Lab 5: Data Wrangling II

Package(s)

Schedule

Learning Materials

Please prepare the following materials

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() and pivot_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 word-of-advise:

  • “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
  • pivotting 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 endoplasmatic 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 (MHCI) and then across the Golgi Aparatus 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 MHCI-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 proces is summarised in the figure below.

Image source: 10.3389/fmicb.2015.00021

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:

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:

  1. What characterises the peptides binding to the HLAs?
  2. 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!

MAKE SURE TO READ THE LAST SECTION ON THE ASSIGNMENT

Getting Started

  1. Click here to go to the course RStudio cloud server and login
  2. Make sure you are in your r_for_bio_data_science-project, you can verify this in the upper right corner
  3. In the same place as your r_for_bio_data_science.Rproj-file and existing data-folder, create a new folder and name it doc
  4. Go to the aforementioned manuscript. Download the PDF and upload it to your new doc-folder
  5. Open the PDF and find the link to the data
  6. 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)
  7. Unpack the downloaded file
  8. Find the files peptide-detail-ci.csv and subject-metadata.csv and compress to .zip-files
  9. Upload the compressed peptide-detail-ci.csv.zip- and subject-metadata.csv.zip-files to your data-folder in your RStudio Cloud session
  10. Finally, once again, create a new Quarto document for today’s exercises, containing the sections:
    1. Background
    2. Aim
    3. Load Libraries
    4. Load Data
    5. Data Description
    6. 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 eJL154          83 PBMC        C19_cI        COVID-19-Exp…    35 F      Nati…
 2 eJL164         466 PBMC        C19_cI        COVID-19-B-N…    33 M      White
 3 ePD79         2162 PBMC        C19_cI        COVID-19-Con…    79 F      <NA> 
 4 eLH49         7517 PBMC        C19_cI        COVID-19-Con…    76 M      <NA> 
 5 eLH44         6501 PBMC        C19_cI        COVID-19-Con…    61 F      <NA> 
 6 eHO132          26 PBMC        C19_cI        COVID-19-Con…    65 F      White
 7 eHH173       19829 naive_CD8   C19_cI        Healthy (No …    50 M      White
 8 eAV100        1995 PBMC        C19_cII       COVID-19-Con…    29 F      <NA> 
 9 eQD113        7477 PBMC        C19_cI        COVID-19-Con…    36 M      <NA> 
10 eEE226       19617 naive_CD8   C19_cI        Healthy (No …    21 F      White
# 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 counting. Recall, that we can stick together a dplyr-pipeline with a call to ggplot, so here we will have to count of Cohort and Gender before plotting

Does your plot look different somehow? Consider peeking at the hint…

Click here for hint

Perhaps not everyone agrees on how to denote NAs 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-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 can cut continuous observations into a set of bins?
STOP! Make sure you handled how NAs 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 and Target 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 eXL32      Healthy …    37 F      White "A*01:01"   "A*02:01"    "B*15:01"   
 2 eJL161     COVID-19…    31 F      White "A*01:01:0… "A*02:01:01" "B*08:01:01"
 3 eJL164     COVID-19…    33 M      White "A*02:01:0… "A*24:02:01" "B*15:01:01"
 4 eHO141     COVID-19…    NA <NA>   <NA>  ""          ""           ""          
 5 eHH173     Healthy …    50 M      White "A*02:01"   "A*03:01"    "B*35:01"   
 6 eLH53      COVID-19…    42 M      White "A*01:01:0… "A*11:01:01" "B*55:01:01"
 7 eQD135     COVID-19…    74 M      <NA>  "A*02:01:0… "A*24:02:01" "B*07:02:01"
 8 eLH49      COVID-19…    76 M      <NA>  "A*03:01:0… "A*29:02:01" "B*07:02:01"
 9 eHO131     COVID-19…    58 F      <NA>  "A*02:01:0… "A*02:01:01" "B*15:01:01"
10 eHH174     Healthy …    31 F      White "A*01:01"   "A*02:01"    "B*08:01"   
# 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 becomes A1 oand HLA-A...10 becomes A2 and so on for B1, B2, C1 and C2 (Think: How can we rename variables? And here, just do it “manually” per variable). Remember to assign this new data to the same meta_data-variable

