide_9 <NA>
4 eJL161 CSAREDSGTEIYGYTF TCRBV20-X TCRBJ… 1 peptide_2 <NA>
5 eMR15 CASSSLPGPPSNEQFF TCRBV06-02/0… TCRBJ… 2 peptide_… <NA>
6 eMR13 CASSLDPGTSVYEQYV TCRBV05-06 TCRBJ… 1 peptide_… <NA>
7 eLH43 CASSIGLAEQFF TCRBV19-01 TCRBJ… 2 peptide_8 <NA>
8 eLH47 CASSSPTSGNTDTQYF TCRBV05-04 TCRBJ… 1 peptide_4 <NA>
9 eJL161 CATSAGNTGELFF TCRBV24-01 TCRBJ… 11 peptide_… <NA>
10 eOX46 CASSSPSSSSYNEQFF TCRBV28-01 TCRBJ… 1 peptide_5 <NA>
Q7: You will see some
NA
s in thepeptide
variable, discuss in your group from where these arise?Q8: How many rows and columns now and how does this compare with Q3? Discuss why/why not it is different?
T13: Now, lose the redundant variables
n_peptides
andpeptide_n
, get rid of theNA
s in thepeptide
column, and make sure that we only have unique observations (i.e. there are no repeated rows/observations).
|>
peptide_data sample_n(10)
# A tibble: 10 × 5
Experiment CDR3b V_gene J_gene peptide
<chr> <chr> <chr> <chr> <chr>
1 eQD109 CASSFGVTDTQYF TCRBV28-01 TCRBJ02-03 GNYTVSCLPF
2 eXL37 CSTLLRDRAYNEQFF TCRBV20-X TCRBJ02-01 AFLLFLVLI
3 eHO126 CASSVGGQDNSPLHF TCRBV09-01 TCRBJ01-06 YANRNRFLY
4 eEE240 CASRSGQNYNEKLFF TCRBV12-X TCRBJ01-04 KPLEFGATSAAL
5 eOX56 CASSGTGGANYGYTF TCRBV12-X TCRBJ01-02 NVFAFPFTI
6 eOX49 CASSFRNGYGYTF TCRBV27-01 TCRBJ01-02 FLAFLLFLV
7 eAV93 CASSQLAGDYEQYV TCRBV04-01 TCRBJ02-07 AFPFTIYSL
8 eAV93 CASSLGTSGRFGTQYF TCRBV07-06 TCRBJ02-05 FLAFLLFLV
9 eEE240 CASSPWDSNTGELFF TCRBV18-01 TCRBJ02-02 FLAFLLFLV
10 eOX49 CASSFQGGVHEQFF TCRBV27-01 TCRBJ02-01 YLCFLAFLL
- Q8: Now how many rows and columns and is this data tidy? Discuss in your group why/why not?
Again, we turn to the stringr
package, as we need to make sure that the sequence data does indeed only contain valid characters. There are a total of 20 proteogenic amino acids, which we symbolise using ARNDCQEGHILKMFPSTWYV
.
