require(IgGeneUsage)
require(rstan)
require(knitr)
require(ggplot2)
require(ggforce)
require(ggrepel)
require(reshape2)
require(patchwork)
Decoding the properties of immune receptor repertoires (IRRs) is key to understanding how our adaptive immune system responds to challenges, such as viral infection or cancer. One important quantitative property of IRRs is their immunoglobulin (Ig) gene usage, i.e. how often are the differnt Igs that make up the immune receptors used in a given IRR. Furthermore, we may ask: is there differential gene usage (DGU) between IRRs from different biological conditions (e.g. healthy vs tumor).
Both of these questions can be answered quantitatively by are answered by IgGeneUsage.
The main input of IgGeneUsage is a data.frame that has the following columns:
IgGeneUsage transforms the input data as follows.
First, given \(R\) repertoires with \(G\) genes each, IgGeneUsage generates a gene usage matrix \(Y^{R \times G}\). Row sums in \(Y\) define the total usage (\(N\)) in each repertoire.
Second, for the analysis of DGU between biological conditions, we use a Bayesian model (\(M\)) for zero-inflated beta-binomial regression. Empirically, we know that Ig gene usage data can be noisy also not exhaustive, i.e. some Ig genes that are systematically rearranged at low probability might not be sampled, and certain Ig genes are not encoded (or dysfunctional) in some individuals. \(M\) can fit over-dispersed and zero-inflated Ig gene usage data.
In the output of IgGeneUsage, we report the mean effect size (es or \(\gamma\)) and its 95% highest density interval (HDI). Genes with \(\gamma \neq 0\) (e.g. if 95% HDI of \(\gamma\) excludes 0) are most likely to experience differential usage. Additionally, we report the probability of differential gene usage (\(\pi\)): \[\begin{align} \pi = 2 \cdot \max\left(\int_{\gamma = -\infty}^{0} p(\gamma)\mathrm{d}\gamma, \int_{\gamma = 0}^{\infty} p(\gamma)\mathrm{d}\gamma\right) - 1 \end{align}\] with \(\pi = 1\) for genes with strong differential usage, and \(\pi = 0\) for genes with negligible differential gene usage. Both metrics are computed based on the posterior distribution of \(\gamma\), and are thus related.
IgGeneUsage has a couple of built-in Ig gene usage datasets. Some were obtained from studies and others were simulated.
Lets look into the simulated dataset d_zibb_3
. This dataset was generated
by a zero-inflated beta-binomial (ZIBB) model, and IgGeneUsage
was designed to fit ZIBB-distributed data.
data("d_zibb_3", package = "IgGeneUsage")
knitr::kable(head(d_zibb_3))
individual_id | gene_name | gene_usage_count | condition |
---|---|---|---|
I_1 | G_1 | 29 | C_1 |
I_1 | G_2 | 135 | C_1 |
I_1 | G_3 | 6 | C_1 |
I_1 | G_4 | 52 | C_1 |
I_1 | G_5 | 68 | C_1 |
I_1 | G_6 | 41 | C_1 |
We can also visualize d_zibb_3
with ggplot:
ggplot(data = d_zibb_3)+
geom_point(aes(x = gene_name, y = gene_usage_count, col = condition),
position = position_dodge(width = .7), shape = 21)+
theme_bw(base_size = 11)+
ylab(label = "Gene usage [count]")+
xlab(label = '')+
theme(legend.position = "top")+
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.4))
As main input IgGeneUsage uses a data.frame formatted as e.g.
d_zibb_3
. Other input parameters allow you to configure specific settings
of the rstan sampler.
In this example, we analyze d_zibb_3
with 3 MCMC chains, 1500 iterations
each including 500 warm-ups using a single CPU core (Hint: for parallel
chain execution set parameter mcmc_cores
= 3). We report for each model
parameter its mean and 95% highest density interval (HDIs).
Important remark: you should run DGU analyses using default IgGeneUsage parameters. If warnings or errors are reported with regard to the MCMC sampling, please consult the Stan manual1 https://mc-stan.org/misc/warnings.html and adjust the inputs accordingly. If the warnings persist, please submit an issue with a reproducible script at the Bioconductor support site or on Github2 https://github.com/snaketron/IgGeneUsage/issues.