Click here for hint

Which tidyverse 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 eJL162     COVID-19-C…    61 M      <NA>  "A*0… "A*0… "B*4… "B*5… "C*0… "C*0…
 2 eQD135     COVID-19-C…    74 M      <NA>  "A*0… "A*2… "B*0… "B*0… "C*0… "C*0…
 3 ePD86      COVID-19-C…    58 M      White "A*0… "A*2… "B*4… "B*5… "C*0… "C*1…
 4 ePD100     COVID-19-C…    66 M      <NA>  ""    ""    ""    ""    ""    ""   
 5 eHO125     COVID-19-C…    52 M      <NA>  "A*0… "A*0… "B*3… "B*4… "C*0… "C*0…
 6 eMR25      COVID-19-C…    21 F      <NA>  ""    ""    ""    ""    ""    ""   
 7 eMR26      COVID-19-C…    62 M      <NA>  "A*0… "A*0… "B*0… "B*5… "C*0… "C*0…
 8 eQD116     COVID-19-C…    66 F      <NA>  "A*0… "A*1… "B*3… "B*3… "C*0… "C*0…
 9 eEE243     Healthy (N…    32 F      <NA>  "A*0… "A*0… "B*2… "B*4… "C*0… "C*0…
10 eQD136     COVID-19-C…    NA <NA>   <NA>  "A*0… "A*6… "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 CASSLVQGALGDTQYF+TCRBV11-02… CCTGCAAAGCTTGAGGACTCG… eEE243     ORF1ab        
 2 CASSDRDRVGNEQFF+TCRBV10-02+… GAGTCAGCTACCCGCTCCCAG… eEE226     surface glyco…
 3 CASSTGTGHSAEAFF+TCRBV19-01+… ACATCGGCCCAAAAGAACCCG… ePD83      ORF3a         
 4 CASSLSWGGPGYTF+TCRBV27-01+T… CTGGAGTCGCCCAGCCCCAAC… eAV91      surface glyco…
 5 CSGTGGVQETQYF+TCRBV20-X+TCR… ACAGTGACCAGTGCCCATCCT… eXL27      ORF7b         
 6 CAWSVLVSEQYF+TCRBV30-01+TCR… CTGAGTTCTAAGAAGCTCCTT… eLH47      ORF7b         
 7 CSARDSTTEYEQYF+TCRBV20-X+TC… GTGACCAGTGCCCATCCTGAA… eOX52      ORF8          
 8 CASSQGAPVDTQYF+TCRBV27-01+T… CTGGAGTCGCCCAGCCCCAAC… eHO134     ORF1ab        
 9 CASSQVGGAEQFF+TCRBV04-02+TC… CACCTACACACCCTGCAGCCA… eOX52      ORF7a         
10 CASSQDWSGTAYNEQFF+TCRBV04-0… CTGCAGCCAGAAGACTCAGCC… eOX43      ORF8          
# 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:

  1. Write the data back into your data-folder, using the filename peptide-detail-ci.csv.gz, note the appending of .gz, which is automatically recognised and results in gz-compression
  2. Now, check in your data folder, that you have two files peptide-detail-ci.csv and peptide-detail-ci.csv.gz, delete the former
  3. 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 can read 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-button, 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 and Amino Acids. Remember to assign this new data to the same peptide_data-variable to avoid cluttering your environment with redundant variables. Bonus: Did you know you can click the Environment 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 eOX49      CASRERGLNTEAFF+TCRBV02-01+TCRBJ01-01           ITEEVGHTDLMAAY     
 2 eOX52      CAWSVDRPRNEKLFF+TCRBV30-01+TCRBJ01-04          DFLEYHDVR,EDFLEYHD…
 3 eJL158     CASSLTDGVGQPQHF+TCRBV27-01+TCRBJ01-05          FLQSINFVR,FLQSINFV…
 4 eOX43      CAWSGMVSQVYNSPLHF+TCRBV30-01+TCRBJ01-06        DFLEYHDVR,EDFLEYHD…
 5 eJL161     CASSQEFGAGLQLETQYF+TCRBV03-01/03-02+TCRBJ02-05 HTTDPSFLGRY        
 6 eXL32      CASSSRAPLLNSPLHF+TCRBV12-X+TCRBJ01-06          TLIGDCATV          
 7 eOX52      CASSFLAGTNEQFF+TCRBV05-04+TCRBJ02-01           ELYSPIFLI,LYSPIFLI…
 8 eEE224     CASSLRAGGTDTQYF+TCRBV05-01+TCRBJ02-03          KLSYGIATV          
 9 eOX52      CASSGGTGGNF+TCRBV12-03/12-04+TCRBJ02-04        LLDDFVEII,LLLDDFVEI
10 eEE226     CASRWNRLYEQYF+TCRBV02-01+TCRBJ02-07            RQLLFVVEV

  • 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 eHO131     CASSLGPSGGVSSYNEQFF TCRBV13-01 TCRBJ02-01 FVCNLLLLFV,LLFVTVYSHL,T…
 2 eHH173     CASRETYEQYF         TCRBV02-01 TCRBJ02-07 YLDAYNMMI               
 3 eXL30      CASSEGKLYEQYF       TCRBV06-01 TCRBJ02-07 AEAELAKNVSL,AELAKNVSLDN…
 4 eHO130     CASSQVWEGYNEQFF     TCRBV04-02 TCRBJ02-01 FLQSINFVR,FLQSINFVRI,FL…
 5 eXL31      CASSTETSAAGGWRDTQYF TCRBV25-X  TCRBJ02-03 MPASWVMRI               
 6 ePD85      CASSIGQGNTYEQYF     TCRBV19-01 TCRBJ02-07 SEHDYQIGGYTEKW,YQIGGYTE…
 7 eEE228     CASSPGLAGGGTYNEQFF  TCRBV05-01 TCRBJ02-01 LEPLVDLPI               
 8 eEE240     CASSILPPETQYF       TCRBV19-01 TCRBJ02-05 FLNGSCGSV               
 9 eXL27      CASSYFPGETALQLYEQYF TCRBV28-01 TCRBJ02-07 FVDGVPFVV               
10 eAV88      CSARDGDSGTGELFF     TCRBV20-X  TCRBJ02-02 FLQSINFVR,FLQSINFVRI,FL…

Click here for hint

First: Compare the two datasets and identify what happened? Did any variables “dissappear” and did any “appear”? Ok, so this is a bit tricky, but perhaps there is a function to separate a composit (untidy) column into a set of new variables based on a separator? But what is a separator? Just like when you read a file with Comma Separated Values, a separator denotes how a composite string is divided into fields. So look for such a repeated values, 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 the stringr-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-button 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 eOX52      CASSYSAGDPYNEQFF TCRBV06-06       TCRBJ0… FVDGVPFVV              1
 2 eEE224     CASLGGYSYEQYF    TCRBV02-01       TCRBJ0… FVCNLLLLFV,L…          3
 3 eXL27      CSVATGVSGNTIYF   TCRBV29-01       TCRBJ0… AFLLFLVLI,FL…         11
 4 eXL31      CASSYRTGGNQPQHF  TCRBV06-02/06-03 TCRBJ0… LSPRWYFYY,SP…          2
 5 eXL30      CASSLANPHGYTF    TCRBV12-03/12-04 TCRBJ0… AFLLFLVLI,FL…         11
 6 eOX54      CASGAGVRETQYF    TCRBV12-03/12-04 TCRBJ0… IQYIDIGNY              1
 7 eXL30      CSARLTANTGELFF   TCRBV20-X        TCRBJ0… FVDGVPFVV              1
 8 eQD112     CASSLRGYNEQFF    TCRBV13-01       TCRBJ0… AFLLFLVLI,FL…         11
 9 eHO134     CASSVGVGYEQYF    TCRBV09-01       TCRBJ0… GTITSGWTF              1
10 ePD83      CASSMGQGARTEAFF  TCRBV19-01       TCRBJ0… SEHDYQIGGYTE…          3
  • T8: Re-create the following plot

  • Q4: What is the maximum number of peptides assigned to one observation?

