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This is part of the online course Statistical Genomics 2021 (SGA21)

1 Breast cancer example

  • part of study https://doi.org/10.1093/jnci/djj052)
  • Histologic grade in breast cancer clinically prognostic. Association of histologic grade on expression of KPNA2 gene that is known to be associated with poor BC prognosis.
  • Population: all current and future breast cancer patients







2 Data Exploration

2.1 Import

library(tidyverse)
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──
## ✔ ggplot2 3.3.5     ✔ purrr   0.3.4
## ✔ tibble  3.1.6     ✔ dplyr   1.0.7
## ✔ tidyr   1.1.4     ✔ stringr 1.4.0
## ✔ readr   2.1.1     ✔ forcats 0.5.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
gene <- read.table("https://raw.githubusercontent.com/statOmics/SGA21/master/data/kpna2.txt",header=TRUE)
head(gene)

We will transform the variable grade and node to a factor

gene$grade <- as.factor(gene$grade)
gene$node <- as.factor(gene$node)

2.2 Summary statistics

geneSum <- gene %>%
  group_by(grade) %>%
  summarize(mean = mean(gene),
            sd = sd(gene),
            n=length(gene)
            ) %>%
  mutate(se = sd/sqrt(n))
geneSum

2.3 Visualisation

gene %>%
  ggplot(aes(x=grade,y=gene)) +
  geom_boxplot(outlier.shape=NA) +
  geom_jitter()

We can also save the plots as objects for later use!

p1 <- gene %>%
  ggplot(aes(x=grade,y=gene)) +
  geom_boxplot(outlier.shape=NA) +
  geom_jitter()

p2 <- gene %>%
  ggplot(aes(sample=gene)) +
  geom_qq() +
  geom_qq_line() +
  facet_wrap(~grade)

p1

p2

2.4 Research questions

Researchers want to assess the association of the histological grade on KPNA2 gene expression


2.5 Estimation of effect size and standard error

effectSize <- tibble(
  delta = geneSum$mean[2]- geneSum$mean[1],
  seDelta = geneSum %>%
    pull(se) %>%
    .^2 %>%
    sum %>%
    sqrt
  )
effectSize

3 Statistical Inference

  • Researchers want to assess the association of histological grade on KPNA2 gene expression
  • Inference?


  • Researchers want to assess the association of histological grade on KPNA2 gene expression
  • Inference?
  • testing + CI $ $ Assumptions

  • In general we start from alternative hypothese \(H_A\): we want to show an association

  • Gene expression of grade 1 and grade 3 patients is on average different

  • But, we will assess it by falsifying the opposite:

  • The average KPNA2 gene expression of grade 1 and grade 3 patients is equal


  • How likely is it to observe an equal or more extreme association than the one observed in the sample when the null hypothesis is true?

  • When we make assumptions about the distribution of our test statistic we can quantify this probability: p-value.

  • If the p-value is below a significance threshold \(\alpha\) we reject the null hypothesis

We control the probability on a false positive result at the \(\alpha\)-level (type I error)

  • The p-value will only be calculated correctly if the underlying assumptions hold!
library(gridExtra)
## 
## Attaching package: 'gridExtra'
## The following object is masked from 'package:dplyr':
## 
##     combine
p1

p2

t.test(gene~grade,data=gene)
## 
##  Welch Two Sample t-test
## 
## data:  gene by grade
## t = -7.2132, df = 15.384, p-value = 2.598e-06
## alternative hypothesis: true difference in means between group 1 and group 3 is not equal to 0
## 95 percent confidence interval:
##  -425.4218 -231.6751
## sample estimates:
## mean in group 1 mean in group 3 
##        232.5003        561.0487
effectSize <- effectSize %>%
  mutate(t.stat=delta/seDelta) %>%
  mutate(p.value= pt(-abs(t.stat),21.352)*2)

effectSize
  • Intensities are often not normally distributed and have a mean variance relation
  • Commonly log2-transformed
  • Differences on log scale:

\[ \log_2(B) - \log_2(A) = \log_2 \frac{B}{A} = \log_2 FC_{\frac{B}{A}} \]


3.1 Log transformation

gene <- gene %>%
  mutate(lgene = log2(gene))

p1 <- gene %>%
  ggplot(aes(x=grade,y=lgene)) +
  geom_boxplot(outlier.shape=NA) +
  geom_jitter()

p2 <- gene %>%
  ggplot(aes(sample=lgene)) +
  geom_qq() +
  geom_qq_line() +
  facet_wrap(~grade)

p1

p2

logtest <- t.test(lgene~grade,data=gene,var.equal=TRUE)
logtest
## 
##  Two Sample t-test
## 
## data:  lgene by grade
## t = -8.0455, df = 22, p-value = 5.372e-08
## alternative hypothesis: true difference in means between group 1 and group 3 is not equal to 0
## 95 percent confidence interval:
##  -1.610148 -0.950178
## sample estimates:
## mean in group 1 mean in group 3 
##        7.808478        9.088641
log2FC <- logtest$estimate[2]-logtest$estimate[1]
log2FC
## mean in group 3 
##        1.280163
names(log2FC) <- "g3-g1"
2^log2FC
##    g3-g1 
## 2.428664

3.2 Conclusion

There is a extremely significant association of the histological grade on the gene expression in tumor tissue. On average, the gene expression for the grade 3 patients is 2.43 times higher than the gene expression in grade 1 patients (95% CI [1.93, 3.05], \(p<<0.001\)).



