You can install the most recent version of scLANE using:
remotes::install_github("jr-leary7/scLANE")The scLANE package enables users to accurately determine differential
expression of genes over pseudotime or latent time, and to characterize
gene’s dynamics using interpretable model coefficients. scLANE builds
upon the marge modeling
framework(GitHub,
paper), allowing users
to characterize their trajectory’s effects on gene expression using
negative binomial GLMs, GEEs, or GLMMs depending on the experimental
design & biological questions of interest. This modeling framework is an
extension of the Multivariate Adapative Regression Splines
(MARS)
method, which builds nonlinear models out of piecewise linear
components. scLANE is agnostic with respect to the ordering estimation
method used, and can be implemented downstream of any pseudotime or RNA
velocity method.
A quickstart guide on how to use scLANE with simulated data continues
below, and a more detailed vignette showcasing its performance on real
data can be found
here.
Our method relies on a relatively simple test in order to define whether
a given gene is differentially expressed (or “dynamic”) over the
provided trajectory. While the exact structure of the test differs by
model mode, the concept is the same: the spline-based NB GLM / GEE /
GLMM is treated as the alternate model, and a null model is fit using
the corresponding model mode. If the GLM mode is used, then the null
model is simply an intercept-only NB GLM; the GEE mode fits an
intercept-only model with the same working correlation structure as the
alternate model, and if the GLMM mode is used then the null model is an
intercept-only model with random intercepts for each subject. The
alternate hypothesis is that at least one of the estimated coefficients
is significantly different from zero. We predict a given gene to be
dynamic if the adjusted p-value of the test is less than the default
library(dplyr)
library(scLANE)
library(ggplot2)
library(SingleCellExperiment)We read a previously-simulated dataset comprised of cells from 3
subjects exhibiting a homogeneous trajectory structure from the Zenodo
repository. The underlying true
pseudotime values are stored in the colData slot of the
SingleCellExperiment object under the name cell_time_normed.
sim_data <- readRDS(url("https://zenodo.org/records/8433077/files/scLANE_sim_data.Rds"))The PCA embedding shows us a pretty simple trajectory that’s strongly correlated with the first principal component.
data.frame(sim_data@int_colData$reducedDims@listData$PCA[, 1:2]) %>%
mutate(pseudotime = sim_data$cell_time_normed) %>%
ggplot(aes(x = PC1, y = PC2, color = pseudotime)) +
geom_point(size = 2, alpha = 0.75, stroke = 0) +
scale_color_gradientn(colors = viridisLite::plasma(n = 20)) +
labs(x = "PC 1", y = "PC 2", color = "Pseudotime") +
theme_scLANE(umap = TRUE)
We also see that the data are not clustered by subject, which indicates that gene dynamics are mostly homogeneous across subjects.
data.frame(sim_data@int_colData$reducedDims@listData$PCA[, 1:2]) %>%
mutate(subject = sim_data$subject) %>%
ggplot(aes(x = PC1, y = PC2, color = subject)) +
geom_point(size = 2, alpha = 0.75, stroke = 0) +
labs(x = "PC 1", y = "PC 2", color = "Subject ID") +
theme_scLANE(umap = TRUE)
Since we have multi-subject data, we can use any of the three model modes to run our DE testing. We’ll start with the simplest model, the GLM, then work our way through the other options in order of increasing complexity. We first prepare our inputs - a dataframe containing our pseudotime / latent time cellular ordering, a set of genes to build models for, and a vector of per-cell size factors to be used as offsets during estimation. In reality, it’s usually unnecessary to fit a model for every single gene in a dataset, as trajectories are usually estimated using a subset of the entire set of genes (usually a few thousand most highly variable genes). For the purpose of demonstration, we’ll select 50 genes each from the dynamic and non-dynamic populations.
Note: In this case we’re working with a single pseudotime lineage,
though in real datasets several lineages often exist; in order to fit
models for a subset of lineages simply remove the corresponding columns
from the cell ordering dataframe passed as input to testDynamic().
set.seed(312)
gene_sample <- c(sample(rownames(sim_data)[rowData(sim_data)$geneStatus_overall == "Dynamic"], size = 50),
sample(rownames(sim_data)[rowData(sim_data)$geneStatus_overall == "NotDynamic"], size = 50))
pt_df <- data.frame(PT = sim_data$cell_time_normed)
cell_offset <- createCellOffset(sim_data)Running testDynamic() provides us with a nested list containing model
output & DE test results for each gene over each pseudotime / latent
time lineage. In this case, since we have a true cell ordering we only
have one lineage. Parallel processing is turned on by default, and we
use 6 cores here to speed up runtime.
scLANE_models_glm <- testDynamic(sim_data,
pt = pt_df,
genes = gene_sample,
size.factor.offset = cell_offset,
n.cores = 6L,
verbose = FALSE)
#> Registered S3 method overwritten by 'bit':
#> method from
#> print.ri gamlss
#> scLANE testing in GLM mode completed for 100 genes across 1 lineage in 16.048 secsAfter the function finishes running, we use getResultsDE() to generate
a sorted table of DE test results, with one row for each gene & lineage.
