Cellular omics such as single-cell genomics, proteomics, and microbiomics allow the characterization of tissue and microbial community composition, which can be compared between conditions to identify biological drivers. This strategy has been critical to unveiling markers of disease progression in conditions such as cancer and pathogen infections.
For cellular omic data, no method for differential variability analysis exists, and methods for differential composition analysis only take a few fundamental data properties into account. Here we introduce sccomp, a generalised method for differential composition and variability analyses capable of jointly modelling data count distribution, compositionality, group-specific variability, and proportion mean-variability association, while being robust to outliers.
sccomp is an extensive analysis framework that allows realistic data simulation and cross-study knowledge transfer. We demonstrate that mean-variability association is ubiquitous across technologies, highlighting the inadequacy of the very popular Dirichlet-multinomial modeling and providing essential principles for differential variability analysis.
Comparison with other methodsMangiola, Stefano, Alexandra J. Roth-Schulze, Marie Trussart, Enrique Zozaya-Valdés, Mengyao Ma, Zijie Gao, Alan F. Rubin, Terence P. Speed, Heejung Shim, and Anthony T. Papenfuss. 2023. “Sccomp: Robust Differential Composition and Variability Analysis for Single-Cell Data.” Proceedings of the National Academy of Sciences of the United States of America 120 (33): e2203828120. https://doi.org/10.1073/pnas.2203828120 PNAS - sccomp: Robust differential composition and variability analysis for single-cell data
sccomp tests differences in cell type proportions from single-cell data. It is robust against outliers, it models continuous and discrete factors, and capable of random-effect/intercept modelling.
sccomp is based on cmdstanr (version 0.9.0 or higher) which provides the latest version of cmdstan the Bayesian modelling tool. cmdstanr is not on CRAN, so we need to have 3 simple step process (that will be prompted to the user is forgot).
sccompcmdstanr (>= 0.9.0)cmdstanr call to cmdstan installationBioconductor
if (!requireNamespace("BiocManager")) install.packages("BiocManager")
# Step 1
BiocManager::install("sccomp")
# Step 2
install.packages("cmdstanr", repos = c("https://stan-dev.r-universe.dev/", getOption("repos")))
# Note: cmdstanr version 0.9.0 or higher is required for sum_to_zero_vector support
# Step 3
cmdstanr::check_cmdstan_toolchain(fix = TRUE) # Just checking system setting
cmdstanr::install_cmdstan()
Github
# Step 1
devtools::install_github("MangiolaLaboratory/sccomp")
# Step 2
install.packages("cmdstanr", repos = c("https://stan-dev.r-universe.dev/", getOption("repos")))
# Note: cmdstanr version 0.9.0 or higher is required for sum_to_zero_vector support
# Step 3
cmdstanr::check_cmdstan_toolchain(fix = TRUE) # Just checking system setting
cmdstanr::install_cmdstan() Function Description sccomp_estimate Fit the model onto the data, and estimate the coefficients sccomp_remove_outliers Identify outliers probabilistically based on the model fit, and exclude them from the estimation sccomp_test Calculate the probability that the coefficients are outside the H0 interval (i.e. test_composition_above_logit_fold_change) sccomp_replicate Simulate data from the model, or part of the model sccomp_predict Predicts proportions, based on the model, or part of the model sccomp_remove_unwanted_effects Removes the variability for unwanted factors plot Plots summary plots to assess significance
library(dplyr)
library(sccomp)
library(ggplot2)
library(forcats)
library(tidyr)
data("seurat_obj")
data("sce_obj")
data("counts_obj")
sccomp can model changes in composition and variability. By default, the formula for variability is either ~1, which assumes that the cell-group variability is independent of any covariate or ~ factor_of_interest, which assumes that the model is dependent on the factor of interest only. The variability model must be a subset of the model for composition.
Of the output table, the estimate columns start with the prefix c_ indicate composition, or with v_ indicate variability (when formula_variability is set).
sccomp_result =
sce_obj |>
sccomp_estimate(
formula_composition = ~ type,
sample = "sample",
cell_group = "cell_group",
cores = 1
) |>
sccomp_test()
sccomp_result =
counts_obj |>
sccomp_estimate(
formula_composition = ~ type,
sample = "sample",
cell_group = "cell_group",
abundance = "count",
cores = 1, verbose = FALSE
) |>
sccomp_test()
Here you see the results of the fit, the effects of the factor on composition and variability. You also can see the uncertainty around those effects.