The patients also differ in the their lymph node status. Hence, we have a two factorial design: grade x lymph node status!!!

Solution??


4 General Linear Model

How can we integrate multiple factors and continuous covariates in linear model.

\[ y_i= \beta_0 + \beta_1 x_{i,1} + \beta_2 x_{i,2} + \beta_{12}x_{i,1}x_{i,2}+\epsilon_i, \] with

  • \(x_{i,1}\) a dummy variable for histological grade: \(x_{i,1}=\begin{cases} 0& \text{grade 1}\\ 1& \text{grade 3} \end{cases}\)
  • \(x_{i,2}\) a dummy variable for : \(x_{i,2}=\begin{cases} 0& \text{lymph nodes were not removed}\\ 1& \text{lymph nodes were removed} \end{cases}\)
  • \(\epsilon_i\)?

4.1 Implementation in R

lm1 <- lm(gene~grade*node,data=gene)
summary(lm1)
## 
## Call:
## lm(formula = gene ~ grade * node, data = gene)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -201.748  -53.294   -6.308   46.216  277.601 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    180.51      44.37   4.068   0.0006 ***
## grade3         401.33      62.75   6.396 3.07e-06 ***
## node1          103.98      62.75   1.657   0.1131    
## grade3:node1  -145.57      88.74  -1.640   0.1166    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 108.7 on 20 degrees of freedom
## Multiple R-squared:  0.7437, Adjusted R-squared:  0.7052 
## F-statistic: 19.34 on 3 and 20 DF,  p-value: 3.971e-06

4.2 Assumptions

plot(lm1)


4.3 Breast cancer example

  • Paper: https://doi.org/10.1093/jnci/djj052
  • Histologic grade in breast cancer provides clinically important prognostic information. Two factors have to be concidered: Histologic grade (grade 1 and grade 3) and lymph node status (0 vs 1). The researchers assessed gene expression of the KPNA2 gene a protein-coding gene associated with breast cancer and are mainly interested in the association of histological grade. Note, that the gene variable consists of background corrected normalized intensities obtained with a microarray platform. Upon log-transformation, they are known to be a good proxy for the \(\log\) transformed concentration of gene expression product of the KPNA2 gene.
  • Research questions and translate them towards model parameters (contrasts)?
  • Make an R markdown file to answer the research questions
library(ExploreModelMatrix)
explMx <- VisualizeDesign(gene,designFormula = ~grade*node)
explMx$plotlist
## [[1]]

You can also explore the model matrix interactively:

ExploreModelMatrix(gene,designFormula = ~grade*node)

5 Linear regression in matrix form

5.1 Scalar form

  • Consider a vector of predictors \(\mathbf{x}=(x_1,\ldots,x_p)^T\) and
  • a real-valued response \(Y\)
  • then the linear regression model can be written as \[ Y=f(\mathbf{x}) +\epsilon=\beta_0+\sum\limits_{j=1}^p x_j\beta_j + \epsilon \] with i.i.d. \(\epsilon\sim N(0,\sigma^2)\)

5.2 Matrix form

  • \(n\) observations \((\mathbf{x}_1,y_1) \ldots (\mathbf{x}_n,y_n)\)
  • Regression in matrix notation \[\mathbf{Y}=\mathbf{X\beta} + \mathbf{\epsilon}\] with \(\mathbf{Y}=\left[\begin{array}{c}y_1\\ \vdots\\y_n\end{array}\right]\), \(\mathbf{X}=\left[\begin{array}{cccc} 1&x_{11}&\ldots&x_{1p}\\ \vdots&\vdots&&\vdots\\ 1&x_{n1}&\ldots&x_{np} \end{array}\right]\), \(\mathbf{\beta}=\left[\begin{array}{c}\beta_0\\ \vdots\\ \beta_p\end{array}\right]\) and \(\mathbf{\epsilon}=\left[\begin{array}{c} \epsilon_1 \\ \vdots \\ \epsilon_n\end{array}\right]\)

5.3 Least Squares (LS)

  • Minimize the residual sum of squares \[\begin{eqnarray*} RSS(\mathbf{\beta})&=&\sum\limits_{i=1}^n e^2_i\\ &=&\sum\limits_{i=1}^n \left(y_i-\beta_0-\sum\limits_{j=1}^p x_{ij}\beta_j\right)^2 \end{eqnarray*}\]
  • or in matrix notation \[\begin{eqnarray*} RSS(\mathbf{\beta})&=&(\mathbf{Y}-\mathbf{X\beta})^T(\mathbf{Y}-\mathbf{X\beta})\\ &=&\Vert \mathbf{Y}-\mathbf{X\beta}\Vert^2_2 \end{eqnarray*}\] with the \(L_2\)-norm of a \(p\)-dim. vector \(v\) \(\Vert \mathbf{v} \Vert=\sqrt{v_1^2+\ldots+v_p^2}\) \(\rightarrow\) \(\hat{\mathbf{\beta}}=\text{argmin}_\beta \Vert \mathbf{Y}-\mathbf{X\beta}\Vert^2_2\)