- T14: Use the
str_detect()
function tofilter
theCDR3b
andpeptide
variables using apattern
of[^ARNDCQEGHILKMFPSTWYV]
and then play with thenegate
parameter so see what happens
Click here for hint
Again, try to play a bit around with the function in the console, type e.g.str_detect(string = "ARND", pattern = "A")
and str_detect(string = "ARND", pattern = "C")
and then recall, that the filter()
function requires a logical vector, i.e. a vector of TRUE
and FALSE
to filter the rows
- T15: Add two new variables to the data,
k_CDR3b
andk_peptide
each signifying the length of the respective sequences
Click here for hint
Again, we’re working with strings, so perhaps there is a package of interest and perhaps in that package, there is a function, which can get the length of a string?|>
peptide_data sample_n(10)
# A tibble: 10 × 7
Experiment CDR3b V_gene J_gene peptide k_CDR3b k_peptide
<chr> <chr> <chr> <chr> <chr> <int> <int>
1 eXL31 CASSYSTGVGADTQYF TCRBV06-05 TCRBJ02-03 MIELSLID… 16 10
2 eEE228 CASSLVGGVGYEQYF TCRBV07-02 TCRBJ02-07 IELSLIDF… 15 10
3 eEE228 CASSQGTALYQETQYF TCRBV04-01 TCRBJ02-05 LIDFYLCFL 16 9
4 eEE224 CASSFYRQGGGAFF TCRBV12-X TCRBJ01-01 GYINVFAF… 14 10
5 eEE224 CASSLRPLGAYNEQFF TCRBV27-01 TCRBJ02-01 QELYSPIFL 16 9
6 eEE226 CASSLGGTEAFF TCRBV11-03 TCRBJ01-01 PLLYDANY… 12 10
7 eOX54 CSAREWGGGYTF TCRBV20-X TCRBJ01-02 LTDEMIAQ… 12 10
8 eOX43 CASSSGSPTGYEQYF TCRBV06-05 TCRBJ02-07 FLPRVFSAV 15 9
9 eOX52 CASRSGQGARETQYF TCRBV05-06 TCRBJ02-05 YEQYIKWP… 15 10
10 eEE240 CASSINGRGPYNEQFF TCRBV19-01 TCRBJ02-01 INFVRIIMR 16 9
- T16: Re-create this plot
Q9: What is the most predominant length of the CDR3b-sequences?
T17: Re-create this plot
Q10: What is the most predominant length of the peptide-sequences?
Q11: Discuss in your group, if this data set is tidy or not?
|>
peptide_data sample_n(10)
# A tibble: 10 × 7
Experiment CDR3b V_gene J_gene peptide k_CDR3b k_peptide
<chr> <chr> <chr> <chr> <chr> <int> <int>
1 eOX52 CASSPRSVNEQFF TCRBV07-08 TCRBJ… SLIDFY… 13 10
2 eOX52 CASRGIGDRAFF TCRBV06-05 TCRBJ… IDFYLC… 12 10
3 eMR12 CASSLRDPTNEKLFF TCRBV27-01 TCRBJ… HTTDPS… 15 11
4 eLH47 CASSLAYEQYF TCRBV12-03/12-… TCRBJ… INVFAF… 11 10
5 eMR20 CASSSRSAYEQYF TCRBV19-01 TCRBJ… LPFFSN… 13 9
6 eEE224 CSASDVTSGSRNGELFF TCRBV20-01 TCRBJ… SLIDFY… 17 10
7 eEE240 CATSGANEQYF TCRBV06-X TCRBJ… TLACFV… 11 10
8 eHH175 CASSFEGLNTGELFF TCRBV28-01 TCRBJ… QYIKWP… 15 9
9 eEE228 CASSYSPGAEHYGYTF TCRBV06-05 TCRBJ… FLWLLW… 16 10
10 eOX43 CASSPFLQPPYNEQFF TCRBV27-01 TCRBJ… NVFAFP… 16 9
Creating one data set from two data sets
Before we move onto using the family of *_join()
functions you prepared for today, we will just take a quick peek at the meta data again:
|>
meta_data sample_n(10)
# A tibble: 10 × 11
Experiment Cohort Age Gender Race A1 A2 B1 B2 C1 C2
<chr> <chr> <dbl> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
1 eQD126 COVID-19-C… 54 F <NA> A*01… A*03… B*07… B*08… C*07… C*07…
2 eQD136 COVID-19-C… NA <NA> <NA> A*02… A*68… B*08… B*15… C*03… C*07…
3 eJL164 COVID-19-B… 33 M White A*02… A*24… B*15… B*40… C*03… C*15…
4 eLH45 COVID-19-C… 53 M <NA> A*02… A*03… B*07… B*51… C*07… C*12…
5 eHO132 COVID-19-C… 65 F White A*02… A*24… B*14… B*35… C*04… C*08…
6 eQD109 COVID-19-C… 61 M <NA> A*03… A*69… B*07… B*07… C*07… C*07…
7 eHO130 Healthy (N… 28 F White A*02… A*03… B*07… B*08… C*07… C*07…
8 eLH57 COVID-19-C… NA <NA> <NA> A*01… A*03… B*07… B*37… C*06… C*07…
9 eQD135 COVID-19-C… 74 M <NA> A*02… A*24… B*07… B*07… C*07… C*07…
10 eJL161 COVID-19-C… 31 F White A*01… A*02… B*08… B*13… C*06… C*07…
Remember you can scroll in the data.