M <- DGU(ud = d_zibb_3, # input data
mcmc_warmup = 300, # how many MCMC warm-ups per chain (default: 500)
mcmc_steps = 1500, # how many MCMC steps per chain (default: 1,500)
mcmc_chains = 3, # how many MCMC chain to run (default: 4)
mcmc_cores = 1, # how many PC cores to use? (e.g. parallel chains)
hdi_lvl = 0.95, # highest density interval level (de fault: 0.95)
adapt_delta = 0.8, # MCMC target acceptance rate (default: 0.95)
max_treedepth = 10) # tree depth evaluated at each step (default: 12)
FALSE
FALSE SAMPLING FOR MODEL 'dgu' NOW (CHAIN 1).
FALSE Chain 1:
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FALSE Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 3.26 seconds.
FALSE Chain 1: Adjust your expectations accordingly!
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In the output of DGU, we provide the following objects:
dgu
and dgu_prob
(main results of IgGeneUsage):
quantitative DGU summary on a log- and probability-scale, respectively.gu
: condition-specific relative gene usage (GU) of each genetheta
: probabilities of gene usage in each sampleppc
: posterior predictive checks data (see section ‘Model checking’)ud
: processed Ig gene usage datafit
: rstan (‘stanfit’) object of the fitted model \(\rightarrow\) used
for model checks (see section ‘Model checking’)summary(M)
FALSE Length Class Mode
FALSE dgu 9 data.frame list
FALSE dgu_prob 9 data.frame list
FALSE gu 8 data.frame list
FALSE theta 12 data.frame list
FALSE ppc 2 -none- list
FALSE ud 24 -none- list
FALSE fit 1 stanfit S4
Check your model fit. For this, you can use the object glm.
rstan::check_hmc_diagnostics(M$fit)
FALSE
FALSE Divergences:
FALSE
FALSE Tree depth:
FALSE
FALSE Energy:
rstan::stan_rhat(object = M$fit)|rstan::stan_ess(object = M$fit)
The model used by IgGeneUsage is generative, i.e. with the model we can generate usage of each Ig gene in a given repertoire (y-axis). Error bars show 95% HDI of mean posterior prediction. The predictions can be compared with the observed data (x-axis). For points near the diagonal \(\rightarrow\) accurate prediction.
ggplot(data = M$ppc$ppc_rep)+
facet_wrap(facets = ~individual_id, ncol = 5)+
geom_abline(intercept = 0, slope = 1, linetype = "dashed", col = "darkgray")+
geom_errorbar(aes(x = observed_count, y = ppc_mean_count,
ymin = ppc_L_count, ymax = ppc_H_count), col = "darkgray")+
geom_point(aes(x = observed_count, y = ppc_mean_count), size = 1)+
theme_bw(base_size = 11)+
theme(legend.position = "top")+
xlab(label = "Observed usage [counts]")+
ylab(label = "PPC usage [counts]")
Prediction of generalized gene usage within a biological condition is also possible. We show the predictions (y-axis) of the model, and compare them against the observed mean usage (x-axis). If the points are near the diagonal \(\rightarrow\) accurate prediction. Errors are 95% HDIs of the mean.