The GLM mode uses a simple likelihood ratio test to compare the null &
alternate models, with the test statistic assumed to be asymptotically
Chi-squared distributed.
scLANE_res_glm <- getResultsDE(scLANE_models_glm)
select(scLANE_res_glm, Gene, Lineage, Test_Stat, P_Val, P_Val_Adj, Gene_Dynamic_Overall) %>%
slice_sample(n = 5) %>%
knitr::kable(format = "pipe",
digits = 3,
col.names = c("Gene", "Lineage", "LRT stat.", "P-value", "Adj. p-value", "Predicted dynamic status"))| Gene | Lineage | LRT stat. | P-value | Adj. p-value | Predicted dynamic status |
|---|---|---|---|---|---|
| MFSD2B | A | 217.016 | 0.000 | 0.000 | 1 |
| LGR4 | A | 6.685 | 0.035 | 1.000 | 0 |
| UAP1L1 | A | 9.882 | 0.007 | 0.365 | 0 |
| TMCO3 | A | 167.764 | 0.000 | 0.000 | 1 |
| MRTO4 | A | 4.771 | 0.092 | 1.000 | 0 |
The function call is essentially the same when using the GLM mode, with
the exception of needing to provide a sorted vector of subject IDs & a
desired correlation structure. We also need to flip the is.gee flag in
order to indicate that we’d like to fit estimating equations models
(instead of mixed models). Since fitting GEEs is more computationally
complex than fitting GLMs, DE testing with the GEE mode takes a bit
longer. Using more cores and / or running the tests on an HPC cluster
speeds things up considerably.
scLANE_models_gee <- testDynamic(sim_data,
pt = pt_df,
genes = gene_sample,
size.factor.offset = cell_offset,
is.gee = TRUE,
id.vec = sim_data$subject,
cor.structure = "ar1",
n.cores = 6L,
verbose = FALSE)
#> scLANE testing in GEE mode completed for 100 genes across 1 lineage in 44.965 secsWe again generate the table of DE test results. The variance of the estimated coefficients is determined using the sandwich estimator, and a Wald test is used to compare the null & alternate models.
scLANE_res_gee <- getResultsDE(scLANE_models_gee)
select(scLANE_res_gee, Gene, Lineage, Test_Stat, P_Val, P_Val_Adj, Gene_Dynamic_Overall) %>%
slice_sample(n = 5) %>%
knitr::kable("pipe",
digits = 3,
col.names = c("Gene", "Lineage", "Wald stat.", "P-value", "Adj. p-value", "Predicted dynamic status"))| Gene | Lineage | Wald stat. | P-value | Adj. p-value | Predicted dynamic status |
|---|---|---|---|---|---|
| BAD | A | 159682.044 | 0 | 0 | 1 |
| RPL29 | A | 32.647 | 0 | 0 | 1 |
| HOXC8 | A | NA | NA | NA | 0 |
| DDX41 | A | 4216.586 | 0 | 0 | 1 |
| PFDN2 | A | 2362.431 | 0 | 0 | 1 |
We re-run the DE tests a final time using the GLMM mode. This is the
most complex model architecture we support, and is the trickiest to
interpret. We recommend using it when you’re most interested in how a
trajectory differs between subjects e.g., if the subjects belong to
groups like Treatment & Control, and you expect the Treatment group to
experience a different progression through the biological process.
Executing the function with the GLMM mode differs only in that we switch
the is.glmm flag to TRUE and no longer need to specify a working
correlation structure.
scLANE_models_glmm <- testDynamic(sim_data,
pt = pt_df,
genes = gene_sample,
size.factor.offset = cell_offset,
n.potential.basis.fns = 3,
is.glmm = TRUE,
id.vec = sim_data$subject,
n.cores = 6L,
verbose = FALSE)
#> scLANE testing in GLMM mode completed for 100 genes across 1 lineage in 2.21 minsNote: The GLMM mode is still under development, as we are working on further reducing runtime and increasing the odds of the underlying optimization process converging successfully. As such, updates will be frequent and functionality / results may shift slightly.