- Q12: Discuss in your group, if this data with respect to the
A1
,A2
,B1
,B2
,C1
andC2
variables is a wide or a long data format?
As with the peptide_data
, we will now have to use data pivoting again. I.e.:
- T18: use either
pivot_wider()
orpivot_longer()
to create the following data:
|>
meta_data sample_n(10)
# A tibble: 10 × 7
Experiment Cohort Age Gender Race Gene Allele
<chr> <chr> <dbl> <chr> <chr> <chr> <chr>
1 eNL187 COVID-19-Convalescent NA <NA> <NA> A1 ""
2 eLH59 COVID-19-Convalescent NA <NA> <NA> A1 "A*01…
3 eMR13 COVID-19-Convalescent NA <NA> <NA> A1 "A*01…
4 eQD111 COVID-19-Convalescent 51 M <NA> C1 "C*07…
5 eQD112 COVID-19-Convalescent 65 M <NA> C2 "C*07…
6 eOX56 Healthy (No known exposure) 30 M Black or Af… A2 "A*33…
7 eMR26 COVID-19-Convalescent 62 M <NA> B1 "B*07…
8 eHO136 COVID-19-Convalescent 51 M Hispanic or… B2 ""
9 ePD91 COVID-19-Convalescent 52 M White B2 ""
10 eMR12 COVID-19-Convalescent NA <NA> <NA> A2 "A*02…
Remember, what we are aiming for here, is to create one data set from two. So:
- Q13: Discuss in your group, which variable(s?) define the same observations between the
peptide_data
and themeta_data
?
Once you have agreed upon Experiment
, then use that knowledge to subset the meta_data
to the variables-of-interest:
|>
meta_data sample_n(10)
# A tibble: 10 × 2
Experiment Allele
<chr> <chr>
1 eHO141 ""
2 eEE224 "C*07:04"
3 eQD123 "C*07:01:01"
4 eXL27 "C*07:04"
5 eJL161 "A*02:01:01"
6 eXL27 "B*27:05"
7 eHH174 "B*51:01"
8 eNL187 ""
9 eOX54 "C*02:10"
10 eEE228 "B*35:03"
Use the View()
function again, to look at the meta_data
. Notice something? Some alleles are e.g. A*11:01
, whereas others are B*51:01:02
. You can find information on why, by visiting Nomenclature for Factors of the HLA System.
Long story short, we only want to include Field 1
(allele group) and Field 2
(Specific HLA protein). You have prepared the stringr
package for today. See if you can find a way to reduce e.g. B*51:01:02
to B*51:01
and then create a new variable Allele_F_1_2
accordingly, while also removing the ...x
(where x
is a number) subscripts from the Gene
variable (It is an artifact from having the data in a wide format, where you cannot have two variables with the same name) and also, remove any NA
s and ""
s, denoting empty entries.
Click here for hint
There are several ways this can be achieved, the easiest being to consider if perhaps a part of the string based on indices could be of interest. This term “a part of a string” is called a substring, perhaps thestringr
package contains a function work with substring? In the console, type stringr::
and hit tab
. This will display the functions available in the stringr
package. Scroll down and find the functionst starting with str_
and look for on, which might be relevant and remember you can use ?function_name
to get more information on how a given function works.
