name: inverse layout: true class: center, middle, inverse <div class="my-header"><span> <a href="/training-material/topics/transcriptomics" title="Return to topic page" ><i class="fa fa-level-up" aria-hidden="true"></i></a> <a class="nav-link" href="https://github.com/galaxyproject/training-material/edit/main/topics/transcriptomics/tutorials/scrna-plates-batches-barcodes/slides.html"><i class="fa fa-pencil" aria-hidden="true"></i></a> </span></div> <div class="my-footer"><span> <img src="/training-material/assets/images/GTN-60px.png" alt="Galaxy Training Network" style="height: 40px;"/> </span></div> --- <img src="/training-material/assets/images/GTN.png" alt="Galaxy Training Network" class="cover-logo"/> # Plates, Batches, and Barcodes <div markdown="0"> <div class="contributors-line"> Authors: <a href="/training-material/hall-of-fame/nomadscientist/" class="contributor-badge contributor-nomadscientist"><img src="https://avatars.githubusercontent.com/nomadscientist?s=27" alt="Avatar">Wendi Bacon</a> <a href="/training-material/hall-of-fame/mtekman/" class="contributor-badge contributor-mtekman"><img src="https://avatars.githubusercontent.com/mtekman?s=27" alt="Avatar">Mehmet Tekman</a> <a href="/training-material/hall-of-fame/astrovsky01/" class="contributor-badge contributor-astrovsky01"><img src="https://avatars.githubusercontent.com/astrovsky01?s=27" alt="Avatar">Alex Ostrovsky</a> </div> </div> <!-- modified date --> <div class="footnote" style="bottom: 8em;"><i class="far fa-calendar" aria-hidden="true"></i><span class="visually-hidden">last_modification</span> Updated: Apr 12, 2021</div> <!-- other slide formats (video and plain-text) --> <div class="footnote" style="bottom: 6em;"> <i class="far fa-play-circle" aria-hidden="true"></i><span class="visually-hidden">video-slides</span> <a href="/training-material/videos/watch.html?v=/transcriptomics/tutorials/scrna-plates-batches-barcodes/slides">Video slides</a> | <i class="fas fa-file-alt" aria-hidden="true"></i><span class="visually-hidden">text-document</span><a href="slides-plain.html"> Plain-text slides</a> </div> <!-- usage tips --> <div class="footnote" style="bottom: 2em;"> <strong>Tip: </strong>press <kbd>P</kbd> to view the presenter notes | <i class="fa fa-arrows" aria-hidden="true"></i><span class="visually-hidden">arrow-keys</span> Use arrow keys to move between slides </div> ??? 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Useful when presenting. --- ## Requirements Before diving into this slide deck, we recommend you to have a look at: - [Introduction to Galaxy Analyses](/training-material/topics/introduction) - [Sequence analysis](/training-material/topics/sequence-analysis) - Quality Control: [<i class="fab fa-slideshare" aria-hidden="true"></i><span class="visually-hidden">slides</span> slides](/training-material/topics/sequence-analysis/tutorials/quality-control/slides.html) - [<i class="fas fa-laptop" aria-hidden="true"></i><span class="visually-hidden">tutorial</span> hands-on](/training-material/topics/sequence-analysis/tutorials/quality-control/tutorial.html) - Mapping: [<i class="fab fa-slideshare" aria-hidden="true"></i><span class="visually-hidden">slides</span> slides](/training-material/topics/sequence-analysis/tutorials/mapping/slides.html) - [<i class="fas fa-laptop" aria-hidden="true"></i><span class="visually-hidden">tutorial</span> hands-on](/training-material/topics/sequence-analysis/tutorials/mapping/tutorial.html) - [Transcriptomics](/training-material/topics/transcriptomics) - Understanding Barcodes: - [<i class="fas fa-laptop" aria-hidden="true"></i><span class="visually-hidden">tutorial</span> hands-on](/training-material/topics/transcriptomics/tutorials/scrna-umis/tutorial.html) --- ### <i class="far fa-question-circle" aria-hidden="true"></i><span class="visually-hidden">question</span> Questions - What is a batch when it comes to single cells? - What’s the difference between a barcode and an index? - What is batch effect and how do you prevent it? - What is a lane? --- ### <i class="fas fa-bullseye" aria-hidden="true"></i><span class="visually-hidden">objectives</span> Objectives - How to set up plates to prevent batch effect - Proper naming conventions when dealing with scRNA-seq samples --- ### Sorting Plates .image-100[] .left[Plates are *N x M* arrays of wells that cells are sorted, to then be individually amplified and sequenced.] ??? - Sorting plates are 2 dimensional arrays of wells that individual cells are placed into. - Each cell in each well is tagged with a unique barcode and then primed to then be amplified and sequenced - The method in which this is performed can yield significant pros and cons as we will see. --- ### Sorting Plates .image-80[] ??? - Here we see 3 different sorting strategies with different advantages for same treatment sample. - In the top image: We see all wells