ggplot(data = M$ppc$ppc_condition)+
geom_errorbar(aes(x = gene_name, ymin = ppc_L_prop*100,
ymax = ppc_H_prop*100, col = condition),
position = position_dodge(width = 0.65), width = 0.1)+
geom_point(aes(x = gene_name, y = ppc_mean_prop*100,col = condition),
position = position_dodge(width = 0.65))+
theme_bw(base_size = 11)+
theme(legend.position = "top")+
xlab(label = "Observed usage [%]")+
ylab(label = "PPC usage [%]")+
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.4))
Each row of glm
summarizes the degree of DGU observed for specific
Igs. Two metrics are reported:
es
(also referred to as \(\gamma\)): effect size on DGU, where contrast
gives the direction of the effect (e.g. tumor - healthy or healthy - tumor)pmax
(also referred to as \(\pi\)): probability of DGU (parameter \(\pi\)
from model \(M\))For es
we also have the mean, median standard error (se), standard
deviation (sd), L (low bound of 95% HDI), H (high bound of 95% HDI)
kable(x = head(M$dgu), row.names = FALSE, digits = 2)
es_mean | es_mean_se | es_sd | es_median | es_L | es_H | contrast | gene_name | pmax |
---|---|---|---|---|---|---|---|---|
0.19 | 0.01 | 0.29 | 0.12 | -0.25 | 0.91 | C_1-vs-C_2 | G_1 | 0.49 |
-0.02 | 0.00 | 0.20 | -0.01 | -0.46 | 0.38 | C_1-vs-C_2 | G_4 | 0.05 |
-0.09 | 0.00 | 0.23 | -0.05 | -0.62 | 0.32 | C_1-vs-C_2 | G_3 | 0.28 |
-0.06 | 0.00 | 0.17 | -0.05 | -0.44 | 0.26 | C_1-vs-C_2 | G_2 | 0.27 |
0.06 | 0.00 | 0.18 | 0.05 | -0.26 | 0.46 | C_1-vs-C_2 | G_5 | 0.26 |
-0.03 | 0.00 | 0.19 | -0.02 | -0.44 | 0.33 | C_1-vs-C_2 | G_8 | 0.13 |
We know that the values of \gamma
and \pi
are related to each other.
Lets visualize them for all genes (shown as a point). Names are shown for
genes associated with \(\pi \geq 0.95\). Dashed horizontal line represents
null-effect (\(\gamma = 0\)).
Notice that the gene with \(\pi \approx 1\) also has an effect size whose 95% HDI (error bar) does not overlap the null-effect. The genes with high degree of differential usage are easy to detect with this figure.
# format data
stats <- M$dgu
stats <- stats[order(abs(stats$es_mean), decreasing = FALSE), ]
stats$gene_fac <- factor(x = stats$gene_name, levels = unique(stats$gene_name))
ggplot(data = stats)+
geom_hline(yintercept = 0, linetype = "dashed", col = "gray")+
geom_errorbar(aes(x = pmax, y = es_mean, ymin = es_L, ymax = es_H),
col = "darkgray")+
geom_point(aes(x = pmax, y = es_mean, col = contrast))+
geom_text_repel(data = stats[stats$pmax >= 0.95, ],
aes(x = pmax, y = es_mean, label = gene_fac),
min.segment.length = 0, size = 2.75)+
theme_bw(base_size = 11)+
theme(legend.position = "top")+
xlab(label = expression(pi))+
xlim(c(0, 1))+
ylab(expression(gamma))
Lets visualize the observed data of the genes with high probability of differential gene usage (\(\pi \geq 0.95\)). Here we show the gene usage in %.
promising_genes <- stats$gene_name[stats$pmax >= 0.95]
ppc_gene <- M$ppc$ppc_condition
ppc_gene <- ppc_gene[ppc_gene$gene_name %in% promising_genes, ]
ppc_rep <- M$ppc$ppc_rep
ppc_rep <- ppc_rep[ppc_rep$gene_name %in% promising_genes, ]
ggplot()+
geom_point(data = ppc_rep,
aes(x = gene_name, y = observed_prop*100, col = condition),
size = 1, fill = "black",
position = position_jitterdodge(jitter.width = 0.1,
jitter.height = 0,
dodge.width = 0.35))+
geom_errorbar(data = ppc_gene,
aes(x = gene_name, ymin = ppc_L_prop*100,
ymax = ppc_H_prop*100, group = condition),
position = position_dodge(width = 0.35), width = 0.15)+
theme_bw(base_size = 11)+
theme(legend.position = "top")+
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.4))+
ylab(label = "PPC usage [%]")+
xlab(label = '')
Lets also visualize the predicted gene usage counts in each repertoire.