The output is a tibble containing the Following columns
cell_group - The cell groups being tested.parameter - The parameter being estimated from the design matrix described by the input formula_composition and formula_variability.factor - The covariate factor in the formula, if applicable (e.g., not present for Intercept or contrasts).c_lower - Lower (2.5%) quantile of the posterior distribution for a composition (c) parameter.c_effect - Mean of the posterior distribution for a composition (c) parameter.c_upper - Upper (97.5%) quantile of the posterior distribution for a composition (c) parameter.c_pH0 - Probability of the null hypothesis (no difference) for a composition (c). This is not a p-value.c_FDR - False-discovery rate of the null hypothesis for a composition (c).v_lower - Lower (2.5%) quantile of the posterior distribution for a variability (v) parameter.v_effect - Mean of the posterior distribution for a variability (v) parameter.v_upper - Upper (97.5%) quantile of the posterior distribution for a variability (v) parameter.v_pH0 - Probability of the null hypothesis for a variability (v).v_FDR - False-discovery rate of the null hypothesis for a variability (v).## sccomp model
## ============
##
## Model specifications:
## Family: multi_beta_binomial
## Composition formula: ~type
## Variability formula: ~1
## Inference method: pathfinder
##
## Data: Samples: 20 Cell groups: 36
##
## Column prefixes: c_ -> composition parameters v_ -> variability parameters
##
## Convergence diagnostics:
## For each parameter, n_eff is the effective sample size and R_k_hat is the potential
## scale reduction factor on split chains (at convergence, R_k_hat = 1).
##
## # A tibble: 72 × 19
## cell_group parameter factor c_lower c_effect c_upper c_pH0 c_FDR c_rhat
## <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 B1 (Intercept) <NA> 0.904 1.18 1.44 0 0 1.00
## 2 B1 typecancer type -1.07 -0.666 -0.272 0.00175 3.50e-4 1.00
## 3 B2 (Intercept) <NA> 0.432 0.753 1.07 0 0 1.00
## 4 B2 typecancer type -1.18 -0.723 -0.263 0.00200 5.00e-4 1.000
## 5 B3 (Intercept) <NA> -0.659 -0.348 -0.0346 0.0597 7.38e-3 1.00
## 6 B3 typecancer type -0.774 -0.315 0.135 0.18 5.37e-2 1.000
## 7 BM (Intercept) <NA> -1.30 -0.988 -0.678 0 0 1.00
## 8 BM typecancer type -0.734 -0.304 0.144 0.171 4.80e-2 1.00
## 9 CD4 1 (Intercept) <NA> 0.155 0.340 0.519 0.00525 4.13e-4 1.00
## 10 CD4 1 typecancer type -0.0769 0.170 0.412 0.288 6.92e-2 1.00
## # ℹ 62 more rows
## # ℹ 10 more variables: c_ess_bulk <dbl>, c_ess_tail <dbl>, v_lower <dbl>,
## # v_effect <dbl>, v_upper <dbl>, v_pH0 <dbl>, v_FDR <dbl>, v_rhat <dbl>,
## # v_ess_bulk <dbl>, v_ess_tail <dbl>
sccomp can identify outliers probabilistically and exclude them from the estimation.
sccomp_result =
counts_obj |>
sccomp_estimate(
formula_composition = ~ type,
sample = "sample",
cell_group = "cell_group",
abundance = "count",
cores = 1, verbose = FALSE
) |>
# max_sampling_iterations is used her to not exceed the maximum file size for GitHub action of 100Mb
sccomp_remove_outliers(cores = 1, verbose = FALSE, max_sampling_iterations = 2000) |> # Optional
sccomp_test()
## Running standalone generated quantities after 1 MCMC chain, with 1 thread(s) per chain...
##
## Chain 1 finished in 0.0 seconds.
## Running standalone generated quantities after 1 MCMC chain, with 1 thread(s) per chain...
##
## Chain 1 finished in 0.0 seconds.
A plot of group proportions, faceted by groups. The blue boxplots represent the posterior predictive check. If the model is descriptively adequate for the data, the blue boxplots should roughly overlay the black boxplots, which represent the observed data. The outliers are coloured in red. A boxplot will be returned for every (discrete) covariate present in formula_composition. The colour coding represents the significant associations for composition and/or variability.
sccomp_result |> sccomp_boxplot(factor = "type")
## sccomp says: When visualising proportions, especially for complex models, consider setting `remove_unwanted_effects=TRUE`. This will adjust the proportions, preserving only the observed effect.