are filled, to maximise the number of cells on a plate - In the middle image: We see only the inner wells are filled, to maintain a protective border during library preparation - In the bottom image: We see a striping pattern, which mitigates contamination of material from adjoining wells --- ### Setting up Plates .image-75[] .center[What is the problem with this plate setup?] ??? - However, if we consider different treatment samples, then we must consider different challenges. - What is the advantage of the top example, i.e. Several different treatments on one plate? - What is the advantage of the bottom example, i.e. One treatment on one plate? --- ### Setting up Plates .image-50[] .center[Batch effect (plate vs plate) cannot be separated from treatment effect in either scenario.] ??? - Trick question! - In both examples, the treatment effect cannot be separated from any technical effect from the plate itself. - For example, the biological effect of the yellow cells might just be the technical confounding effect from Batch 1. - What can we do instead to reduce the effect of these technical confounding effects across treatments? --- ### Setting up Plates .image-50[] .left[Either of these are better set-ups. Mixing columns is good, but not required. Ultimately, batch effect can now be separated from variable effect.] ??? - The answer is that we can balance the treatments across our plates better. - By ensuring the same treatment occurs in comparable amounts in different plates, we can reduce the effect that each plate would have on the treatment. --- ### Setting up Plates .image-90[] .center[Can't mix samples on plates? Separate replicates evenly and process together.] ??? - If putting multiple treatments on the same plate is not an option, then having enough separated replicates will also allow for batch correction. - Here, you can assess the variation between replicates, and thus batches, as well as between treatments. --- ### What about sequencing lanes? .center[So now it's time to sequence our samples! How do we combine samples into sequencing lanes?] .image-90[] .center[This works well, but what if you have too many samples for one lane?] ??? - So now it's time to sequence our samples! How do we combine samples into sequencing lanes? - In many cases, it is enough to sequence 1 or 2 plates together in a single sequencing lane. - But what if we wish to sequence 8 plates? --- ### What about sequencing lanes? .center[So now it's time to sequence our samples! How do we combine samples into sequencing lanes?] .image-90[] .center[Does this look ok?] ??? - In the example show, Treatment A would go to the first sequencing lane. - Treatment B would go to the second sequencing lane. - Is it okay to sequence different treatments across different sequencing lanes? -- .center[No! You've turned each treatment (A & B) into a batch!] ??? - No, because you've turned Treatment A and Treatment B into separate batches. - This gives us the same unbalanced design we saw with the sorting plates. --- ### What about sequencing lanes? .image-75[] .center[This is the way to balance your batches at the lane-level.] ??? - Instead we should balance the availability of each treatment across different sequencing lanes - This way, each treatment is sequenced at the same time as another treatment, reducing the technical effect from the sequencing on the treatment. --- ### Distinguishing cells in a plate .image-60[] * Cells are selected from a plate by their *barcodes* * Barcodes must be unique + e.g. 96 wells in a plate, need 96 barcodes to sequence them together ??? - How do we distinguish each cell in each well? - Do we remember the physical location of that cell on that well, or do we tag it somehow? - The answer is that each cell is given a unique barcode that distinguishes from other cells which have different cell barcodes. --- ### What are Cell Barcodes? .image-100[] * Barcodes are added to *all* transcripts of a *specific* cell * Transcripts with different cell barcodes originate from different cells ??? - What exactly are cell barcodes? - These are usually short nucleotide barcodes that are added to all transcripts of a specific cell - The idea is that transcripts with different cell barcodes originate from different cells. --- ### What are Cell Barcodes? .image-100[] * Many different cell barcodes are used across many different cells * Each well in a plate contains a cell, indexed by its cell barcode ??? - Within a plate, many different barcodes are used, and each cell is indexed by the cell barcode assigned to that well. --- ### Questions about Cell Barcodes .image-50[] .left[Assuming you sequence one 96-well plate:] 1. How many cell barcodes are needed for a single lane? 1. How many cell barcodes are needed if you combine 10 plates into a single sequencing lane? 1. What would be the minimum length of the barcodes for each of the previous questions? ??? - Consider a 12 by 8 plate with 96 wells. - How many cell barcodes are needed if you sequence a single plate? - How many cell barcodes are needed if you sequence 10 plates into a single sequencing lane? - What would be the minimum length of the barcodes for each of the previous questions? -- .footnote[ 1. *96 unique barcodes per lane* 1. *96 x 10 = 960 unique barcodes per plate* ] ??? - For one plate you would just need 96 unique barcodes - For 10 plates you would need 960 unique barcodes --- ### Questions about Cell Barcodes .left[<small>3) What would be the minimum length of the barcodes for each of the previous questions?