ggplot()+
geom_point(data = ppc_rep,
aes(x = gene_name, y = observed_count, col = condition),
size = 1, fill = "black",
position = position_jitterdodge(jitter.width = 0.1,
jitter.height = 0,
dodge.width = 0.5))+
theme_bw(base_size = 11)+
theme(legend.position = "top")+
ylab(label = "PPC usage [count]")+
xlab(label = '')+
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.4))
IgGeneUsage also reports the inferred gene usage (GU)
probability of individual genes in each condition. For a given gene we
report its mean GU (prob_mean
) and the 95% (for instance) HDI (prob_L
and prob_H
).
ggplot(data = M$gu)+
geom_errorbar(aes(x = gene_name, y = prob_mean, ymin = prob_L,
ymax = prob_H, col = condition),
width = 0.1, position = position_dodge(width = 0.4))+
geom_point(aes(x = gene_name, y = prob_mean, col = condition), size = 1,
position = position_dodge(width = 0.4))+
theme_bw(base_size = 11)+
theme(legend.position = "top")+
ylab(label = "GU [probability]")+
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.4))
To assert the robustness of the probability of DGU (\(\pi\)) and the effect size (\(\gamma\)), IgGeneUsage has a built-in procedure for fully Bayesian leave-one-out (LOO) analysis.
During each step of LOO, we discard the data of one of the R repertoires, and use the remaining data to analyze for DGU. In each step we record \(\pi\) and \(\gamma\) for all genes, including the mean and 95% HDI of \(\gamma\). We assert quantitatively the robustness of \(\pi\) and \(\gamma\) by evaluating their variability for a specific gene.
This analysis can be computationally demanding.
L <- LOO(ud = d_zibb_3, # input data
mcmc_warmup = 500, # how many MCMC warm-ups per chain (default: 500)
mcmc_steps = 1000, # how many MCMC steps per chain (default: 1,500)
mcmc_chains = 1, # how many MCMC chain to run (default: 4)
mcmc_cores = 1, # how many PC cores to use? (e.g. parallel chains)
hdi_lvl = 0.95, # highest density interval level (de fault: 0.95)
adapt_delta = 0.8, # MCMC target acceptance rate (default: 0.95)
max_treedepth = 10) # tree depth evaluated at each step (default: 12)
FALSE
FALSE SAMPLING FOR MODEL 'dgu' NOW (CHAIN 1).
FALSE Chain 1:
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FALSE Chain 1: Adjust your expectations accordingly!
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FALSE Chain 1:
FALSE Chain 1: Elapsed Time: 4.275 seconds (Warm-up)
FALSE Chain 1: 2.863 seconds (Sampling)
FALSE Chain 1: 7.138 seconds (Total)
FALSE Chain 1:
FALSE
FALSE SAMPLING FOR MODEL 'dgu' NOW (CHAIN 1).
FALSE Chain 1:
FALSE Chain 1: Gradient evaluation took 0.000225 seconds
FALSE Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 2.25 seconds.
FALSE Chain 1: Adjust your expectations accordingly!
FALSE Chain 1:
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FALSE Chain 1:
FALSE Chain 1: Elapsed Time: 4.449 seconds (Warm-up)
FALSE Chain 1: 2.823 seconds (Sampling)
FALSE Chain 1: 7.272 seconds (Total)
FALSE Chain 1:
FALSE
FALSE SAMPLING FOR MODEL 'dgu' NOW (CHAIN 1).
FALSE Chain 1:
FALSE Chain 1: Gradient evaluation took 0.000216 seconds
FALSE Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 2.16 seconds.
FALSE Chain 1: Adjust your expectations accordingly!
FALSE Chain 1:
FALSE Chain 1:
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FALSE Chain 1:
FALSE Chain 1: Elapsed Time: 3.755 seconds (Warm-up)
FALSE Chain 1: 2.731 seconds (Sampling)
FALSE Chain 1: 6.486 seconds (Total)
FALSE Chain 1:
FALSE
FALSE SAMPLING FOR MODEL 'dgu' NOW (CHAIN 1).
FALSE Chain 1:
FALSE Chain 1: Gradient evaluation took 0.000152 seconds
FALSE Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 1.52 seconds.
FALSE Chain 1: Adjust your expectations accordingly!