## Loading model from cache...
## Running standalone generated quantities after 1 MCMC chain, with 1 thread(s) per chain...
##
## Chain 1 finished in 0.0 seconds.
## Joining with `by = join_by(cell_group, sample)`
## Joining with `by = join_by(cell_group, type)`
## Warning in stat_summary(aes(!!as.symbol(factor_of_interest), (generated_proportions)), : Ignoring unknown parameters: `outlier.shape`, `outlier.colour`, and
## `outlier.size`
You can plot proportions adjusted for unwanted effects. This is helpful especially for complex models, where multiple factors can significantly impact the proportions.
sccomp_result |> sccomp_boxplot(factor = "type", remove_unwanted_effects = TRUE)
## sccomp says: calculating residuals
## Loading model from cache...
## Running standalone generated quantities after 1 MCMC chain, with 1 thread(s) per chain...
##
## Chain 1 finished in 0.0 seconds.
## sccomp says: regressing out unwanted factors
## Loading model from cache...
## Running standalone generated quantities after 1 MCMC chain, with 1 thread(s) per chain...
##
## Chain 1 finished in 0.0 seconds.
## Loading model from cache...
## Running standalone generated quantities after 1 MCMC chain, with 1 thread(s) per chain...
##
## Chain 1 finished in 0.0 seconds.
## Joining with `by = join_by(cell_group, sample)`
## Joining with `by = join_by(cell_group, type)`
## Warning in stat_summary(aes(!!as.symbol(factor_of_interest), (generated_proportions)), : Ignoring unknown parameters: `outlier.shape`, `outlier.colour`, and
## `outlier.size`
A plot of estimates of differential composition (c_) on the x-axis and differential variability (v_) on the y-axis. The error bars represent 95% credible intervals. The dashed lines represent the minimal effect that the hypothesis test is based on. An effect is labelled as significant if it exceeds the minimal effect according to the 95% credible interval. Facets represent the covariates in the model.
sccomp_result |> plot_1D_intervals()
## Warning: annotation$theme is not a valid theme.
## Please use `theme()` to construct themes.
We can plot the relationship between abundance and variability. As we can see below, they are positively correlated. sccomp models this relationship to obtain a shrinkage effect on the estimates of both the abundance and the variability. This shrinkage is adaptive as it is modelled jointly, thanks to Bayesian inference.
sccomp_result |> plot_2D_intervals()
## Warning: annotation$theme is not a valid theme.
## Please use `theme()` to construct themes.
You can produce the series of plots calling the plot method.
Note: If counts are available, we strongly discourage the use of proportions, as an important source of uncertainty (i.e., for rare groups/cell types) is not modeled.
The use of proportions is better suited for modelling deconvolution results (e.g., of bulk RNA data), in which case counts are not available.
Proportions should be greater than 0. Assuming that zeros derive from a precision threshold (e.g., deconvolution), zeros are converted to the smallest non-zero value.
sccomp_result =
counts_obj |>
sccomp_estimate(
formula_composition = ~ type,
sample = "sample",
cell_group = "cell_group",
abundance = "proportion",
cores = 1, verbose = FALSE
) |>
sccomp_test()
sccomp is able to fit arbitrary complex models. In this example we have a continuous and binary covariate.
res =
seurat_obj |>
sccomp_estimate(
formula_composition = ~ type + continuous_covariate,
sample = "sample",
cell_group = "cell_group",
cores = 1, verbose=FALSE
)