  • T9: Using the str_c- and the seq-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. type str_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      CASSVVGNTEA… TCRBV… TCRBJ… FLPRVFSAV <NA>      <NA>      <NA>     
 2 eHO138     CASSHQSSYEQ… TCRBV… TCRBJ… AYKTFPPT… KTFPPTEPK <NA>      <NA>     
 3 eQD125     CASSLGADSQE… TCRBV… TCRBJ… SYFTSDYY… VLHSYFTS… YFTSDYYQ… <NA>     
 4 eAV88      CASSYQGINEQ… TCRBV… TCRBJ… FVFKNIDGY <NA>      <NA>      <NA>     
 5 eXL31      CASSPGAGTRD… TCRBV… TCRBJ… YIFFASFYY <NA>      <NA>      <NA>     
 6 eEE240     CASSAAQPGQN… TCRBV… TCRBJ… AFLLFLVLI FLAFLLFLV FYLCFLAFL FYLCFLAF…
 7 eEE228     CASSHTNEQFF  TCRBV… TCRBJ… FLWLLWPVT FLWLLWPV… LWLLWPVTL LWPVTLACF
 8 eOX46      CASSALAGRNT… TCRBV… TCRBJ… VLWAHGFEL <NA>      <NA>      <NA>     
 9 eOX52      CASSLSYNEQFF TCRBV… TCRBJ… FGEVFNAT… FNATRFAS… GEVFNATRF NATRFASVY
10 eEE224     CASSLYPLDQP… TCRBV… TCRBJ… FLQSINFVR FLQSINFV… FLYLYALV… GLEAPFLY…
# 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-pivotting. 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 few sample_n()-calls with varying degree of n to make sure, that you get a feeling for what is going on

  • T12: From the peptide_data data above, with peptide_1, peptide_2, etc. create this data set using one of the data-pivotting 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 the peptide_data-variable as we don’t want huge redundant data sets floating around in our environment

Click here for hint

If the pivotting is not clear at all, then do what I do, create some example data:

my_data <- tibble(
  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 the pivot_wider()-/pivot_longer()-functions. Use the View()-function to inspect and get a better overview of the results of pivotting.

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 eOX52      CASSLAGPGELFF      TCRBV07-06  TCRBJ…          1 peptide_… <NA>   
 2 eEE240     CASSLALGGDNYGYTF   TCRBV12-X   TCRBJ…          1 peptide_… <NA>   
 3 eEE226     CASSQDPLGGGASYEQYF TCRBV04-01  TCRBJ…          6 peptide_6 VLPFND…
 4 eEE240     CASSIDASMNTEAFF    TCRBV19-01  TCRBJ…          3 peptide_9 <NA>   
 5 eMR14      CASSLNQAEAFF       TCRBV28-01  TCRBJ…          2 peptide_… <NA>   
 6 eQD114     CASSDRAGTDTQYF     TCRBV27-01  TCRBJ…          1 peptide_7 <NA>   
 7 eOX43      CASSLGGTGNTIYF     TCRBV28-01  TCRBJ…         13 peptide_8 QSINFV…
 8 eOX52      CASSLPHATNEKLFF    TCRBV11-03  TCRBJ…          1 peptide_4 <NA>   
 9 eEE226     CASSFGGNEQFF       TCRBV12-03… TCRBJ…          1 peptide_… <NA>   
10 eOX54      CATSKQRAGGNGYTF    TCRBV15-01  TCRBJ…          7 peptide_… <NA>