Like the GLM mode, the GLMM mode uses a likelihood ratio test to compare the null & alternate models.
scLANE_res_glmm <- getResultsDE(scLANE_models_glmm)
select(scLANE_res_glmm, Gene, Lineage, Test_Stat, P_Val, P_Val_Adj, Gene_Dynamic_Overall) %>%
slice_sample(n = 5) %>%
knitr::kable("pipe",
digits = 3,
col.names = c("Gene", "Lineage", "LRT stat.", "P-value", "Adj. p-value", "Predicted dynamic status"))| Gene | Lineage | LRT stat. | P-value | Adj. p-value | Predicted dynamic status |
|---|---|---|---|---|---|
| VDAC1 | A | 151.417 | 0 | 0 | 1 |
| KLHDC10 | A | 122.491 | 0 | 0 | 1 |
| ZNF575 | A | NA | NA | NA | 0 |
| BCAT1 | A | NA | NA | NA | 0 |
| TSPAN1 | A | 78.597 | 0 | 0 | 1 |
We can use the plotModels() to visually compare different types of
models. It takes as input the results from testDynamic(), as well as a
few specifications for which models & lineages should be plotted. While
more complex visualizations can be created from our model output, this
function gives us a good first glance at which models fit the underlying
trend the best. Here we show the output generated using the GLM mode,
split by model type. The intercept-only model shows the null hypothesis
against which the scLANE model is compared using the likelihood ratio
test and the GLM displays the inadequacy of monotonic modeling
architectures for nonlinear dynamics. A GAM shows essentially the same
trend as the scLANE model, though the fitted trend from scLANE is
more interpretable.
plotModels(scLANE_models_glm,
gene = "MPG",
pt = pt_df,
expr.mat = sim_data,
size.factor.offset = cell_offset,
plot.null = TRUE,
plot.glm = TRUE,
plot.gam = TRUE,
plot.scLANE = TRUE)
When plotting the models generated using the GLMM mode, we split by lineage & color the points by subject ID instead of by lineage. The gene in question highlights the utility of the scLANE model, since the gene dynamics differ significantly by subject.
plotModels(scLANE_models_glmm,
gene = "FLOT2",
pt = pt_df,
expr.mat = sim_data,
size.factor.offset = cell_offset,
id.vec = sim_data$subject,
is.glmm = TRUE,
plot.glm = TRUE,
plot.gam = TRUE,
plot.scLANE = TRUE)
A key feature of scLANE is the ability to obtain a quantitative,
interpretable coefficient for the effect of pseudotime on gene
expression. This functionality is currently available for the GLM & GEE
frameworks, and each coefficient carries the interpretation of a
generalized linear
model.
scLANE_models_glm[["JARID2"]]$Lineage_A$Gene_Dynamics %>%
knitr::kable("pipe",
digits = 2,
col.names = c("Gene", "Lineage", "Breakpoint", "First Slope", "Second Slope", "First Trend", "Second Trend"))| Gene | Lineage | Breakpoint | First Slope | Second Slope | First Trend | Second Trend |
|---|---|---|---|---|---|---|
| JARID2 | A | 0.11 | -34.03 | 3.99 | -1 | 1 |
Coefficients can also be plotted like so:
plotModelCoefs(scLANE_models_glm,
gene = "JARID2",
pt = pt_df,
expr.mat = sim_data,
size.factor.offset = cell_offset)
Lastly, we can pull the locations in pseudotime of all the knots fitted
by scLANE. Visualizing this distribution gives us some idea of where
transcriptional switches are occurring in the set of genes classified as
dynamic.
dyn_genes <- filter(scLANE_res_glm, Gene_Dynamic_Overall == 1) %>%
pull(Gene)
knot_dist <- getKnotDist(scLANE_models_glm, dyn.genes = dyn_genes)
ggplot(knot_dist, aes(x = knot)) +
geom_histogram(aes(y = after_stat(density)),
color = "black",
fill = "white",
linewidth = 0.5) +
geom_density(color = "forestgreen",
fill = "forestgreen",
alpha = 0.5,
linewidth = 0.75) +
labs(x = "Knot Location", y = "Density") +
theme_scLANE()
We can extract matrix of the fitted values for each dynamic gene using
the smoothedCountsMatrix() function.
smoothed_dynamics <- smoothedCountsMatrix(scLANE_models_glm,
size.factor.offset = cell_offset,
pt = pt_df,
genes = dyn_genes)The smoothed dynamics can then be used to generate expression cascade heatmaps, cluster genes, etc. For more information on downstream analysis of gene dynamics, see the corresponding vignette.
In general, starting with the GLM mode is probably your best bet unless you have a strong prior belief that expression trends will differ significantly between subjects. If that is the case, you should use the GEE mode if you’re interested in population-level estimates, but are worried about wrongly predicting differential expression when differences in expression are actually caused by inter-subject variation. If you’re interested in generating subject-specific estimates then the GLMM mode should be used; take care when interpreting the fixed vs. random effects though, and consult a biostatistician if necessary.
If you have a large dataset (10,000+ cells), you should start with the GLM mode, since standard error estimates don’t differ much between modeling methods given high enough n. In addition, running the tests on an HPC cluster with 12+ CPUs and 64+ GB of RAM will help your computations to complete swiftly. Datasets with smaller numbers of cells or fewer genes of interest may be easily analyzed in an R session on a local machine.
This package is developed & maintained by Jack Leary. Feel free to reach out by opening an issue or by email ([email protected]) if more detailed assistance is needed.
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