## Loading required namespace: SeuratObject
## sccomp says: count column is an integer. The sum-constrained beta binomial model will be used
## sccomp says: estimation
## sccomp says: the composition design matrix has columns: (Intercept), typehealthy, continuous_covariate
## sccomp says: the variability design matrix has columns: (Intercept)
## Loading model from cache...
## sccomp says: to do hypothesis testing run `sccomp_test()`,
## the `test_composition_above_logit_fold_change` = 0.1 equates to a change of ~10%, and
## 0.7 equates to ~100% increase, if the baseline is ~0.1 proportion.
## Use `sccomp_proportional_fold_change` to convert c_effect (linear) to proportion difference (non-linear).
## sccomp model
## ============
##
## Model specifications:
## Family: multi_beta_binomial
## Composition formula: ~type + continuous_covariate
## Variability formula: ~1
## Inference method: pathfinder
##
## Data: Samples: 20 Cell groups: 30
##
## Column prefixes: c_ -> composition parameters v_ -> variability parameters
##
## Convergence diagnostics:
## For each parameter, n_eff is the effective sample size and R_k_hat is the potential
## scale reduction factor on split chains (at convergence, R_k_hat = 1).
##
## # A tibble: 90 × 15
## cell_group parameter factor c_lower c_effect c_upper c_rhat c_ess_bulk
## <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 B immature (Interce… <NA> 0.379 0.758 1.12 1.00 4213.
## 2 B immature typeheal… type 0.847 1.36 1.86 1.000 4153.
## 3 B immature continuo… conti… -0.236 0.0547 0.345 1.00 3737.
## 4 B mem (Interce… <NA> -1.24 -0.804 -0.357 1.00 3904.
## 5 B mem typeheal… type 1.06 1.66 2.27 1.00 3900.
## 6 B mem continuo… conti… -0.228 0.0899 0.421 1.00 4116.
## 7 CD4 cm S100A4 (Interce… <NA> 1.19 1.50 1.82 1.00 4184.
## 8 CD4 cm S100A4 typeheal… type 0.685 1.12 1.56 1.000 4076.
## 9 CD4 cm S100A4 continuo… conti… -0.0726 0.189 0.445 1.000 3759.
## 10 CD4 cm high cyto… (Interce… <NA> -0.941 -0.460 0.0213 1.000 4035.
## # ℹ 80 more rows
## # ℹ 7 more variables: c_ess_tail <dbl>, v_lower <dbl>, v_effect <dbl>,
## # v_upper <dbl>, v_rhat <dbl>, v_ess_bulk <dbl>, v_ess_tail <dbl>
sccomp supports multilevel modeling by allowing the inclusion of random effects in the compositional and variability formulas. This is particularly useful when your data has hierarchical or grouped structures, such as measurements nested within subjects, batches, or experimental units. By incorporating random effects, sccomp can account for variability at different levels of your data, improving model fit and inference accuracy.
In this example, we demonstrate how to fit a random intercept model using sccomp. We’ll model the cell-type proportions with both fixed effects (e.g., treatment) and random effects (e.g., subject-specific variability).
Here is the input data
seurat_obj[[]] |> as_tibble()
## # A tibble: 106,297 × 9
## cell_group nCount_RNA nFeature_RNA group__ group__wrong sample type group2__
## <chr> <dbl> <int> <chr> <chr> <chr> <chr> <chr>
## 1 CD4 naive 0 0 GROUP1 1 SI-GA… canc… GROUP21
## 2 Mono clas… 0 0 GROUP1 1 SI-GA… canc… GROUP21
## 3 CD4 cm S1… 0 0 GROUP1 1 SI-GA… canc… GROUP21
## 4 B immature 0 0 GROUP1 1 SI-GA… canc… GROUP21
## 5 CD8 naive 0 0 GROUP1 1 SI-GA… canc… GROUP21
## 6 CD4 naive 0 0 GROUP1 1 SI-GA… canc… GROUP21
## 7 Mono clas… 0 0 GROUP1 1 SI-GA… canc… GROUP21
## 8 CD4 cm S1… 0 0 GROUP1 1 SI-GA… canc… GROUP21
## 9 CD4 cm hi… 0 0 GROUP1 1 SI-GA… canc… GROUP21
## 10 B immature 0 0 GROUP1 1 SI-GA… canc… GROUP21
## # ℹ 106,287 more rows
## # ℹ 1 more variable: continuous_covariate <dbl>
res =
seurat_obj |>
sccomp_estimate(
formula_composition = ~ type + (1 | group__),
sample = "sample",
cell_group = "cell_group",
bimodal_mean_variability_association = TRUE,
cores = 1, verbose = FALSE
)