5.3.1 Minimize RSS

\[ \begin{array}{ccc} \frac{\partial RSS}{\partial \mathbf{\beta}}&=&\mathbf{0}\\\\ \frac{(\mathbf{Y}-\mathbf{X\beta})^T(\mathbf{Y}-\mathbf{X\beta})}{\partial \mathbf{\beta}}&=&\mathbf{0}\\\\ -2\mathbf{X}^T(\mathbf{Y}-\mathbf{X\beta})&=&\mathbf{0}\\\\ \mathbf{X}^T\mathbf{X\beta}&=&\mathbf{X}^T\mathbf{Y}\\\\ \hat{\mathbf{\beta}}&=&(\mathbf{X}^T\mathbf{X})^{-1}\mathbf{X}^T\mathbf{Y} \end{array} \]


5.3.2 Geometrical Interpretation

Toy Example: fit without intercept

  • n=3 and p=2 \[ \mathbf{X}=\left[\begin{array}{cc} 2&0\\ 0&2\\ 0&0 \end{array}\right] \]
set.seed(4)
x1 <- c(2,0,0)
x2 <- c(0,2,0)
y <- x1*0.5 + x2*0.5 + rnorm(3,2)
fit <- lm(y~-1+x1+x2)

5.3.2.1 Visualise fit

# predict values on regular xy grid
x1pred <- seq(-1, 3, length.out = 10)
x2pred <- seq(-1, 3, length.out = 10)
xy <- expand.grid(x1 = x1pred,
x2 = x2pred)
ypred <- matrix (nrow = 30, ncol = 30,
data = predict(fit, newdata = data.frame(xy),
interval = "prediction"))

library(plot3D)
## Warning in fun(libname, pkgname): no display name and no $DISPLAY environment
## variable
# fitted points for droplines to surface
th=20
ph=5
scatter3D(x1,
  x2,
  y,
  pch = 16,
  col="darkblue",
  cex = 1,
  theta = th,
  ticktype = "detailed",
  xlab = "x1",
  ylab = "x2",
  zlab = "y",  
  colvar=FALSE,
  bty = "g",
  xlim=c(-1,3),
  ylim=c(-1,3),
  zlim=c(-2,4))

for (i in 1:3)
  lines3D(
    x = rep(x1[i],2),
    y = rep(x2[i],2),
    z = c(y[i],fit$fitted[i]),
    col="darkblue",
    add=TRUE,
    lty=2)

z.pred3D <- outer(
  x1pred,
  x2pred,
  function(x1,x2)
  {
    fit$coef[1]*x1+fit$coef[2]*x2
  })

x.pred3D <- outer(
  x1pred,
  x2pred,
  function(x,y) x)

y.pred3D <- outer(
  x1pred,
  x2pred,
  function(x,y) y)

surf3D(
  x.pred3D,
  y.pred3D,
  z.pred3D,
  col="blue",
  facets=NA,
  add=TRUE)

5.3.3 Projection

  • We can also interpret the fit as the projection of the \(n\times 1\) vector \(\mathbf{Y}\) on the column space of the matrix \(\mathbf{X}\).

  • So each column in \(\mathbf{X}\) is also an \(n\times 1\) vector.

  • For the toy example n=3 and p=2. So the column space of X is a plane in the three dimensional space.

\[ \hat{\mathbf{Y}} = \mathbf{X} (\mathbf{X}^T\mathbf{X})^{-1} \mathbf{X}^T \mathbf{Y} \]

  1. Plane spanned by column space:
arrows3D(0,0,0,x1[1],x1[2],x1[3],xlim=c(0,5),ylim=c(0,5),zlim=c(0,5),bty = "g",theta=th,col=2,xlab="row 1",ylab="row 2",zlab="row 3")
text3D(x1[1],x1[2],x1[3],labels="X1",col=2,add=TRUE)
arrows3D(0,0,0,x2[1],x2[2],x2[3],add=TRUE,col=2)
text3D(x2[1],x2[2],x2[3],labels="X2",col=2,add=TRUE)

  1. Vector of Y:
arrows3D(0,0,0,x1[1],x1[2],x1[3],xlim=c(0,5),ylim=c(0,5),zlim=c(0,5),bty = "g",theta=th,col=2,xlab="row 1",ylab="row 2",zlab="row 3")
text3D(x1[1],x1[2],x1[3],labels="X1",col=2,add=TRUE)
arrows3D(0,0,0,x2[1],x2[2],x2[3],add=TRUE,col=2)
text3D(x2[1],x2[2],x2[3],labels="X2",col=2,add=TRUE)
arrows3D(0,0,0,y[1],y[2],y[3],add=TRUE,col="darkblue")
text3D(y[1],y[2],y[3],labels="Y",col="darkblue",add=TRUE)

  1. Projection of Y onto column space
arrows3D(0,0,0,x1[1],x1[2],x1[3],xlim=c(0,5),ylim=c(0,5),zlim=c(0,5),bty = "g",theta=th,col=2,xlab="row 1",ylab="row 2",zlab="row 3")
text3D(x1[1],x1[2],x1[3],labels="X1",col=2,add=TRUE)
arrows3D(0,0,0,x2[1],x2[2],x2[3],add=TRUE,col=2)
text3D(x2[1],x2[2],x2[3],labels="X2",col=2,add=TRUE)
arrows3D(0,0,0,y[1],y[2],y[3],add=TRUE,col="darkblue")
text3D(y[1],y[2],y[3],labels="Y",col="darkblue",add=TRUE)
arrows3D(0,0,0,fit$fitted[1],fit$fitted[2],fit$fitted[3],add=TRUE,col="darkblue")
segments3D(y[1],y[2],y[3],fit$fitted[1],fit$fitted[2],fit$fitted[3],add=TRUE,lty=2,col="darkblue")
text3D(fit$fitted[1],fit$fitted[2],fit$fitted[3],labels="fit",col="darkblue",add=TRUE)