FALSE Chain 1:
FALSE Chain 1:
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FALSE Chain 1:
FALSE Chain 1: Elapsed Time: 4.041 seconds (Warm-up)
FALSE Chain 1: 2.69 seconds (Sampling)
FALSE Chain 1: 6.731 seconds (Total)
FALSE Chain 1:
FALSE
FALSE SAMPLING FOR MODEL 'dgu' NOW (CHAIN 1).
FALSE Chain 1:
FALSE Chain 1: Gradient evaluation took 0.000152 seconds
FALSE Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 1.52 seconds.
FALSE Chain 1: Adjust your expectations accordingly!
FALSE Chain 1:
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FALSE Chain 1:
FALSE Chain 1: Elapsed Time: 3.713 seconds (Warm-up)
FALSE Chain 1: 2.741 seconds (Sampling)
FALSE Chain 1: 6.454 seconds (Total)
FALSE Chain 1:
FALSE
FALSE SAMPLING FOR MODEL 'dgu' NOW (CHAIN 1).
FALSE Chain 1:
FALSE Chain 1: Gradient evaluation took 0.000147 seconds
FALSE Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 1.47 seconds.
FALSE Chain 1: Adjust your expectations accordingly!
FALSE Chain 1:
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FALSE Chain 1:
FALSE Chain 1: Elapsed Time: 3.801 seconds (Warm-up)
FALSE Chain 1: 2.651 seconds (Sampling)
FALSE Chain 1: 6.452 seconds (Total)
FALSE Chain 1:
FALSE
FALSE SAMPLING FOR MODEL 'dgu' NOW (CHAIN 1).
FALSE Chain 1:
FALSE Chain 1: Gradient evaluation took 0.000169 seconds
FALSE Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 1.69 seconds.
FALSE Chain 1: Adjust your expectations accordingly!
FALSE Chain 1:
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FALSE Chain 1:
FALSE Chain 1: Elapsed Time: 3.574 seconds (Warm-up)
FALSE Chain 1: 2.712 seconds (Sampling)
FALSE Chain 1: 6.286 seconds (Total)
FALSE Chain 1:
FALSE
FALSE SAMPLING FOR MODEL 'dgu' NOW (CHAIN 1).
FALSE Chain 1:
FALSE Chain 1: Gradient evaluation took 0.000153 seconds
FALSE Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 1.53 seconds.
FALSE Chain 1: Adjust your expectations accordingly!
FALSE Chain 1:
FALSE Chain 1:
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FALSE Chain 1:
FALSE Chain 1: Elapsed Time: 4.014 seconds (Warm-up)
FALSE Chain 1: 4.589 seconds (Sampling)
FALSE Chain 1: 8.603 seconds (Total)
FALSE Chain 1:
FALSE
FALSE SAMPLING FOR MODEL 'dgu' NOW (CHAIN 1).
FALSE Chain 1:
FALSE Chain 1: Gradient evaluation took 0.000244 seconds
FALSE Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 2.44 seconds.
FALSE Chain 1: Adjust your expectations accordingly!
FALSE Chain 1:
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FALSE Chain 1:
FALSE Chain 1: Elapsed Time: 4.093 seconds (Warm-up)
FALSE Chain 1: 2.761 seconds (Sampling)
FALSE Chain 1: 6.854 seconds (Total)
FALSE Chain 1:
FALSE
FALSE SAMPLING FOR MODEL 'dgu' NOW (CHAIN 1).
FALSE Chain 1:
FALSE Chain 1: Gradient evaluation took 0.000148 seconds
FALSE Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 1.48 seconds.
FALSE Chain 1: Adjust your expectations accordingly!
FALSE Chain 1:
FALSE Chain 1:
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FALSE Chain 1:
FALSE Chain 1: Elapsed Time: 3.946 seconds (Warm-up)
FALSE Chain 1: 2.807 seconds (Sampling)
FALSE Chain 1: 6.753 seconds (Total)
FALSE Chain 1:
FALSE
FALSE SAMPLING FOR MODEL 'dgu' NOW (CHAIN 1).
FALSE Chain 1:
FALSE Chain 1: Gradient evaluation took 0.000204 seconds
FALSE Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 2.04 seconds.
