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| --- | ||
| title: "Introduction to RNAseq analysis in R" | ||
| date: "March 2023" | ||
| date: "October 2024" | ||
| output: | ||
| ioslides_presentation: | ||
| css: css/stylesheet.css | ||
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| <div style="line-height: 50%;"><br></div> | ||
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| <img src="images/workflow_3Day.svg" class="centerimg" style="width: 80%; margin-top: 60px;"> | ||
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| <img src="images/01s_workflow_3Day.svg" class="centerimg" style="width: 80%; margin-top: 60px;"> | ||
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| ## General idea behind RNAseq data analysis | ||
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| <img src="images/RNAseq_data_ana_gen_idea.png" class="centerimg" style="width: 90%"> | ||
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| ## General idea behind any statistical test | ||
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| <img src="images/general_idea_statistical_test.png" class="centerimg" style="width: 90%"> | ||
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| ## Sources of Noise (Variance) | ||
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| <img src="images/Noise.svg" style="width: 65%; margin-left: 23%"> | ||
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| ## Normalisation | ||
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| * Counting estimates the *relative* counts for each gene | ||
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| * Does this **accurately** represent the original population of RNAs? | ||
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| * The relationship between counts and RNA expression is not the same for all | ||
| genes across all samples | ||
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| <div style="width: 30%; | ||
| float: left; | ||
| border-style: solid; | ||
| border-width: 1px; | ||
| border-radius: 25px; | ||
| padding: 20px; | ||
| margin-right: 10%; | ||
| margin-left: 10%;"> | ||
| <span style="color: #2e3192">**Library Size**</span> | ||
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| Differing sequencing depth | ||
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| </div> | ||
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| <div style="width: 30%; | ||
| float: left; | ||
| border-style: solid; | ||
| border-width: 1px; | ||
| border-radius: 25px; | ||
| padding: 20px;"> | ||
| <span style="color: #2e3192">**Gene properties**</span> | ||
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| Length, GC content, sequence | ||
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| </div> | ||
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| <div style="width: 40%; | ||
| float: left; | ||
| border-style: solid; | ||
| border-width: 1px; | ||
| border-radius: 25px; | ||
| padding: 20px; | ||
| clear: both; | ||
| margin-top: 20px; | ||
| margin-left: 27%"> | ||
| <span style="color: #2e3192;">**Library composition**</span> | ||
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| Quantification is relative - changes in | ||
| relative abundance for one gene will affect the relative abundances of other genes | ||
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| "Composition Bias" | ||
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| </div> | ||
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| ## General principle behind normalisation | ||
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| * Normalization has two steps | ||
| * Scaling | ||
| * First get size factors or normalization factors | ||
| * Usually one size factor per sample | ||
| * Scale the counts by divide the raw counts of a sample with sample specific size factor | ||
| * Transformation: Transform the data after scaling | ||
| * Per million | ||
| * log2 | ||
| * square root transformation | ||
| * Pearson residuals (eg. sctransform) | ||
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| * Normalization removes technical variance but not biological variance | ||
| * Normalization helps in making two samples comparable | ||
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| ## Normalization toy example | ||
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| <img src="images/normalisation_toy_example.png" class="centerimg" style="width: 90%"> | ||
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| ## DESeq2 analysis workflow | ||
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| <div style="line-height: 50%;"><br></div> | ||
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| <img src="images/DESeq2_workflow_00.png" class="centerimg" style="width: 25%"> | ||
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| ## DESeq2 Normalisation | ||
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| <div class="smalltext" style="margin-left: 25px"> | ||
| 1. Geometric mean is calculated for each gene across all samples. | ||
| 2. The counts for a gene in each sample is then divided by this mean. | ||
| 3. The median of these ratios in a sample is the size factor (normalization factor) for that sample. | ||
| 4. DESEq2 normalization corrects for library size and RNA composition bias | ||
| 5. Composition bias: Arise for example when only a small number of genes are very highly expressed in one sample but not in the other. | ||
| </div> | ||
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| <img src="images/DESeq2_workflow_01.png" style="width: 15%; float: left"> | ||
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| <img src="images/GeometricScaling.svg" style="margin-left: 15%; width: 60%"> | ||
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| ## Differential Expression | ||