  • Note, that it is also clear from the equation in the derivation of the LS that the residual is orthogonal on the column space: \[ -2 \mathbf{X}^T(\mathbf{Y}-\mathbf{X}\boldsymbol{\beta}) = 0 \]

5.4 Variance Estimator?

\[ \begin{array}{ccl} \hat{\boldsymbol{\Sigma}}_{\hat{\mathbf{\beta}}} &=&\text{var}\left[(\mathbf{X}^T\mathbf{X})^{-1}\mathbf{X}^T\mathbf{Y}\right]\\\\ &=&(\mathbf{X}^T\mathbf{X})^{-1}\mathbf{X}^T\text{var}\left[\mathbf{Y}\right]\mathbf{X}(\mathbf{X}^T\mathbf{X})^{-1}\\\\ &=&(\mathbf{X}^T\mathbf{X})^{-1}\mathbf{X}^T(\mathbf{I}\sigma^2)\mathbf{X}(\mathbf{X}^T\mathbf{X})^{-1} \\\\ &=&(\mathbf{X}^T\mathbf{X})^{-1}\mathbf{X}^T\mathbf{I}\quad\mathbf{X}(\mathbf{X}^T\mathbf{X})^{-1}\sigma^2\\\\ %\hat{\boldmath{\Sigma}}_{\hat{\mathbf{\beta}}}&=&(\mathbf{X}^T\mathbf{X})^{-1}\mathbf{X}^T\var\left[\mathbf{Y}\right](\mathbf{X}^T\mathbf{X})^{-1}\mathbf{X}\\ &=&(\mathbf{X}^T\mathbf{X})^{-1}\mathbf{X}^T\mathbf{X}(\mathbf{X}^T\mathbf{X})^{-1}\sigma^2\\\\ &=&(\mathbf{X}^T\mathbf{X})^{-1}\sigma^2 \end{array} \]


5.5 Contrasts

Hypotheses often involve linear combinations of the model parameters!

e.g.

  • \(H_0: \log_2{FC}_{g3n1-g1n1}= \beta_{g3} + \hat\beta_{g3n1}=0\) \(\rightarrow\) “grade3+grade3:node1 = 0”

  • Let \[ \boldsymbol{\beta} = \left[ \begin{array}{c} \beta_{0}\\ \beta_{g3}\\ \beta_{n1}\\ \beta_{g3:n1} \end{array} \right]\]

  • we can write that contrast using a contrast matrix: \[ \mathbf{L}=\left[\begin{array}{c}0\\1\\0\\1\end{array}\right] \rightarrow \mathbf{L}^T\boldsymbol{beta} \]

  • Then the variance becomes: \[ \text{var}_{\mathbf{L}^T\boldsymbol{\hat\beta}}= \mathbf{L}^T \boldsymbol{\Sigma}_{\boldsymbol{\hat\beta}}\mathbf{L} \]


6 Homework: Adopt the gene analysis on log scale in matrix form!

  1. Study the solution of the exercise to understand the analysis in R

  2. Calculate

  • model parameters and contrasts of interest
  • standard errors, standard errors on contrasts
  • t-test statistics on the model parameters and contrasts of interest
  1. Compare your results with the output of the lm(.) function

6.1 Inspiration

Tip: details on the implementation can be found in the book of Faraway (chapter 2). https://people.bath.ac.uk/jjf23/book/

  • Design matrix
X <- model.matrix(~grade*node,data=gene)
  • Transpose of a matrix: use function t(.)
t(X)
##              1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
## (Intercept)  1 1 1 1 1 1 1 1 1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
## grade3       1 1 0 0 0 1 1 0 1  1  0  1  0  0  0  1  0  1  0  1  0  1  1  0
## node1        1 1 1 1 0 0 1 1 0  0  0  0  0  0  0  1  1  0  1  1  1  0  1  0
## grade3:node1 1 1 0 0 0 0 1 0 0  0  0  0  0  0  0  1  0  0  0  1  0  0  1  0
## attr(,"assign")
## [1] 0 1 2 3
## attr(,"contrasts")
## attr(,"contrasts")$grade
## [1] "contr.treatment"
## 
## attr(,"contrasts")$node
## [1] "contr.treatment"
  • Matrix product %*% operator
t(X)%*%X
##              (Intercept) grade3 node1 grade3:node1
## (Intercept)           24     12    12            6
## grade3                12     12     6            6
## node1                 12      6    12            6
## grade3:node1           6      6     6            6
  • Degrees of freedom of a model?

\[ df = n-p\]

summary(lm1)
## 
## Call:
## lm(formula = gene ~ grade * node, data = gene)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -201.748  -53.294   -6.308   46.216  277.601 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    180.51      44.37   4.068   0.0006 ***
## grade3         401.33      62.75   6.396 3.07e-06 ***
## node1          103.98      62.75   1.657   0.1131    
## grade3:node1  -145.57      88.74  -1.640   0.1166    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 108.7 on 20 degrees of freedom
## Multiple R-squared:  0.7437, Adjusted R-squared:  0.7052 
## F-statistic: 19.34 on 3 and 20 DF,  p-value: 3.971e-06
dfRes <- (nrow(X)-ncol(X))
dfRes
## [1] 20
  • Variance estimator: MSE

\[ \hat \sigma^2 = \frac{\sum\limits_{i=1}^n\epsilon_i^2}{n-p} \]

  • Invert matrix: use function solve(.)