FALSE Chain 1: Adjust your expectations accordingly!
FALSE Chain 1:
FALSE Chain 1:
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FALSE Chain 1:
FALSE Chain 1: Elapsed Time: 3.436 seconds (Warm-up)
FALSE Chain 1: 2.858 seconds (Sampling)
FALSE Chain 1: 6.294 seconds (Total)
FALSE Chain 1:
FALSE
FALSE SAMPLING FOR MODEL 'dgu' NOW (CHAIN 1).
FALSE Chain 1:
FALSE Chain 1: Gradient evaluation took 0.000152 seconds
FALSE Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 1.52 seconds.
FALSE Chain 1: Adjust your expectations accordingly!
FALSE Chain 1:
FALSE Chain 1:
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FALSE Chain 1:
FALSE Chain 1: Elapsed Time: 4.065 seconds (Warm-up)
FALSE Chain 1: 2.853 seconds (Sampling)
FALSE Chain 1: 6.918 seconds (Total)
FALSE Chain 1:
FALSE
FALSE SAMPLING FOR MODEL 'dgu' NOW (CHAIN 1).
FALSE Chain 1:
FALSE Chain 1: Gradient evaluation took 0.000182 seconds
FALSE Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 1.82 seconds.
FALSE Chain 1: Adjust your expectations accordingly!
FALSE Chain 1:
FALSE Chain 1:
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FALSE Chain 1:
FALSE Chain 1: Elapsed Time: 4.286 seconds (Warm-up)
FALSE Chain 1: 2.781 seconds (Sampling)
FALSE Chain 1: 7.067 seconds (Total)
FALSE Chain 1:
Next, we collected the results (GU and DGU) from each LOO iteration:
L_gu <- do.call(rbind, lapply(X = L, FUN = function(x){return(x$gu)}))
L_dgu <- do.call(rbind, lapply(X = L, FUN = function(x){return(x$dgu)}))
… and plot them:
ggplot(data = L_dgu)+
facet_wrap(facets = ~contrast, ncol = 1)+
geom_hline(yintercept = 0, linetype = "dashed", col = "gray")+
geom_errorbar(aes(x = gene_name, y = es_mean, ymin = es_L,
ymax = es_H, col = contrast, group = loo_id),
width = 0.1, position = position_dodge(width = 0.75))+
geom_point(aes(x = gene_name, y = es_mean, col = contrast,
group = loo_id), size = 1,
position = position_dodge(width = 0.75))+
theme_bw(base_size = 11)+
theme(legend.position = "none")+
ylab(expression(gamma))
ggplot(data = L_dgu)+
facet_wrap(facets = ~contrast, ncol = 1)+
geom_point(aes(x = gene_name, y = pmax, col = contrast,
group = loo_id), size = 1,
position = position_dodge(width = 0.5))+
theme_bw(base_size = 11)+
theme(legend.position = "none")+
ylab(expression(pi))
ggplot(data = L_gu)+
geom_hline(yintercept = 0, linetype = "dashed", col = "gray")+
geom_errorbar(aes(x = gene_name, y = prob_mean, ymin = prob_L,
ymax = prob_H, col = condition,
group = interaction(loo_id, condition)),
width = 0.1, position = position_dodge(width = 1))+
geom_point(aes(x = gene_name, y = prob_mean, col = condition,
group = interaction(loo_id, condition)), size = 1,
position = position_dodge(width = 1))+
theme_bw(base_size = 11)+
theme(legend.position = "top")+
ylab("GU [probability]")+
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.4))
data("d_zibb_4", package = "IgGeneUsage")
knitr::kable(head(d_zibb_4))
individual_id | condition | gene_name | replicate | gene_usage_count |
---|---|---|---|---|
I_1 | C_1 | G_1 | R_1 | 29 |
I_1 | C_1 | G_2 | R_1 | 66 |
I_1 | C_1 | G_3 | R_1 | 285 |
I_1 | C_1 | G_4 | R_1 | 20 |
I_1 | C_1 | G_5 | R_1 | 38 |
I_1 | C_1 | G_6 | R_1 | 709 |
We can also visualize d_zibb_4
with ggplot:
ggplot(data = d_zibb_4)+
geom_point(aes(x = gene_name, y = gene_usage_count, col = condition,
shape = replicate), position = position_dodge(width = 0.8))+
theme_bw(base_size = 11)+
ylab(label = "Gene usage [count]")+
xlab(label = '')+
theme(legend.position = "top")+
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.4))
M <- DGU(ud = d_zibb_4, # input data
mcmc_warmup = 500, # how many MCMC warm-ups per chain (default: 500)
mcmc_steps = 1500, # how many MCMC steps per chain (default: 1,500)
mcmc_chains = 2, # how many MCMC chain to run (default: 4)
mcmc_cores = 1, # how many PC cores to use? (e.g. parallel chains)
hdi_lvl = 0.95, # highest density interval level (de fault: 0.95)
adapt_delta = 0.8, # MCMC target acceptance rate (default: 0.95)
max_treedepth = 10) # tree depth evaluated at each step (default: 12)
FALSE
FALSE SAMPLING FOR MODEL 'dgu_rep' NOW (CHAIN 1).