</small>] .pull-left[ A single lane? * 96 barcodes, 4 nucleotides for each base of a barcode | Barcodes | Result | |---------|------| | $$4^2 = 16$$ | <small>No, 2 bases is not enough | | $$4^3 = 64$$ | <small>No, 3 bases is not enough | | $$4^4 = 256$$ | <small>Yes, 4 bases is enough to cover 96 barcodes (and more!) | ] ??? - To answer the third question, we need to make some assumptions about the barcodes. - If we assume a barcode is made up of a chain of 4 possible nucleotides, then we can solve the problem as a power of 4. - For a single lane with only 96 barcodes, we require our barcodes to be of at least 4 base pairs in length. -- .pull-right[ 10 plates in a single lane? * 960 barcodes, 4 nucleotides for each base of a barcode | Barcodes | Result | |---------|------| |$$4^4 = 256$$ | <small>No, 4 bases is not enough | |$$4^5 = 1024$$ | <small>Yes, 5 bases is enough to cover 960 barcodes (just barely!) | ] ??? - For 10 lanes with 960 barcodes, we require our barcodes to be of at least 5 base pairs in length. - However these calculations also make one serious assumption about the barcodes. --- ### Barcode Safeguarding .pull-left[ * Is 5 nucleotides really enough to capture 960 cells? * What could go wrong if all barcodes are separated by 1 bp, as shown? ] .pull-right[ <small>*Edit distance = 1bp* AAAAA AAAAC AAAAG AAAAT AAACA AAAGA AAATA ···· CCCCC CCCCA CCCCG CCCCT CCCAC CCCGC CCCTC ···· · · · ] ??? - For example, we assume that all our barcodes are at minimum separated by a single base pair. - This is known as an edit distance of 1. - If we assume that every barcode is separated from every other barcode by 1 base pair, then 5 nucleotides is indeed enough to delineate 960 cells. - However if there was even just one small sequencing error of 1 base pair on any barcode, it would immediately change that erronous barcode into another real barcode. - That is to say, we have no way of detecting sequencing errors if we use an edit distance of 1. --- ### Guarding against Sequencing Errors .pull-left[ * Sequencing errors can change the cell barcode * To guard against a 1 bp sequencing error, need to space barcodes by 2 bp * How many barcodes for a cell barcode of length 5 bp (ED = 2)? ] .pull-right[ <small>*Edit Distance = 2 bp* AAAAA AAACC AAAGG AAATT AACCA AAGGA AATTA ···· CCCCC CCCAA CCCGG CCCTT CCCAA CCGGC CCTTC ···· · · · ] ??? - Let us try to rectify this by increasing the edit distance to 2. - In the example barcodes shown we see barcodes of length 5, separated by 2 base pairs. - How many unique cell barcodes can we make given these restrictions? -- $$ 4^{5-1} = 512 $$ ??? - The answer is half the amount that we need. --- ### Edit Distance : General Principle .pull-left[ e.g. For barcodes of length N=3: * Edit Distance of E=1: .center[`AAA AAC AAG AAT ACA ACC` ...] .center[<small>64 barcodes</small>] * Edit Distance of E=2: .center[`AAA ACC AGG ATT CAA CCC` ...] .center[<small>16 barcodes</small>] * Edit Distance of E=3: .center[`AAA CCC GGG TTT`] .center[<small>4 barcodes</small>] ] ??? - Let us explore this more explicitly. - Here we will be using barcodes only of length 3, and we will explore different edit distances of 1, 2, and 3. - These yield 64, 16, and 4 barcodes respectively. -- .pull-right[ Number of Barcodes : $$ 4^{N-(E-1)} $$ .center[For barcodes of length *N*, and Edit Distance of *E*.] ] ??? - This can be summarized by the formula given, where the length of the barcodes, minus one minus the edit distance, gives the number of available barcodes. --- ### How many available barcodes are there? .pull-left[ * Barcodes typically limited to 4 main bases {A,C,T,G}. * Number of available barcodes depends on: 1. The length of the barcode 2. The edit distance between adjacent barcodes * Must take sequencing errors into account ] ??? - To summarize, barcodes are typically limited to 4 nucleotides, and the number of available barcodes depends on the length and edit distance. - This is to ensure that sequencing errors are taken into account. -- .pull-right[ * Availability is balance between *barcode size* vs. *sequencing errors* * Typically between 4-10 bases, range of cells that can be labelled: $$[4^4, 4^{10}] = [256, 1048576]$$ * True