  • Q7: You will see some NAs in the peptide-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, loose the redundant variables n_peptides and peptide_n and also get rid of the NAs in the peptide-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 eEE240     CASSLGPTYEQYF      TCRBV11-03 TCRBJ02-07 IDFYLCFLAF
 2 eEE226     CASSTDPNRDLNTEAFF  TCRBV19-01 TCRBJ01-01 VLSFCAFAV 
 3 eAV88      CASSRLAGVREQYF     TCRBV05-06 TCRBJ02-07 FVDGVPFVV 
 4 eEE228     CASSEFYPGQGYTGELFF TCRBV25-01 TCRBJ02-02 LIVNSVLLFL
 5 eXL27      CASSLGTPTYNEQFF    TCRBV07-09 TCRBJ02-01 LLFLVLIML 
 6 eEE228     CASSLLSGNTEAFF     TCRBV27-01 TCRBJ01-01 WLLWPVTLA 
 7 eLH42      CASSLGASMNTEAFF    TCRBV13-01 TCRBJ01-01 AFLLFLVLI 
 8 eOX49      CSAPTGTTYEQYF      TCRBV20-X  TCRBJ02-07 SLIDFYLCFL
 9 eLH41      CASSLAGLAADTQYF    TCRBV27-01 TCRBJ02-03 LQSINFVRI 
10 eXL37      CASSLETVDPYEQYF    TCRBV07-09 TCRBJ02-07 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 to filter the CDR3b and peptide variables using a pattern of [^ARNDCQEGHILKMFPSTWYV] and then play with the negate-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 and k_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 eLH47      CASSLSPGESLSSYNSPLHF TCRBV07-08   TCRBJ… FLYLYA…      20        10
 2 eXL27      CASSPGQGAYSEQFF      TCRBV02-01   TCRBJ… VQELYS…      15        10
 3 eOX49      CSATLAGGQETQYF       TCRBV20-X    TCRBJ… FYLCFL…      14         9
 4 eOX43      CASSFHGEQYF          TCRBV12-03/… TCRBJ… NVFAFP…      11        10
 5 eXL27      CASSQQGGTGELFF       TCRBV14-01   TCRBJ… FVCNLL…      14        10
 6 eOX43      CATSRVPSGRASYNEQFF   TCRBV15-01   TCRBJ… KEIIFL…      18        11
 7 eOX54      CASSPVRQNSYEQYF      TCRBV07-09   TCRBJ… LLLDDF…      15         9
 8 eOX52      CASSQYPGAGLDEQYF     TCRBV14-01   TCRBJ… FVDGVP…      16         9
 9 eEE226     CASSSQPIQLYEQYF      TCRBV27-01   TCRBJ… IDFYLC…      15        10
10 eOX46      CASSYSLAGGTYEQYF     TCRBV06-05   TCRBJ… FLAFLL…      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 eMR14      CASSQDAEGRGLAKNIQYF TCRBV04-03 TCRBJ02-… QSINFV…      19         9
 2 eLH48      CASSIIQGSYNSPLHF    TCRBV07-02 TCRBJ01-… TTLPKG…      16         9
 3 eOX49      CASSVTGGTNEKLFF     TCRBV07-03 TCRBJ01-… MIELSL…      15        10
 4 eOX49      CSVEDLLQGNYGYTF     TCRBV29-01 TCRBJ01-… FLAFLL…      15         9
 5 eOX46      CASGDLAGGPNNEQFF    TCRBV12-05 TCRBJ02-… TLACFV…      16        10
 6 eEE240     CASTHDWEDTQYF       TCRBV06-X  TCRBJ02-… NVFAFP…      13         9
 7 eOX52      CASRTTSGGYEQYF      TCRBV27-01 TCRBJ02-… LYSPIF…      14         9
 8 eOX46      CASSYRSSRGRPEAFF    TCRBV28-01 TCRBJ01-… SLIDFY…      16        10
 9 eAV88      CASSQTGDRLYEQYF     TCRBV04-01 TCRBJ02-… VLNDIL…      15         9
10 eOX46      CASSFPTAGTNNGEQFF   TCRBV07-08 TCRBJ02-… FLAFLL…      17         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 eQD116     COVID-19-C…    66 F      <NA>  "A*0… "A*1… "B*3… "B*3… "C*0… "C*0…
 2 eQD138     COVID-19-C…    NA <NA>   <NA>  "A*0… "A*0… "B*3… "B*4… "C*0… "C*1…
 3 eNL192     COVID-19-C…    NA <NA>   <NA>  ""    ""    ""    ""    ""    ""   
 4 eQD113     COVID-19-C…    36 M      <NA>  "A*0… "A*1… "B*5… "B*5… "C*0… "C*1…
 5 eQD117     COVID-19-C…    70 F      <NA>  "A*0… "A*2… "B*3… "B*4… "C*0… "C*0…
 6 eHO140     COVID-19-C…    NA <NA>   <NA>  ""    ""    ""    ""    ""    ""   
 7 ePD83      Healthy (N…    29 F      Asian "A*0… "A*0… "B*1… "B*4… "C*0… "C*0…
 8 eQD124     COVID-19-B…    40 F      White "A*0… "A*0… "B*1… "B*5… "C*0… "C*0…
 9 eDH105     COVID-19-C…    32 F      <NA>  "A*2… "A*2… "B*4… "B*4… "C*0… "C*0…
10 eLH47      COVID-19-C…    35 F      White "A*0… "A*0… "B*0… "B*0… "C*0… "C*0…