  • Diagonal elements of a matrix: use function diag(.)

t(X)%*%X
##              (Intercept) grade3 node1 grade3:node1
## (Intercept)           24     12    12            6
## grade3                12     12     6            6
## node1                 12      6    12            6
## grade3:node1           6      6     6            6
diag(t(X)%*%X)
##  (Intercept)       grade3        node1 grade3:node1 
##           24           12           12            6
---
title: "Recap general linear model"
author: "Lieven Clement"
date: "statOmics, Ghent University (https://statomics.github.io)"
output:
    html_document:
      code_download: true
      theme: cosmo
      toc: true
      toc_float: true
      highlight: tango
      number_sections: true
---


<a rel="license" href="https://creativecommons.org/licenses/by-nc-sa/4.0"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png" /></a>

This is part of the online course [Statistical Genomics 2021 (SGA21)](https://statomics.github.io/SGA21/)

# Breast cancer example

- part of study https://doi.org/10.1093/jnci/djj052)
- Histologic grade in breast cancer clinically prognostic.
Association of histologic grade on expression of KPNA2 gene that is known to be associated with poor BC prognosis.
- Population: all current and future breast cancer patients

![](https://raw.githubusercontent.com/statOmics/SGA21/master/figures/statGenomicsGent201718-1.jpeg)

---

![](https://raw.githubusercontent.com/statOmics/SGA21/master/figures/statGenomicsGent201718-2.jpeg)

---

![](https://raw.githubusercontent.com/statOmics/SGA21/master/figures/statGenomicsGent201718-3.jpeg)

---


![](https://raw.githubusercontent.com/statOmics/SGA21/master/figures/statGenomicsGent201718-4.jpeg)

---


![](https://raw.githubusercontent.com/statOmics/SGA21/master/figures/statGenomicsGent201718-5.jpeg)

---


![](https://raw.githubusercontent.com/statOmics/SGA21/master/figures/statGenomicsGent201718-6.jpeg)

---

# Data Exploration

## Import

```{r}
library(tidyverse)
gene <- read.table("https://raw.githubusercontent.com/statOmics/SGA21/master/data/kpna2.txt",header=TRUE)
head(gene)
```

We will transform the variable grade and node to a factor

```{r}
gene$grade <- as.factor(gene$grade)
gene$node <- as.factor(gene$node)
```

## Summary statistics

```{r}
geneSum <- gene %>%
  group_by(grade) %>%
  summarize(mean = mean(gene),
            sd = sd(gene),
            n=length(gene)
            ) %>%
  mutate(se = sd/sqrt(n))
geneSum
```

## Visualisation

```{r}
gene %>%
  ggplot(aes(x=grade,y=gene)) +
  geom_boxplot(outlier.shape=NA) +
  geom_jitter()
```

We can also save the plots as objects for later use!

```{r}
p1 <- gene %>%
  ggplot(aes(x=grade,y=gene)) +
  geom_boxplot(outlier.shape=NA) +
  geom_jitter()

p2 <- gene %>%
  ggplot(aes(sample=gene)) +
  geom_qq() +
  geom_qq_line() +
  facet_wrap(~grade)

p1
p2
```


## Research questions

Researchers want to assess the association of the histological grade on KPNA2 gene expression




![](https://raw.githubusercontent.com/statOmics/SGA21/master/figures/statGenomicsGent201718-6.jpeg)

---

## Estimation of effect size and standard error

```{r}
effectSize <- tibble(
  delta = geneSum$mean[2]- geneSum$mean[1],
  seDelta = geneSum %>%
    pull(se) %>%
    .^2 %>%
    sum %>%
    sqrt
  )
effectSize
```

# Statistical Inference

- Researchers want to assess the association of histological grade on KPNA2 gene expression
- Inference?

---

![](https://raw.githubusercontent.com/statOmics/SGA21/master/figures/statGenomicsGent201718-7.jpeg)


---


- Researchers want to assess the association of histological grade on KPNA2 gene expression
- Inference?
- testing + CI $ \rightarrow $ Assumptions

---

- In general we start from **alternative hypothese** $H_A$: we want to show an association
- Gene expression of grade 1 and grade 3 patients is on average different

- But, we will assess it by falsifying the opposite:

- The average KPNA2 gene expression of  grade 1 and grade 3 patients is equal

---

- How likely is it to observe an equal or more extreme association than the one observed in the sample when the null hypothesis is true?