## sccomp says: count column is an integer. The sum-constrained beta binomial model will be used
## sccomp says: estimation
## sccomp says: the composition design matrix has columns: (Intercept), typehealthy
## sccomp says: the variability design matrix has columns: (Intercept)
## Loading model from cache...
## sccomp says: to do hypothesis testing run `sccomp_test()`,
## the `test_composition_above_logit_fold_change` = 0.1 equates to a change of ~10%, and
## 0.7 equates to ~100% increase, if the baseline is ~0.1 proportion.
## Use `sccomp_proportional_fold_change` to convert c_effect (linear) to proportion difference (non-linear).
## sccomp model
## ============
##
## Model specifications:
## Family: multi_beta_binomial
## Composition formula: ~type + (1 | group__)
## Variability formula: ~1
## Inference method: pathfinder
##
## Data: Samples: 20 Cell groups: 30
##
## Column prefixes: c_ -> composition parameters v_ -> variability parameters
##
## Convergence diagnostics:
## For each parameter, n_eff is the effective sample size and R_k_hat is the potential
## scale reduction factor on split chains (at convergence, R_k_hat = 1).
##
## # A tibble: 180 × 15
## cell_group parameter factor c_lower c_effect c_upper c_rhat c_ess_bulk
## <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 B immature (Intercept) <NA> 0.510 0.827 1.17 1.00 128.
## 2 B immature typehealthy type 0.585 1.07 1.49 1.01 96.9
## 3 B immature (Intercept)___G… <NA> -0.356 0.125 0.711 1.01 95.0
## 4 B immature (Intercept)___G… <NA> -0.0298 0.301 0.835 1.00 121.
## 5 B immature (Intercept)___G… <NA> -0.0678 0.243 0.656 1.00 187.
## 6 B immature (Intercept)___G… <NA> -0.797 -0.327 0.00890 1.02 165.
## 7 B mem (Intercept) <NA> -0.741 -0.327 0.100 1.00 115.
## 8 B mem typehealthy type 0.210 0.982 1.54 1.01 85.7
## 9 B mem (Intercept)___G… <NA> -0.400 0.0851 0.752 1.00 113.
## 10 B mem (Intercept)___G… <NA> -0.0228 0.341 0.953 1.00 143.
## # ℹ 170 more rows
## # ℹ 7 more variables: c_ess_tail <dbl>, v_lower <dbl>, v_effect <dbl>,
## # v_upper <dbl>, v_rhat <dbl>, v_ess_bulk <dbl>, v_ess_tail <dbl>
sccomp can model random slopes. We providean example below.
res =
seurat_obj |>
sccomp_estimate(
formula_composition = ~ type + (type | group__),
sample = "sample",
cell_group = "cell_group",
bimodal_mean_variability_association = TRUE,
cores = 1, verbose = FALSE
)