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| Simple difference in means | ||
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| <img src="images/DifferenceInMeans.png" class="centerimg" style="width: 60%;"> | ||
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| <div style="text-align: right"> | ||
| Replication introduces variation | ||
| </div> | ||
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| ## Differential Expression - Modelling population distributions | ||
| <img src="images/06s_RNAseq_data_ana_gen_idea.png" class="centerimg" style="width: 90%"> | ||
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| * Normal (Gaussian) Distribution - t-test | ||
| ## Day 2 in detail | ||
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| * Two parameters - $mean$ and $sd$ ($sd^2 = variance$) | ||
| * Experimental Design | ||
| * General Statistical Principles | ||
| * Statistics specific to RNASeq Differential Expression | ||
| * The 'under the hood' of the DESEQ2 workflow | ||
| * How to run basic DESEQ2 in R | ||
| * Different linear models and how to choose the best for your experiment | ||
| * How to use different models in R | ||
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| * Suitable for microarray data but not for RNAseq data | ||
| ## The Results Table | ||
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| <div style="width: 60%; margin-left: 16%; padding-top: 5px"> | ||
| <img src="images/06s_ResultsTab.png" class="centerimg" style="width: 90%"> | ||
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| ```{r diffInMeans, echo=FALSE, fig.width=7, fig.height=4} | ||
| library(shape) | ||
| x1 <- seq(0, 6, length=100) | ||
| hx1 <- dnorm(x1, mean = 3, sd = 1) | ||
| x2 <- seq(2, 12, length=100) | ||
| hx2 <- dnorm(x2, mean = 7, sd = 1.5) | ||
| par(bg=NA, mar=c(5, 4, 0, 4) + 0.1) | ||
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| plot(x1, hx1, type="l", lty=1, | ||
| xlab="x value", ylab="Density", | ||
| col="tomato", ylim=c(0, 0.6), xlim=c(0, 13)) | ||
| lines(x2, hx2, type="l", col="steelblue") | ||
| abline(v=3, col="tomato3", lty=2) | ||
| abline(v=7, col="steelblue3", lty=2) | ||
| Arrows(3.3, 0.5, 6.7, 0.5, code = 3, arr.type = "curved") | ||
| ``` | ||
| </div> | ||
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| ## Differential Expression - Modelling population distributions | ||
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| * Count data - Poisson distribution | ||
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| * One parameter - $mean$ $(\lambda)$ | ||
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| * $variance$ = $mean$ | ||
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| <div style="width: 60%; margin-left: 16%; padding-top: 5px"> | ||
| ```{r poissonDistr, echo=FALSE, fig.width=7, fig.height=4} | ||
| x1 <- seq(0, 20) | ||
| hx1 <- dpois(x1, lambda = 1) | ||
| hx2 <- dpois(x1, lambda = 4) | ||
| hx3 <- dpois(x1, lambda = 10) | ||
| par(bg=NA, mar=c(5, 4, 0, 4) + 0.1) | ||
| plot(x1, hx1, type="l", lty=1, | ||
| xlab="k", ylab="P(X=k)") | ||
| lines(x1, hx2, type="l") | ||
| lines(x1, hx3, type="l") | ||
| cols <- c("coral2", "darkgoldenrod1", "deepskyblue3") | ||
| points(x1, hx1, bg=cols[1], pch=21) | ||
| points(x1, hx2, bg=cols[2], pch=21) | ||
| points(x1, hx3, bg=cols[3], pch=21) | ||
| leg <- c(expression(paste(lambda, " = ", 1)), | ||
| expression(paste(lambda, " = ", 4)), | ||
| expression(paste(lambda, " = ", 10))) | ||
| legend("topright", legend = leg, pt.bg = cols, pch=21, bty="n") | ||
| ``` | ||
| </div> | ||
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| ## Differential Expression - Modelling population distributions | ||
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| <img src="images/DESeq2_workflow_02.png" style="width: 16%; float: left; | ||
| margin-top: 40px"> | ||
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| <div style="width: 45%; float: left; | ||
| margin-right: 10px; | ||
| margin-left: 30px; | ||
| margin-top: 40px"> | ||
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| * Use the Negative Binomial distribution | ||
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| * In the NB distribution $mean$ not equal to $variance$ | ||
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| * Two paramenters - $mean$ and $dispersion$ | ||
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| * $dispersion$ describes how $variance$ changes with $mean$ | ||
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| </div> | ||
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| <img src="images/NegativeBinomialDistribution.png" style="width: 33%; | ||
| margin-top: 40px"> | ||
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| <div style="text-align: right"> | ||
| Anders, S. & Huber, W. (2010) Genome Biology | ||
| </div> | ||
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| ## Differential Expression - estimating dispersion | ||
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| <img src="images/DESeq2_workflow_03.png" style="width: 16%; float: left; | ||
| margin-top: 40px"> | ||
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| <div style="width: 40%; float: left; | ||
| margin-right: 10px; | ||
| margin-left: 30px; | ||
| margin-top: 40px"> | ||
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| * Estimating the dispersion parameter can be difficult with a small number of samples | ||
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| * DESeq2 models the variance as the sum of technical and biological variance | ||
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| * Esimate dispersion for each gene | ||
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| * ‘Share’ dispersion information between genes to obtain fitted estimate | ||