- When we make assumptions about the distribution of our test statistic we can quantify this probability: **p-value**.
- If the p-value is below a significance threshold $\alpha$ we reject the null hypothesis

*We control the probability on a false positive result at the $\alpha$-level (type I error)*

- The p-value will only be calculated correctly if the underlying assumptions hold!

```{r}
library(gridExtra)
p1
p2
```

```{r}
t.test(gene~grade,data=gene)

effectSize <- effectSize %>%
  mutate(t.stat=delta/seDelta) %>%
  mutate(p.value= pt(-abs(t.stat),21.352)*2)

effectSize
```

- Intensities are often not normally distributed and have a mean variance relation
- Commonly log2-transformed
- Differences on log scale:

$$
\log_2(B) - \log_2(A) = \log_2 \frac{B}{A} = \log_2 FC_{\frac{B}{A}}
$$



![](https://raw.githubusercontent.com/statOmics/SGA21/master/figures/statGenomicsGent201718-8.jpeg)

---

## Log transformation

```{r}
gene <- gene %>%
  mutate(lgene = log2(gene))

p1 <- gene %>%
  ggplot(aes(x=grade,y=lgene)) +
  geom_boxplot(outlier.shape=NA) +
  geom_jitter()

p2 <- gene %>%
  ggplot(aes(sample=lgene)) +
  geom_qq() +
  geom_qq_line() +
  facet_wrap(~grade)

p1
p2

logtest <- t.test(lgene~grade,data=gene,var.equal=TRUE)
logtest

log2FC <- logtest$estimate[2]-logtest$estimate[1]
log2FC
names(log2FC) <- "g3-g1"
2^log2FC
```

## Conclusion

There is a extremely significant association of the histological grade on the gene expression in tumor tissue.  On average, the gene expression for the grade 3 patients is `r round(2^log2FC,2)` times higher than the gene expression in grade 1 patients (95\% CI  [`r paste(round(2^-logtest$conf.int[2:1],2),collapse=", ")`], $p<<0.001$).




![](https://raw.githubusercontent.com/statOmics/SGA21/master/figures/statGenomicsGent201718-10.jpeg) 

---


![](https://raw.githubusercontent.com/statOmics/SGA21/master/figures/statGenomicsGent201718-11.jpeg)

---

The patients also differ in the their lymph node status. Hence, we have a two factorial design: grade x lymph node status!!!

Solution??

![](https://raw.githubusercontent.com/statOmics/SGA21/master/figures/statGenomicsGent201718-12.jpeg)

---

# General Linear Model

How can we integrate multiple factors and continuous covariates in linear model.

\[
y_i= \beta_0 + \beta_1 x_{i,1} + \beta_2 x_{i,2} + \beta_{12}x_{i,1}x_{i,2}+\epsilon_i,
\]
with

- $x_{i,1}$ a dummy variable for histological grade: $x_{i,1}=\begin{cases}
0& \text{grade 1}\\
1& \text{grade 3}
\end{cases}$
- $x_{i,2}$ a dummy variable for : $x_{i,2}=\begin{cases}
0& \text{lymph nodes were not removed}\\
1& \text{lymph nodes were removed}
\end{cases}$
- $\epsilon_i$?

---

## Implementation in R

```{r}
lm1 <- lm(gene~grade*node,data=gene)
summary(lm1)
```

---

## Assumptions

```{r}
plot(lm1)
```

---

## Breast cancer example

-  Paper: https://doi.org/10.1093/jnci/djj052
- Histologic grade in breast cancer provides clinically important prognostic information. Two factors have to be concidered: Histologic grade (grade 1 and grade 3) and lymph node status (0 vs 1). The researchers assessed gene expression of the KPNA2 gene a protein-coding gene associated with breast cancer and are mainly interested in the association of histological grade. Note, that the gene variable consists of background corrected normalized intensities obtained with a microarray platform. Upon log-transformation, they are known to be a good proxy for the $\log$ transformed concentration of gene expression product of the KPNA2 gene.
- Research questions and translate them towards model parameters (contrasts)?
- Make an R markdown file to answer the research questions


```{r}
library(ExploreModelMatrix)
explMx <- VisualizeDesign(gene,designFormula = ~grade*node)
explMx$plotlist
```

You can also explore the model matrix interactively:

```{r eval=FALSE}
ExploreModelMatrix(gene,designFormula = ~grade*node)
```
---

# Linear regression in matrix form

## Scalar form

- Consider a vector of predictors $\mathbf{x}=(x_1,\ldots,x_p)^T$ and
- a real-valued response $Y$
- then the linear regression model can be written as
\[
Y=f(\mathbf{x}) +\epsilon=\beta_0+\sum\limits_{j=1}^p x_j\beta_j + \epsilon
\]
with i.i.d. $\epsilon\sim N(0,\sigma^2)$

## Matrix form

- $n$ observations $(\mathbf{x}_1,y_1) \ldots (\mathbf{x}_n,y_n)$
- Regression in matrix notation
\[\mathbf{Y}=\mathbf{X\beta} + \mathbf{\epsilon}\]
with $\mathbf{Y}=\left[\begin{array}{c}y_1\\ \vdots\\y_n\end{array}\right]$,
$\mathbf{X}=\left[\begin{array}{cccc} 1&x_{11}&\ldots&x_{1p}\\
\vdots&\vdots&&\vdots\\
1&x_{n1}&\ldots&x_{np}
\end{array}\right]$,
$\mathbf{\beta}=\left[\begin{array}{c}\beta_0\\ \vdots\\ \beta_p\end{array}\right]$ and
$\mathbf{\epsilon}=\left[\begin{array}{c} \epsilon_1 \\ \vdots \\ \epsilon_n\end{array}\right]$