Remember you can scroll in the data.

  • Q12: Discuss in your group, if this data with respect to the A1-, A2-, B1-, B2-, C1- and C2-variables is a wide- or a long-data format?

As with the peptide_data, we will now have to use data-pivotting again. I.e.:

  • T18: use either the pivot_wider- or pivot_longer-function 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 ePD100     COVID-19-Convalescent          66 M      <NA>  B1    ""          
 2 eEE224     Healthy (No known exposure)    24 M      White B2    "B*40:01"   
 3 eQD131     COVID-19-Exposed               NA <NA>   <NA>  A2    "A*32:01:01"
 4 eLH42      COVID-19-Convalescent          63 M      <NA>  B1    "B*07:02:01"
 5 eOX49      Healthy (No known exposure)    21 M      White A2    "A*26:01"   
 6 eNL192     COVID-19-Convalescent          NA <NA>   <NA>  C2    ""          
 7 eQD112     COVID-19-Convalescent          65 M      <NA>  C2    "C*07:02:01"
 8 eMR20      COVID-19-B-Non-Acute           37 M      White A2    "A*26:01:01"
 9 eQD108     COVID-19-Convalescent          NA <NA>   <NA>  A2    "A*68:01:02"
10 eJL146     Healthy (No known exposure)    30 M      White C2    "C*08:02"

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 the meta_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 eMR14      "C*07:02:01"
 2 eAV105     "C*07:02:01"
 3 eDH113     "C*07:01"   
 4 eLH48      "A*03:01:01"
 5 eEE226     "C*04:01"   
 6 ePD91      ""          
 7 eAV91      "B*51:01"   
 8 ePD90      ""          
 9 eQD139     "B*56:01:01"
10 eHO126     "C*07:02:01"

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 NAs 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 the stringr-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 eJL158     B*40:02:01 B*40:02     
 2 eAM23      A*11:01:01 A*11:01     
 3 eJL160     C*05:01:01 C*05:01     
 4 eJL154     A*02:01:01 A*02:01     
 5 eMR15      A*03:01:01 A*03:01     
 6 eHO133     C*12:03:01 C*12:03     
 7 eQD139     C*01:02:01 C*01:02     
 8 eLH59      C*03:04:01 C*03:04     
 9 eQD138     A*02:01:01 A*02:01     
10 eMR14      C*07:02:01 C*07:02