FALSE Chain 1:
FALSE Chain 1: Gradient evaluation took 0.000568 seconds
FALSE Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 5.68 seconds.
FALSE Chain 1: Adjust your expectations accordingly!
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FALSE Chain 1:
FALSE Chain 1: Elapsed Time: 55.508 seconds (Warm-up)
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FALSE Chain 1: 181.517 seconds (Total)
FALSE Chain 1:
FALSE
FALSE SAMPLING FOR MODEL 'dgu_rep' NOW (CHAIN 2).
FALSE Chain 2:
FALSE Chain 2: Gradient evaluation took 0.000659 seconds
FALSE Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 6.59 seconds.
FALSE Chain 2: Adjust your expectations accordingly!
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FALSE Chain 2:
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FALSE Chain 2:
ggplot(data = M$ppc$ppc_rep)+
facet_wrap(facets = ~individual_id, ncol = 3)+
geom_abline(intercept = 0, slope = 1, linetype = "dashed", col = "darkgray")+
geom_errorbar(aes(x = observed_count, y = ppc_mean_count,
ymin = ppc_L_count, ymax = ppc_H_count), col = "darkgray")+
geom_point(aes(x = observed_count, y = ppc_mean_count), size = 1)+
theme_bw(base_size = 11)+
theme(legend.position = "top")+
xlab(label = "Observed usage [counts]")+
ylab(label = "PPC usage [counts]")
The top panel shows the average gene usage (GU) in different biological conditions. The bottom panels shows the differential gene usage (DGU) between pairs of biological conditions.
g1 <- ggplot(data = M$gu)+
geom_errorbar(aes(x = gene_name, y = prob_mean, ymin = prob_L,
ymax = prob_H, col = condition),
width = 0.1, position = position_dodge(width = 0.4))+
geom_point(aes(x = gene_name, y = prob_mean, col = condition), size = 1,
position = position_dodge(width = 0.4))+
theme_bw(base_size = 11)+
theme(legend.position = "top")+
ylab(label = "GU [probability]")+
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.4))
stats <- M$dgu
stats <- stats[order(abs(stats$es_mean), decreasing = FALSE), ]
stats$gene_fac <- factor(x = stats$gene_name, levels = unique(stats$gene_name))
g2 <- ggplot(data = stats)+
facet_wrap(facets = ~contrast)+
geom_hline(yintercept = 0, linetype = "dashed", col = "gray")+
geom_errorbar(aes(x = pmax, y = es_mean, ymin = es_L, ymax = es_H),
col = "darkgray")+
geom_point(aes(x = pmax, y = es_mean, col = contrast))+
geom_text_repel(data = stats[stats$pmax >= 0.95, ],
aes(x = pmax, y = es_mean, label = gene_fac),
min.segment.length = 0, size = 2.75)+
theme_bw(base_size = 11)+
theme(legend.position = "top")+
xlab(label = expression(pi))+
