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Rmd | 1920833 | John Blischak | 2018-08-20 | Refactor first edition of chapter 1 into distinct lessons. |

The goal is to provide some visualizations to help you understand linear models.

```
library("broom")
library("cowplot")
library("dplyr")
library("ggplot2")
theme_set(theme_classic(base_size = 16))
library("knitr")
opts_chunk$set(fig.width = 10, fig.height = 5, message = FALSE)
```

Visualize boxplots of a gene that is clearly differentially expressed (a) and one that is unclear due to increased variance (b).

```
df_vis <- data.frame(status = rep(c("con", "treat"), each = 50)) %>%
mutate(gene_de = c(rpois(n() / 2, lambda = 12), rpois(n() / 2, lambda = 30)),
gene_var =c(rpois(n() / 2, lambda = 15) + rnorm(n() / 2, sd = 10),
rpois(n() / 2, lambda = 25) + rnorm(n() / 2, sd = 10)))
box_de <- ggplot(df_vis, aes(x = status, y = gene_de)) +
geom_boxplot() +
theme_classic(base_size = 16) +
ylim(0, 40) +
labs(x = "Treatment status", y = "Gene expression level",
title = "Differential expression")
box_var <- ggplot(df_vis, aes(x = status, y = gene_var)) +
geom_boxplot() +
theme_classic(base_size = 16) +
ylim(0, 40) +
labs(x = "Treatment status", y = "Gene expression level",
title = "High variance")
plot_grid(box_de, box_var, labels = letters[1:2])
```

`Warning: Removed 1 rows containing non-finite values (stat_boxplot).`

`Warning: Removed 9 rows containing non-finite values (stat_boxplot).`

Version | Author | Date |
---|---|---|

4976490 | John Blischak | 2018-08-20 |

As you just visualized, differential expression describes the situation in which a gene has a different mean expression level between conditions. While some gene expression patterns are easily diagnosed as differential expression or not from a quick visualization, you also saw some examples that were more ambiguous. Furthermore, you need a method that is more robust than a quick visual inspection and also scales to thousands of genes. For this you will use the tools of statistical inference to determine if the difference in mean expression level is larger than that expected by chance. Specifically, you will use linear models to perform the hypothesis tests. Linear models are an ideal choice for genomics experiments because their flexibility and robustness to assumptions allow you to conveniently model data from various study designs and data sources.

You should have already been introduced to linear models, for example in a DataCamp course such as Correlation and Regression, or in an introductory statistics course. Here I’ll review the terminology we will use in the remainder of the course, how to specify a linear model in R, and the intuition behind linear models.

\[ Y = \beta_0 + \beta_1 X_1 + \epsilon \]

In this equation of a linear model, Y is the response variable. It must be a continuous variable. In the context of differential expression, it is a relative measure of either RNA or protein expression level for one gene. \(X_1\) is an explanatory variable, which can be continuous or discrete, for example, control group versus treatment, or mutant versus wild type. \(\beta_1\) quantifies the effect of the explanatory variable on the response variable. Furthermore, we can add additional explanatory variables to the equation for more complicated experimental designs. Lastly, models the random noise in the measurements.

In R, you specify a linear model with the function `lm`

. This uses R’s formula syntax. On the left is the object that contains the response variable, and to the right of the tilde are the objects that contain the explanatory variables.

`lm(y ~ x1)`

A second explanatory variable can be added with a plus sign.

\[ Y = \beta_0 + \beta_1 X_1 + \beta_2 X_2 + \epsilon \]