## Least Squares (LS)
- Minimize the residual sum of squares
\begin{eqnarray*}
RSS(\mathbf{\beta})&=&\sum\limits_{i=1}^n e^2_i\\
&=&\sum\limits_{i=1}^n \left(y_i-\beta_0-\sum\limits_{j=1}^p x_{ij}\beta_j\right)^2
\end{eqnarray*}
- or in matrix notation
\begin{eqnarray*}
RSS(\mathbf{\beta})&=&(\mathbf{Y}-\mathbf{X\beta})^T(\mathbf{Y}-\mathbf{X\beta})\\
&=&\Vert \mathbf{Y}-\mathbf{X\beta}\Vert^2_2
\end{eqnarray*}
with the $L_2$-norm of a $p$-dim. vector $v$ $\Vert \mathbf{v} \Vert=\sqrt{v_1^2+\ldots+v_p^2}$
$\rightarrow$ $\hat{\mathbf{\beta}}=\text{argmin}_\beta \Vert \mathbf{Y}-\mathbf{X\beta}\Vert^2_2$

---

### Minimize RSS
\[
\begin{array}{ccc}
\frac{\partial RSS}{\partial \mathbf{\beta}}&=&\mathbf{0}\\\\
\frac{(\mathbf{Y}-\mathbf{X\beta})^T(\mathbf{Y}-\mathbf{X\beta})}{\partial \mathbf{\beta}}&=&\mathbf{0}\\\\
-2\mathbf{X}^T(\mathbf{Y}-\mathbf{X\beta})&=&\mathbf{0}\\\\
\mathbf{X}^T\mathbf{X\beta}&=&\mathbf{X}^T\mathbf{Y}\\\\
\hat{\mathbf{\beta}}&=&(\mathbf{X}^T\mathbf{X})^{-1}\mathbf{X}^T\mathbf{Y}
\end{array}
\]

---

### Geometrical Interpretation

Toy Example: fit without intercept

- n=3 and p=2
\[
\mathbf{X}=\left[\begin{array}{cc}
2&0\\
0&2\\
0&0
\end{array}\right]
\]

```{r}
set.seed(4)
x1 <- c(2,0,0)
x2 <- c(0,2,0)
y <- x1*0.5 + x2*0.5 + rnorm(3,2)
fit <- lm(y~-1+x1+x2)
```

#### Visualise fit

```{r}
# predict values on regular xy grid
x1pred <- seq(-1, 3, length.out = 10)
x2pred <- seq(-1, 3, length.out = 10)
xy <- expand.grid(x1 = x1pred,
x2 = x2pred)
ypred <- matrix (nrow = 30, ncol = 30,
data = predict(fit, newdata = data.frame(xy),
interval = "prediction"))

library(plot3D)


# fitted points for droplines to surface
th=20
ph=5
scatter3D(x1,
  x2,
  y,
  pch = 16,
  col="darkblue",
  cex = 1,
  theta = th,
  ticktype = "detailed",
  xlab = "x1",
  ylab = "x2",
  zlab = "y",  
  colvar=FALSE,
  bty = "g",
  xlim=c(-1,3),
  ylim=c(-1,3),
  zlim=c(-2,4))

for (i in 1:3)
  lines3D(
    x = rep(x1[i],2),
    y = rep(x2[i],2),
    z = c(y[i],fit$fitted[i]),
    col="darkblue",
    add=TRUE,
    lty=2)

z.pred3D <- outer(
  x1pred,
  x2pred,
  function(x1,x2)
  {
    fit$coef[1]*x1+fit$coef[2]*x2
  })

x.pred3D <- outer(
  x1pred,
  x2pred,
  function(x,y) x)

y.pred3D <- outer(
  x1pred,
  x2pred,
  function(x,y) y)

surf3D(
  x.pred3D,
  y.pred3D,
  z.pred3D,
  col="blue",
  facets=NA,
  add=TRUE)
```

### Projection

- We can also interpret the fit as the projection of the $n\times 1$ vector $\mathbf{Y}$ on the column space of the matrix $\mathbf{X}$.

- So each column in $\mathbf{X}$ is also an $n\times 1$ vector.

- For the toy example n=3 and p=2.
So the column space of X is a plane in the three dimensional space.

\[
\hat{\mathbf{Y}} = \mathbf{X} (\mathbf{X}^T\mathbf{X})^{-1} \mathbf{X}^T \mathbf{Y}
\]

1. Plane spanned by column space:
```{r}
arrows3D(0,0,0,x1[1],x1[2],x1[3],xlim=c(0,5),ylim=c(0,5),zlim=c(0,5),bty = "g",theta=th,col=2,xlab="row 1",ylab="row 2",zlab="row 3")
text3D(x1[1],x1[2],x1[3],labels="X1",col=2,add=TRUE)
arrows3D(0,0,0,x2[1],x2[2],x2[3],add=TRUE,col=2)
text3D(x2[1],x2[2],x2[3],labels="X2",col=2,add=TRUE)
```

2. Vector of Y:
```{r}
arrows3D(0,0,0,x1[1],x1[2],x1[3],xlim=c(0,5),ylim=c(0,5),zlim=c(0,5),bty = "g",theta=th,col=2,xlab="row 1",ylab="row 2",zlab="row 3")
text3D(x1[1],x1[2],x1[3],labels="X1",col=2,add=TRUE)
arrows3D(0,0,0,x2[1],x2[2],x2[3],add=TRUE,col=2)
text3D(x2[1],x2[2],x2[3],labels="X2",col=2,add=TRUE)
arrows3D(0,0,0,y[1],y[2],y[3],add=TRUE,col="darkblue")
text3D(y[1],y[2],y[3],labels="Y",col="darkblue",add=TRUE)
```