## sccomp says: count column is an integer. The sum-constrained beta binomial model will be used
## sccomp says: estimation
## sccomp says: the composition design matrix has columns: (Intercept), typehealthy
## sccomp says: the variability design matrix has columns: (Intercept)
## Loading model from cache...
## sccomp says: to do hypothesis testing run `sccomp_test()`,
## the `test_composition_above_logit_fold_change` = 0.1 equates to a change of ~10%, and
## 0.7 equates to ~100% increase, if the baseline is ~0.1 proportion.
## Use `sccomp_proportional_fold_change` to convert c_effect (linear) to proportion difference (non-linear).
## sccomp model
## ============
##
## Model specifications:
## Family: multi_beta_binomial
## Composition formula: ~type + (type | group__)
## Variability formula: ~1
## Inference method: pathfinder
##
## Data: Samples: 20 Cell groups: 30
##
## Column prefixes: c_ -> composition parameters v_ -> variability parameters
##
## Convergence diagnostics:
## For each parameter, n_eff is the effective sample size and R_k_hat is the potential
## scale reduction factor on split chains (at convergence, R_k_hat = 1).
##
## # A tibble: 240 × 15
## cell_group parameter factor c_lower c_effect c_upper c_rhat c_ess_bulk
## <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 B immature (Intercept) <NA> 0.457 0.861 1.33 1.00 96.4
## 2 B immature typehealthy type 0.524 1.00 1.52 1.01 96.8
## 3 B immature (Intercept)___G… <NA> -0.302 0.0696 0.475 1.01 88.4
## 4 B immature typehealthy___G… <NA> -0.234 0.0549 0.437 1.01 115.
## 5 B immature (Intercept)___G… <NA> -0.106 0.154 0.523 1.00 134.
## 6 B immature typehealthy___G… <NA> -0.103 0.140 0.535 1.03 101.
## 7 B immature (Intercept)___G… <NA> -0.0764 0.176 0.579 1.01 98.2
## 8 B immature (Intercept)___G… <NA> -0.661 -0.233 0.0545 1.02 65.0
## 9 B mem (Intercept) <NA> -0.827 -0.404 -0.0354 1.00 209.
## 10 B mem typehealthy type 0.429 1.01 1.64 1.02 59.1
## # ℹ 230 more rows
## # ℹ 7 more variables: c_ess_tail <dbl>, v_lower <dbl>, v_effect <dbl>,
## # v_upper <dbl>, v_rhat <dbl>, v_ess_bulk <dbl>, v_ess_tail <dbl>
If you have a more complex hierarchy, such as measurements nested within subjects and subjects nested within batches, you can include multiple grouping variables. Here group2__ is nested within group__.
res =
seurat_obj |>
sccomp_estimate(
formula_composition = ~ type + (type | group__) + (1 | group2__),
sample = "sample",
cell_group = "cell_group",
bimodal_mean_variability_association = TRUE,
cores = 1, verbose = FALSE
)
## sccomp says: count column is an integer. The sum-constrained beta binomial model will be used
## sccomp says: estimation
## sccomp says: the composition design matrix has columns: (Intercept), typehealthy
## sccomp says: the variability design matrix has columns: (Intercept)
## Loading model from cache...
## sccomp says: to do hypothesis testing run `sccomp_test()`,
## the `test_composition_above_logit_fold_change` = 0.1 equates to a change of ~10%, and
## 0.7 equates to ~100% increase, if the baseline is ~0.1 proportion.
## Use `sccomp_proportional_fold_change` to convert c_effect (linear) to proportion difference (non-linear).
## sccomp model
## ============
##
## Model specifications:
## Family: multi_beta_binomial
## Composition formula: ~type + (type | group__) + (1 | group2__)
## Variability formula: ~1
## Inference method: pathfinder
##
## Data: Samples: 20 Cell groups: 30
##
## Column prefixes: c_ -> composition parameters v_ -> variability parameters
##
## Convergence diagnostics:
## For each parameter, n_eff is the effective sample size and R_k_hat is the potential
## scale reduction factor on split chains (at convergence, R_k_hat = 1).
##
## # A tibble: 300 × 15
## cell_group parameter factor c_lower c_effect c_upper c_rhat c_ess_bulk
## <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 B immature (Intercept) <NA> 0.463 0.814 1.39 1.00 55.5
## 2 B immature typehealthy type 0.566 1.03 1.52 1.02 74.3
## 3 B immature (Intercept)___G… <NA> -0.261 0.0980 0.515 1.04 87.0
## 4 B immature typehealthy___G… <NA> -0.214 0.0994 0.554 1.04 59.3
## 5 B immature (Intercept)___G… <NA> -0.189 0.0832 0.426 1.02 113.
## 6 B immature typehealthy___G… <NA> -0.147 0.0902 0.385 1.00 77.6
## 7 B immature (Intercept)___G… <NA> -0.0822 0.178 0.584 1.01 53.1
## 8 B immature (Intercept)___G… <NA> -0.786 -0.273 0.0332 1.02 40.9
## 9 B immature (Intercept)___G… <NA> -0.366 -0.0550 0.260 1.02 83.5
## 10 B immature (Intercept)___G… <NA> -0.0169 0.197 0.621 1.00 38.3
## # ℹ 290 more rows
## # ℹ 7 more variables: c_ess_tail <dbl>, v_lower <dbl>, v_effect <dbl>,
## # v_upper <dbl>, v_rhat <dbl>, v_ess_bulk <dbl>, v_ess_tail <dbl>
The estimated effects are expressed in the unconstrained space of the parameters, similar to differential expression analysis that expresses changes in terms of log fold change. However, for differences in proportion, logit fold change must be used, which is harder to interpret and understand.