`lm(y ~ x1 + x2)`

The simulation below demonstrates how the statistical significance of the computed F-statistic of a linear model is equally affected by the noise (the residual sum of squares) and the signal (the explained sum of squares).

```
# Simulate a linear regression.
#
# n = sample size
# effect = slope
# error = standard deviation of distribution of residuals
# seed = seed for random number generator
#
# Returns a data.frame with the following columns:
#
# x Explanatory variable
# y Response variable
# y_bar Mean of response variable
# intercept Intercept of least squares regression line
# slope Slope of least squares regression line
# y_hat Fitted values
# fstat F-statistic
# ss_exp Explained sum of squares
# ss_res Residual sum of squares (noise)
sim_lm <- function(n, effect, error, seed = 1) {
stopifnot(is.numeric(n), n > 0, length(n) == 1)
stopifnot(is.numeric(effect), length(effect) == 1)
stopifnot(is.numeric(error), error > 0, length(error) == 1)
stopifnot(is.numeric(seed), length(seed) == 1)
set.seed(seed)
x = runif(n, min = -25, max = 25)
y = x * effect + rnorm(n, sd = error)
y_bar = mean(y)
mod <- lm(y ~ x)
coefs <- coef(mod)
intercept <- coefs[1]
slope <- coefs[2]
y_hat = fitted(mod)
anova_tidy <- tidy(anova(mod))
fstat <- anova_tidy$statistic[1]
ss <- anova_tidy$sumsq
ss_exp <- ss[1]
ss_res <- ss[2]
stopifnot(ss_exp - sum((y_hat - y_bar)^2) < 0.01)
stopifnot(ss_res - sum(residuals(mod)^2) < 0.01)
return(data.frame(x, y, y_bar, intercept, slope, y_hat, fstat, ss_exp, ss_res,
row.names = 1:n))
}
# Visualize the residual sum of squares
plot_ss_res <- function(d) {
ggplot(d, aes(x = x, y = y)) +
geom_point() +
geom_abline(aes(intercept = intercept, slope = slope)) +
geom_linerange(aes(ymin = y, ymax = y_hat), color = "red",
linetype = "dashed") +
geom_text(aes(x = quantile(x, 0.25), y = quantile(y, 0.75),
label = round(ss_res, 2)), color = "red") +
labs(title = "Residual sum of squares (noise)") +
ylim(-60, 60)
}
# Visualize the explained sum of squares
plot_ss_exp <- function(d) {
ggplot(d, aes(x = x, y = y)) +
geom_abline(aes(intercept = intercept, slope = slope)) +
geom_hline(aes(yintercept = y_bar)) +
geom_linerange(aes(ymin = y_hat, ymax = y_bar), color = "blue",
linetype = "dashed") +
geom_text(aes(x = quantile(x, 0.25), y = quantile(y, 0.75),
label = round(ss_exp, 2)), color = "blue") +
labs(title = "Explained sum of squares") +
ylim(-60, 60)
}
```

```
# baseline
baseline <- sim_lm(n = 10, effect = 2, error = 5)
baseline_ss_res <- plot_ss_res(baseline)
baseline_ss_exp <- plot_ss_exp(baseline)
plot_grid(baseline_ss_res, baseline_ss_exp)
```

Version | Author | Date |
---|---|---|

4976490 | John Blischak | 2018-08-20 |

`baseline$fstat[1]`

`[1] 280.3557`

The baseline simulation has an F-statistic of 280.3557372.

```
# Increased error
more_error <- sim_lm(n = 10, effect = 2, error = 10)
more_error_ss_res <- plot_ss_res(more_error)
more_error_ss_exp <- plot_ss_exp(more_error)
plot_grid(more_error_ss_res, more_error_ss_exp)
```

Version | Author | Date |
---|---|---|

4976490 | John Blischak | 2018-08-20 |

`more_error$fstat[1]`

`[1] 72.89125`

Doubling the error decreases the test statistic by a factor of 4.

```
# Decreased signal
less_signal <- sim_lm(n = 10, effect = 1, error = 5)
less_signal_ss_res <- plot_ss_res(less_signal)
less_signal_ss_exp <- plot_ss_exp(less_signal)
plot_grid(less_signal_ss_res, less_signal_ss_exp)
```

Version | Author | Date |
---|---|---|

4976490 | John Blischak | 2018-08-20 |

`less_signal$fstat[1]`

`[1] 72.89125`

Similarly, halving the signal decreases the test statistic by a factor of 4.

`sessionInfo()`

```
R version 3.3.3 (2017-03-06)
Platform: x86_64-apple-darwin13.4.0 (64-bit)
Running under: OS X Yosemite 10.10.5
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] bindrcpp_0.2.2 knitr_1.20 dplyr_0.7.7 cowplot_0.9.2
[5] ggplot2_2.2.1 broom_0.4.4
loaded via a namespace (and not attached):
[1] Rcpp_1.0.0 pillar_1.2.2 git2r_0.23.0
[4] plyr_1.8.4 workflowr_1.1.1.9001 bindr_0.1.1
[7] tools_3.3.3 htmldeps_0.1.1 digest_0.6.13
[10] evaluate_0.10.1 tibble_1.4.2 nlme_3.1-131
[13] gtable_0.2.0 lattice_0.20-35 pkgconfig_2.0.1
[16] rlang_0.3.0.1 psych_1.8.4 yaml_2.2.0
[19] parallel_3.3.3 stringr_1.3.1 fs_1.2.6
[22] rprojroot_1.3-2 grid_3.3.3 tidyselect_0.2.3
[25] glue_1.2.0.9000 R6_2.2.2 foreign_0.8-69
[28] rmarkdown_1.10.14 purrr_0.2.5 tidyr_0.8.1
[31] reshape2_1.4.3 magrittr_1.5 whisker_0.3-2
[34] backports_1.1.2 scales_0.5.0 htmltools_0.3.6
[37] assertthat_0.2.0 mnormt_1.5-5 colorspace_1.3-2
[40] labeling_0.3 stringi_1.2.3 lazyeval_0.2.1
[43] munsell_0.5.0
```