range is lower due to edit distance. ] ??? - The availability of barcodes and the number of cells they can label is a design choice, where the technician must balance two opposing forces: barcode size vs sequencing errors. - For the size, this means that for a barcode N bases long, there will 4 to the N barcodes available. - Typically barcodes tend to span 4 to 10 bases, since longer barcodes tend to be more subjectable to sequencing errors. - The true number of barcodes used is smaller than 4 to the N, due to the countermeasures used to space barcodes apart from one another, in order to reduce sequencing errors. --- ### Design Factors in Barcodes Is it better to use Longer or Shorter Barcodes? | Longer | Shorter | |--------|---------| | Offer larger range of barcodes | Offer smaller range of barcodes | | Prone to more sequencing errors | More robust against sequencing errors | | Must increase edit distance significantly | A smaller edit distance is more acceptable | | Can accommodate large edit distances | *Cannot* accommodate large edit distances | ??? - To weigh the pros and cons of longer and shorter barcodes, we need to take into account the likelihood of sequencing errors for a given size, and the resulting barcode address space given by the edit distance. --- ### Cell Barcodes: Summary .pull-left[ * A single barcode sequence indexes a single cell * Every transcript in a specific cell has the same cell barcode * Barcodes are *designed* for smaller plate-based protocols, while for split-pool and similar techniques they are randomised. * Barcode use is *limited* by length and read depth ] ??? - To summarize cell barcodes, we should note the following - A single barcode sequence indexes a single cell - Every transcript in a specific cell has the same cell barcode - Barcodes are designed for smaller plate-based protocols, while for split-pool and similar techniques they are randomised - Barcode use is limited by length and read depth -- <hline> .pull-right[#### Need to balance: <small> | | | | |-|-|-| | Edit Distance | vs. | Barcode Length | | Number of Barcodes | vs. | Plate and Lane Size | </small> ] ??? - The number of reads wanted per cell determines how many cells you run in a sequencing lane, which in turn tells you how many barcodes you need. --- # Summary .pull-left[ * Cell Barcodes are sequences attached to transcripts to indicate what cell a transcript came from * Cell Barcodes are *designed* to the Plate/Lane setup ] ??? - From the content shown here, you have learned the following. - Cell Barcodes are sequences attached to transcripts to indicate what cell a transcript came from - Cell Barcodes are designed for the Plate and Lane setup. -- .pull-right[ * Indicates what cell a transcript came from. * Reduce sequencing errors ] ??? - Cell barcodes indicate what cell a transcript came from - They reduce sequencing errors when spaced appropriately --- ### <i class="fas fa-key" aria-hidden="true"></i><span class="visually-hidden">keypoints</span> Key points - Balanced batches and replicates allow bioinformatic batch correction - A sequencing lane often contains multiple batches, and is itself a batch effect! --- ### <i class="fas fa-graduation-cap" aria-hidden="true"></i><span class="visually-hidden">curriculum</span> Do you want to extend your knowledge? Follow one of our recommended follow-up trainings: - [Transcriptomics](/training-material/topics/transcriptomics) - Single-cell quality control with scater: [<i class="fas fa-laptop" aria-hidden="true"></i><span class="visually-hidden">tutorial</span> hands-on](/training-material/topics/transcriptomics/tutorials/scrna-scater-qc/tutorial.html) --- ## Thank You! This material is the result of a collaborative work. Thanks to the [Galaxy Training Network](https://training.galaxyproject.org) and all the contributors! <div markdown="0"> <div class="contributors-line"> Authors: <a href="/training-material/hall-of-fame/nomadscientist/" class="contributor-badge contributor-nomadscientist"><img src="https://avatars.githubusercontent.com/nomadscientist?s=27" alt="Avatar">Wendi Bacon</a> <a href="/training-material/hall-of-fame/mtekman/" class="contributor-badge contributor-mtekman"><img src="https://avatars.githubusercontent.com/mtekman?s=27" alt="Avatar">Mehmet Tekman</a> <a href="/training-material/hall-of-fame/astrovsky01/" class="contributor-badge contributor-astrovsky01"><img src="https://avatars.githubusercontent.com/astrovsky01?s=27" alt="Avatar">Alex Ostrovsky</a> </div> </div> <div style="display: flex;flex-direction: row;align-items: center;justify-content: center;"> <img src="/training-material/assets/images/GTN.png" alt="Galaxy Training Network" style="height: 100px;"/> </div> <a rel="license" href="https://creativecommons.org/licenses/by/4.0/"> This material is licensed under the Creative Commons Attribution 4.0 International License</a>.