Therefore, we provide a more intuitive proportional fold change that can be more easily understood. However, these cannot be used to infer significance (use sccomp_test() instead), and a lot of care must be taken given the nonlinearity of these measures (a 1-fold increase from 0.0001 to 0.0002 carries a different weight than a 1-fold increase from 0.4 to 0.8).
From your estimates, you can specify which effects you are interested in (this can be a subset of the full model if you wish to exclude unwanted effects), and the two points you would like to compare.
In the case of a categorical variable, the starting and ending points are categories.
sccomp_result |>
sccomp_proportional_fold_change(
formula_composition = ~ type,
from = "benign",
to = "cancer"
) |>
select(cell_group, statement)
## Loading model from cache...
## Running standalone generated quantities after 1 MCMC chain, with 1 thread(s) per chain...
##
## Chain 1 finished in 0.0 seconds.
## # A tibble: 36 × 2
## cell_group statement
## <chr> <glue>
## 1 B1 2-fold decrease (from 0.0585 to 0.0295)
## 2 B2 2.1-fold decrease (from 0.0383 to 0.0182)
## 3 B3 1.4-fold decrease (from 0.0129 to 0.0091)
## 4 BM 1.4-fold decrease (from 0.0066 to 0.0048)
## 5 CD4 1 1.2-fold increase (from 0.0249 to 0.0297)
## 6 CD4 2 1.4-fold increase (from 0.0511 to 0.0732)
## 7 CD4 3 2.6-fold decrease (from 0.0856 to 0.0332)
## 8 CD4 4 1.1-fold increase (from 0.0017 to 0.0018)
## 9 CD4 5 1-fold increase (from 0.0305 to 0.0315)
## 10 CD8 1 1.1-fold increase (from 0.11 to 0.1205)
## # ℹ 26 more rows
seurat_obj |>
sccomp_estimate(
formula_composition = ~ 0 + type,
sample = "sample",
cell_group = "cell_group",
cores = 1, verbose = FALSE
) |>
sccomp_test( contrasts = c("typecancer - typehealthy", "typehealthy - typecancer"))
## sccomp model
## ============
##
## Model specifications:
## Family: multi_beta_binomial
## Composition formula: ~0 + type
## Variability formula: ~1
## Inference method: pathfinder
##
## Data: Samples: 20 Cell groups: 30
##
## Column prefixes: c_ -> composition parameters v_ -> variability parameters
##
## Convergence diagnostics:
## For each parameter, n_eff is the effective sample size and R_k_hat is the potential
## scale reduction factor on split chains (at convergence, R_k_hat = 1).
##
## # A tibble: 60 × 11
## cell_group parameter factor c_lower c_effect c_upper c_pH0 c_FDR c_rhat
## <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 B immature typecanc… <NA> -1.90 -1.34 -0.793 2.50e-4 4.17e-5 NA
## 2 B immature typeheal… <NA> 0.793 1.34 1.90 2.50e-4 4.17e-5 NA
## 3 B mem typecanc… <NA> -2.25 -1.65 -1.03 0 0 NA
## 4 B mem typeheal… <NA> 1.03 1.65 2.25 0 0 NA
## 5 CD4 cm S100… typecanc… <NA> -1.45 -0.990 -0.535 0 0 NA
## 6 CD4 cm S100… typeheal… <NA> 0.535 0.990 1.45 0 0 NA
## 7 CD4 cm high… typecanc… <NA> 0.822 1.54 2.23 0 0 NA
## 8 CD4 cm high… typeheal… <NA> -2.23 -1.54 -0.822 0 0 NA
## 9 CD4 cm ribo… typecanc… <NA> 0.296 0.944 1.60 7.25e-3 2.10e-3 NA
## 10 CD4 cm ribo… typeheal… <NA> -1.60 -0.944 -0.296 7.25e-3 2.10e-3 NA
## # ℹ 50 more rows
## # ℹ 2 more variables: c_ess_bulk <dbl>, c_ess_tail <dbl>
This is achieved through model comparison with loo. In the following example, the model with association with factors better fits the data compared to the baseline model with no factor association. For model comparisons sccomp_remove_outliers() must not be executed as the leave-one-out must work with the same amount of data, while outlier elimination does not guarantee it.
If elpd_diff
The new tidy framework was introduced in 2024. To understand the differences and improvements compared to the old framework, please read this blog post.
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