- T19: Create the following data, according to specifications above:
|>
meta_data sample_n(10)
# A tibble: 10 × 3
Experiment Allele Allele_F_1_2
<chr> <chr> <chr>
1 eJL151 C*03:04:01 C*03:04
2 eJL151 C*03:04:01 C*03:04
3 eQD126 B*08:01:01 B*08:01
4 ePD73 A*02:01 A*02:01
5 eMR22 C*16:01:01 C*16:01
6 eHH175 B*07:02 B*07:02
7 eJL162 A*01:01:01 A*01:01
8 eJL147 B*07:06 B*07:06
9 eJL149 C*06:02:01 C*06:02
10 eAV105 A*02:01:01 A*02:01
The asterisk, i.e. *
is a rather annoying character because of ambiguity, so:
- T20: Clean the data a bit more, by removing the asterisk and redundant variables:
|>
meta_data sample_n(size = 10)
# A tibble: 10 × 2
Experiment Allele
<chr> <chr>
1 eEE217 B15:01
2 eQD136 C07:01
3 eQD115 C05:01
4 eXL31 C16:01
5 eHO130 A03:01
6 eAM23 A24:02
7 eLH45 B51:01
8 eQD124 B15:01
9 eAV105 B40:01
10 eOX52 B40:01
Click here for hint 1
Again, thestringr
package may come in handy. Perhaps there is a function remove
, one or more such pesky characters?
Click here for hint 2
Getting a weird error? Recall, that character ambiguity needs to be “escaped”, you did this somehow earlier on…Recall the peptide_data
?
|>
peptide_data sample_n(10)
# A tibble: 10 × 7
Experiment CDR3b V_gene J_gene peptide k_CDR3b k_peptide
<chr> <chr> <chr> <chr> <chr> <int> <int>
1 eOX46 CAISDHTGELFF TCRBV10-03 TCRBJ02-02 RQLLFVVEV 12 9
2 eQD110 CATEQFF TCRBV19-01 TCRBJ02-01 FLQSINFVR 7 9
3 eQD121 CASSPQGNGELFF TCRBV10-02 TCRBJ02-02 HTTDPSFL… 13 11
4 eEE226 CRATSGESNYGYTF TCRBV20-X TCRBJ01-02 HLVDFQVTI 14 9
5 eLH47 CASSPPDRAYEQYF TCRBV27-01 TCRBJ02-07 FLQSINFVR 14 9
6 eOX54 CAWSVSGTGADTQYF TCRBV30-01 TCRBJ02-03 LLFVTVYS… 15 10
7 eXL27 RARQYGDRDTGELFF TCRBV07-03 TCRBJ02-02 SLIDFYLC… 15 10
8 eHH175 CASSAGPHNEQFF TCRBV02-01 TCRBJ02-01 GVVFLHVTY 13 9
9 eOX43 CASSLTLFGNEQFF TCRBV07-03 TCRBJ02-01 SELVIGAVI 14 9
10 eLH48 CAISELKRESYNEQFF TCRBV10-03 TCRBJ02-01 FYLCFLAFL 16 9
- T21: Create a
dplyr
pipeline, starting with thepeptide_data
, which joins it with themeta_data
and remember to make sure that you get only unqiue observations of rows. Save this data into a new variable namespeptide_meta_data
(If you get a warning, discuss in your group what it means?)
Click here for hint 1
Which family of functions do we use to join data? Also, perhaps here it would be prudent to start with working on a smaller data set, recall we could sample a number of rows yielding a smaller development data set
Click here for hint 2
You should get a data set of around +3.000.000, take a moment to consider how that would have been to work with in Excel? Also, in case the servers are not liking this, you can consider subsetting thepeptide_data
prior to joining to e.g. 100,000 or 10,000 rows.