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| * Shrink gene-wise estimates towards the fitted estimates | ||
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| </div> | ||
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| <img src="images/dispersion.png" style="width: 38%; margin-top: 40px"> | ||
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| ## Differential Expression - worrying dispersion plot examples | ||
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| <!-- | ||
| A note about these dispersion plots: | ||
| I wrote the Harvard team and got the response below. This is basically what Dom | ||
| surmised. For the second plot, Dom thinks it is conceivable that there could be | ||
| nothing wrong with the data as such and that this pattern could arise if you | ||
| had a particularly unusual treatment, perhaps resulting in extreme | ||
| downregulation of a large cohort of genes and extreme upregulation of another | ||
| large cohort of genes. Either way, in both cases the thing to do is not to | ||
| worry about trying to interpret the problem from the dispersion plot, but to go | ||
| back to the raw data and figure out what is unusual. | ||
| From: Piper, Mary <[email protected]> | ||
| Sent: 01 July 2020 01:19 | ||
| To: Ashley Sawle <[email protected]> | ||
| Cc: HSPH-HBCTraining <[email protected]> | ||
| Subject: Re: A question about your RNAseq course from a fellow trainer | ||
| Hi Ash, | ||
| Glad that our materials are useful to you - we have converted the DGE materials | ||
| to an online course format too, which is available at: | ||
| https://hbctraining.github.io/DGE_workshop_salmon_online/schedule/. I added | ||
| these dispersion plots a while ago, and I believe that the first plot was from | ||
| data that was highly contaminated with rRNA. I think the rRNA was | ||
| computationally removed prior to the analysis from a low input RNA-seq library | ||
| back 3-4 years ago, but there were still large differences in the complexity of | ||
| the samples (the data was a real mess). The second plot was from a student who | ||
| had taken our course; I know the data was really weird in that it had very few | ||
| genes with higher mean counts (it also had weird MA plot and poor clustering by | ||
| PCA). However, since I had not analyzed the data, I only offered suggestions | ||
| for looking into the dataset - I don't know if they were able to rescue their | ||
| dataset (b/c I believe they also did not have any/many DE genes). So, the bad | ||
| dispersion plot is likely due to the strange nature of their data with few | ||
| genes with higher mean counts (so the dispersion could not be estimated as | ||
| accurately across genes with higher mean counts) and/or affected by the outlier | ||
| sample/s. | ||
| Note that in the online materials, I have an additional bad dispersion plot in | ||
| an exercise. This plot was from a pseudobulk scRNA-seq analysis - the data | ||
| reflect a single cell type that had huge variations in the number of cells | ||
| collapsed together per sample to generate the sample-level counts. Some samples | ||
| had only a handful of cells, while other samples had thousands. Therefore, you | ||
| can imagine the variation being quite large between samples of the same sample | ||
| group. | ||
| Hope this helps, and please let me know if you have additional questions. | ||
| Best wishes, | ||
| Mary | ||
| --> | ||
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| <div><br></div> | ||
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| <img src="images/bad_dispersion.png" class="centerimg" style="width: 100%"> | ||
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| <div style="text-align: right;"> | ||
| Bad dispersion plots from: https://github.com/hbctraining/DGE_workshop | ||
| </div> | ||
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| ## Differential Expression - linear models | ||
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| * Calculate coefficients describing change in gene expression | ||
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| * Linear Model $\rightarrow$ General Linear Model | ||
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| <img src="images/DESeq2_workflow_04.png" style="width: 16%; float: left; | ||
| padding-top: 5px"> | ||
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| <div style="width: 30%; margin-left: 20%; padding-top: 5px"> | ||
| ```{r, echo=FALSE, message=FALSE, warning=FALSE, fig.width=7, fig.height=4} | ||
| library(tidyverse) | ||
| dat <- data.frame(C1=rnorm(6, 4, 1), | ||
| C2=rnorm(6, 6, 1.3)) %>% | ||
| gather("Cat", "Expression") %>% | ||
| mutate(Group=as.numeric(factor(Cat))) | ||
| ewe <- lm(dat$Expression~dat$Group) | ||
| par(bg=NA, mar=c(5, 4, 0, 4) + 0.1) | ||
| plot(dat$Group, dat$Expression, | ||
| pch=21, | ||
| bg=rep(c("tomato", "steelblue"), each=6), | ||
| xlim=c(0, 3), | ||
| ylim=c(0, 8), xaxt="n", xlab="Group", ylab = "Expression") | ||
| axis(1, at = 1:2) | ||
| abline(h=5, lty=2, col="grey") | ||
| abline(ewe, col="red") | ||
| ``` | ||
| </div> | ||
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| ## Towards biological meaning - hierachical clustering {#less_space_after_title} | ||
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| <div style="line-height: 50%;"><br></div> | ||
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| <img src="images/BioMeaning.svg" class="centerimg" style="width: 100%; | ||
| display: block;"> | ||
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| ## | ||
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| <div style="text-align: center; margin-top: 30%"> | ||
| <span style="color: #2e3192; font-size: 80px">**Thank you**</span> | ||
| </div> | ||
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