The asterix, i.e. * is a rather annoying character because of ambiguity, so:

  • T20: Clean the data a bit more, by removing the asterix and redundant variables:
meta_data |> 
  sample_n(size = 10)
# A tibble: 10 × 2
   Experiment Allele
   <chr>      <chr> 
 1 eDH113     C16:01
 2 eLH51      A24:07
 3 eJL160     A01:01
 4 eJL153     B07:02
 5 eQD121     C07:01
 6 eJL158     A02:01
 7 eEE243     C04:01
 8 eQD118     B51:01
 9 eJL154     A02:01
10 eOX43      C07:04

Click here for hint 1

Again, the stringr-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 eXL30      CASSLALLDNEQFF    TCRBV07-06      TCRBJ… YLCFLA…      14         9
 2 eEE226     CASSYTDRSGYTF     TCRBV06-02/06-… TCRBJ… LEYHDV…      13        10
 3 eXL31      CASSIAGNTYEQYF    TCRBV19-01      TCRBJ… WICLLQ…      14         9
 4 eAV88      CSVVYRDGYTF       TCRBV29-01      TCRBJ… FLWLLW…      11        10
 5 eEE228     CATSDLPGGFNTGELFF TCRBV24-01      TCRBJ… LNDLCF…      17        10
 6 eMR20      CASSQGGSLQPQHF    TCRBV16-01      TCRBJ… VLHSYF…      14        10
 7 eEE228     CASSQAVNTEAFF     TCRBV04-03      TCRBJ… IELSLI…      13        10
 8 eOX43      CASSQEGGLAGVHEQYF TCRBV03-01/03-… TCRBJ… LIDFYL…      17         9
 9 eXL27      CASSEGGALGHF      TCRBV02-01      TCRBJ… INVFAF…      12        10
10 eOX46      CASSAGTSSYEQYF    TCRBV07-03      TCRBJ… LLFLVL…      14         9
  • T21: Create a dplyr-pipeline, starting with the peptide_data, which joins it with the meta_data and remember to make sure that you get only unqiue observations of rows. Save this data into a new variable names peptide_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 the peptide_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 eMR14      CASSPGQGSSPLHF     TCRBV0… TCRBJ… FLYLYA…      14        10 A03:01
 2 eEE228     CSARPLGPTSGRIYEQYF TCRBV2… TCRBJ… GYINVF…      18        10 B35:03
 3 eOX52      CASLAGNSYEQYF      TCRBV0… TCRBJ… FVCNLL…      13        10 B15:17
 4 eOX46      CASSQAGTGTGVYEQYF  TCRBV0… TCRBJ… LLTDEM…      17        10 A02:01
 5 eOX43      CATRLGPYYEQYF      TCRBV2… TCRBJ… IDFYLC…      13        10 C03:04
 6 eEE240     CASSPFLLDTQYF      TCRBV0… TCRBJ… AFLLFL…      13         9 C03:04
 7 eJL161     CASLAGTSAWETQYF    TCRBV1… TCRBJ… YLYALV…      15         9 A02:01
 8 eQD112     CASSLVGSNYGYTF     TCRBV0… TCRBJ… NGVEGF…      14         9 C04:01
 9 eEE226     CASSLGREQFF        TCRBV0… TCRBJ… APKEII…      11         8 C07:02
10 eOX52      CASSLLSPGGYNEQFF   TCRBV2… TCRBJ… TVLSFC…      16         9 B40:01

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:

  1. What characterises the peptides binding to the HLAs?
  2. 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 to A02:01 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 into ggseqlogo, 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 favorite 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, and peptide 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 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 toolbelt, will allow you to be so much more effecient in your data wrangling, so you can get to the fun part as quick 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 destill the exercises from today into one small end-to-end runable reproducible micro-report. DO NOT include ALL of the exercises, but rather include as few steps as possible to arrive at your results. Be vey consise!

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!