|>
peptide_meta_data sample_n(10)
# A tibble: 10 × 8
Experiment CDR3b V_gene J_gene peptide k_CDR3b k_peptide Allele
<chr> <chr> <chr> <chr> <chr> <int> <int> <chr>
1 eMR13 CAASIMNTEAFF TCRBV02-01 TCRBJ… YEDFLE… 12 14 B40:01
2 eOX52 CASSRQVPETQYF TCRBV19-01 TCRBJ… DFLEYH… 13 9 B40:01
3 eOX46 CASSQVLAGPGQFF TCRBV03-0… TCRBJ… LLFVTV… 14 10 B44:02
4 eEE224 CASSLLASSTDTQYF TCRBV27-01 TCRBJ… LLFLAF… 15 10 B40:01
5 eEE228 CASSMSRGRTDTQYF TCRBV19-01 TCRBJ… VDDPCP… 15 10 B44:02
6 eOX43 CSASGIDNEQFF TCRBV20-X TCRBJ… SLIDFY… 12 10 C07:04
7 eLH48 CASSSIETGDTEAFF TCRBV05-01 TCRBJ… FLPFFS… 15 9 C03:04
8 eJL158 CAWSWTVGSTDTQYF TCRBV30-01 TCRBJ… SASAFF… 15 10 C15:02
9 eOX46 CSVEQGTYEQYF TCRBV29-01 TCRBJ… YINVFA… 12 9 C04:01
10 eOX46 CASSWGANTGELFF TCRBV27-01 TCRBJ… AYSNNS… 14 13 C04:01
Analysis
Now, that we have the data in a prepared and ready-to-analyse format, let us return to the two burning questions we had:
- What characterises the peptides binding to the HLAs?
- What characterises T-cell Receptors binding to the pMHC-complexes?
Peptides binding to HLA
As we have touched upon multiple times, R
is very flexible and naturally you can also create sequence logos. Finally, let us create a binding motif using the package ggseqlogo
(More info here).
- T22: Subset the final
peptide_meta_data
data toA02:01
and unique observations of peptides of length 9 and re-create the below sequence logo
Click here for hint
You can pipe a vector of peptides intoggseqlogo
, but perhaps you first need to pull
that vector from the relevant variable in your tibble? Also, consider before that, that you’ll need to make sure, you are only looking at peptides of length 9
- T23: Repeat for e.g.
B07:02
or another of your favourite alleles
Now, let’s take a closer look at the sequence logo:
- Q14: Which positions in the peptide determines binding to HLA?
Click here for hint
Recall your Introduction to Bioinformatics course? And/or perhaps ask your fellow group members if they know?CDR3b-sequences binding to pMHC
- T24: Subset the
peptide_meta_data
, such that the length of the CDR3b is 15, the allele is A02:01 and the peptide is LLFLVLIML and re-create the below sequence logo of the CDR3b sequences:
Q15: In your group, discuss what you see?
T25: Play around with other combinations of
k_CDR3b
,Allele
, andpeptide
and inspect how the logo changes
Disclaimer: In this data set, we only get: A given CDR3b was found to recognise a given peptide in a given subject and that subject had a given haplotype - Something’s missing… Perhaps if you have had immunology, then you can spot it? There is a trick to get around this missing information, but that’s beyond scope of what we’re working with here.
Epilogue
That’s it for today - I know this is overwhelming now, but commit to it and you WILL be plenty rewarded! I hope today was at least a glimpse into the flexibility and capabilities of using tidyverse
for applied Bio Data Science
…also, noticed something? We spend maybe 80% of the time here on dealing with data-wrangling and then once we’re good to go, the analysis wasn’t that time consuming - That’s often the way it ends up going. You’ll spend a lot of time on data handling, and getting the tidyverse toolbox in your tool belt will allow you to be so much more efficient in your data wrangling, so you can get to the fun part as quickly as possible!
Today’s Assignment
After today, we are halfway through the labs of the course, so now is a good time to spend some time recalling what we have been over and practising writing a reproducible Quarto-report.
Your group assignment today is to condense the exercises into a group micro-report! Talk together and figure out how to distil the exercises from today into one small end-to-end runnable reproducible micro-report. DO NOT include ALL of the exercises, but rather include as few steps as possible to arrive at your results. Be very concise!
But WHY? WHY are you not specifying exactly what we need to hand in? Because we are training taking independent decisions, which is crucial in applied bio data science, so take a look at the combined group code, select relevant sections and condense - If you don’t make it all the way through the exercises, then condense and present what you were able to arrive at! What do you think is central/important/indispensable? Also, these hand ins are NOT for us to evaluate you, but for you to train creating products and the get feedback on your progress!
IMPORTANT: Remember to check the ASSIGNMENT GUIDELINES
…and as always - Have fun!