3. Projection of Y onto column space
```{r}
arrows3D(0,0,0,x1[1],x1[2],x1[3],xlim=c(0,5),ylim=c(0,5),zlim=c(0,5),bty = "g",theta=th,col=2,xlab="row 1",ylab="row 2",zlab="row 3")
text3D(x1[1],x1[2],x1[3],labels="X1",col=2,add=TRUE)
arrows3D(0,0,0,x2[1],x2[2],x2[3],add=TRUE,col=2)
text3D(x2[1],x2[2],x2[3],labels="X2",col=2,add=TRUE)
arrows3D(0,0,0,y[1],y[2],y[3],add=TRUE,col="darkblue")
text3D(y[1],y[2],y[3],labels="Y",col="darkblue",add=TRUE)
arrows3D(0,0,0,fit$fitted[1],fit$fitted[2],fit$fitted[3],add=TRUE,col="darkblue")
segments3D(y[1],y[2],y[3],fit$fitted[1],fit$fitted[2],fit$fitted[3],add=TRUE,lty=2,col="darkblue")
text3D(fit$fitted[1],fit$fitted[2],fit$fitted[3],labels="fit",col="darkblue",add=TRUE)

```

- Note, that it is also clear from the equation in the derivation of the LS that the residual is orthogonal on the column space:
\[
 -2 \mathbf{X}^T(\mathbf{Y}-\mathbf{X}\boldsymbol{\beta}) = 0
\]


---

## Variance Estimator?
\[
\begin{array}{ccl}
\hat{\boldsymbol{\Sigma}}_{\hat{\mathbf{\beta}}}
&=&\text{var}\left[(\mathbf{X}^T\mathbf{X})^{-1}\mathbf{X}^T\mathbf{Y}\right]\\\\
&=&(\mathbf{X}^T\mathbf{X})^{-1}\mathbf{X}^T\text{var}\left[\mathbf{Y}\right]\mathbf{X}(\mathbf{X}^T\mathbf{X})^{-1}\\\\
&=&(\mathbf{X}^T\mathbf{X})^{-1}\mathbf{X}^T(\mathbf{I}\sigma^2)\mathbf{X}(\mathbf{X}^T\mathbf{X})^{-1}
\\\\
&=&(\mathbf{X}^T\mathbf{X})^{-1}\mathbf{X}^T\mathbf{I}\quad\mathbf{X}(\mathbf{X}^T\mathbf{X})^{-1}\sigma^2\\\\
%\hat{\boldmath{\Sigma}}_{\hat{\mathbf{\beta}}}&=&(\mathbf{X}^T\mathbf{X})^{-1}\mathbf{X}^T\var\left[\mathbf{Y}\right](\mathbf{X}^T\mathbf{X})^{-1}\mathbf{X}\\
&=&(\mathbf{X}^T\mathbf{X})^{-1}\mathbf{X}^T\mathbf{X}(\mathbf{X}^T\mathbf{X})^{-1}\sigma^2\\\\
&=&(\mathbf{X}^T\mathbf{X})^{-1}\sigma^2
\end{array}
\]

---

## Contrasts

Hypotheses often involve linear combinations of the model parameters!

e.g.

- $H_0: \log_2{FC}_{g3n1-g1n1}= \beta_{g3} + \hat\beta_{g3n1}=0$ $\rightarrow$ "grade3+grade3:node1 = 0"

- Let \[
\boldsymbol{\beta} = \left[
\begin{array}{c}
\beta_{0}\\
\beta_{g3}\\
\beta_{n1}\\
\beta_{g3:n1}
\end{array}
\right]\]
- we can write that contrast using a contrast matrix:
\[
\mathbf{L}=\left[\begin{array}{c}0\\1\\0\\1\end{array}\right] \rightarrow \mathbf{L}^T\boldsymbol{beta} \]

- Then the variance becomes:
\[
\text{var}_{\mathbf{L}^T\boldsymbol{\hat\beta}}= \mathbf{L}^T \boldsymbol{\Sigma}_{\boldsymbol{\hat\beta}}\mathbf{L}
\]


---

# Homework: Adopt the gene analysis on log scale in matrix form!

1. Study the solution of the exercise to understand the analysis in R


2. Calculate
- model parameters and contrasts of interest
- standard errors, standard errors on contrasts
- t-test statistics on the model parameters and contrasts of interest

3. Compare your results with the output of the lm(.) function


---

## Inspiration

Tip: details on the implementation can be found in the book of Faraway (chapter 2). https://people.bath.ac.uk/jjf23/book/

- Design matrix

```{r}
X <- model.matrix(~grade*node,data=gene)
```

- Transpose of a matrix: use function t(.)

```{r}
t(X)
```

- Matrix product %\*% operator

```{r}
t(X)%*%X
```

- Degrees of freedom of a model?

$$ df =  n-p$$

```{r}
summary(lm1)
dfRes <- (nrow(X)-ncol(X))
dfRes
```

- Variance estimator: MSE

$$
\hat \sigma^2 = \frac{\sum\limits_{i=1}^n\epsilon_i^2}{n-p}
$$


- Invert matrix: use function solve(.)

- Diagonal elements of a matrix: use function diag(.)

```{r}
t(X)%*%X
diag(t(X)%*%X)
```