xlim(c(0, 1))+
ylab(expression(gamma))
(g1/g2)
sessionInfo()
FALSE R Under development (unstable) (2025-03-01 r87860 ucrt)
FALSE Platform: x86_64-w64-mingw32/x64
FALSE Running under: Windows Server 2022 x64 (build 20348)
FALSE
FALSE Matrix products: default
FALSE LAPACK version 3.12.0
FALSE
FALSE locale:
FALSE [1] LC_COLLATE=C
FALSE [2] LC_CTYPE=English_United States.utf8
FALSE [3] LC_MONETARY=English_United States.utf8
FALSE [4] LC_NUMERIC=C
FALSE [5] LC_TIME=English_United States.utf8
FALSE
FALSE time zone: America/New_York
FALSE tzcode source: internal
FALSE
FALSE attached base packages:
FALSE [1] stats graphics grDevices utils datasets methods base
FALSE
FALSE other attached packages:
FALSE [1] patchwork_1.3.0 reshape2_1.4.4 ggrepel_0.9.6
FALSE [4] ggforce_0.4.2 ggplot2_3.5.1 knitr_1.50
FALSE [7] rstan_2.32.7 StanHeaders_2.32.10 IgGeneUsage_1.21.0
FALSE [10] BiocStyle_2.35.0
FALSE
FALSE loaded via a namespace (and not attached):
FALSE [1] tidyselect_1.2.1 dplyr_1.1.4
FALSE [3] farver_2.1.2 loo_2.8.0
FALSE [5] fastmap_1.2.0 tweenr_2.0.3
FALSE [7] digest_0.6.37 lifecycle_1.0.4
FALSE [9] magrittr_2.0.3 compiler_4.5.0
FALSE [11] rlang_1.1.5 sass_0.4.9
FALSE [13] tools_4.5.0 yaml_2.3.10
FALSE [15] S4Arrays_1.7.3 labeling_0.4.3
FALSE [17] pkgbuild_1.4.7 curl_6.2.2
FALSE [19] DelayedArray_0.33.6 plyr_1.8.9
FALSE [21] abind_1.4-8 withr_3.0.2
FALSE [23] purrr_1.0.4 BiocGenerics_0.53.6
FALSE [25] grid_4.5.0 polyclip_1.10-7
FALSE [27] stats4_4.5.0 colorspace_2.1-1
FALSE [29] inline_0.3.21 scales_1.3.0
FALSE [31] MASS_7.3-65 tinytex_0.56
FALSE [33] SummarizedExperiment_1.37.0 cli_3.6.4
FALSE [35] rmarkdown_2.29 crayon_1.5.3
FALSE [37] generics_0.1.3 RcppParallel_5.1.10
FALSE [39] httr_1.4.7 cachem_1.1.0
FALSE [41] stringr_1.5.1 parallel_4.5.0
FALSE [43] BiocManager_1.30.25 XVector_0.47.2
FALSE [45] matrixStats_1.5.0 vctrs_0.6.5
FALSE [47] V8_6.0.2 Matrix_1.7-3
FALSE [49] jsonlite_1.9.1 bookdown_0.42
FALSE [51] IRanges_2.41.3 S4Vectors_0.45.4
FALSE [53] magick_2.8.6 jquerylib_0.1.4
FALSE [55] tidyr_1.3.1 glue_1.8.0
FALSE [57] codetools_0.2-20 stringi_1.8.4
FALSE [59] gtable_0.3.6 GenomeInfoDb_1.43.4
FALSE [61] QuickJSR_1.6.0 GenomicRanges_1.59.1
FALSE [63] UCSC.utils_1.3.1 munsell_0.5.1
FALSE [65] tibble_3.2.1 pillar_1.10.1
FALSE [67] htmltools_0.5.8.1 GenomeInfoDbData_1.2.14
FALSE [69] R6_2.6.1 evaluate_1.0.3
FALSE [71] lattice_0.22-6 Biobase_2.67.0
FALSE [73] bslib_0.9.0 rstantools_2.4.0
FALSE [75] Rcpp_1.0.14 gridExtra_2.3
FALSE [77] SparseArray_1.7.7 xfun_0.51
FALSE [79] MatrixGenerics_1.19.1 pkgconfig_2.0.3