GitHub - MangiolaLaboratory/sccomp: Mixed-effect model to test differences in cell type proportions from single-cell data, in R (original) (raw)

sccomp - Tests differences in cell type proportions and variability from single-cell data

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 introducesccomp, 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 methods

I: Data are modelled as counts.
II: Group proportions are modelled as compositional.
III: The proportion variability is modelled as cell-type specific.
IV: Information sharing across cell types, mean–variability association.
V: Outlier detection or robustness.
VI: Differential variability analysis.

Method	Year	Model	I	II	III	IV	V	VI
sccomp	2023	Sum-constrained Beta-binomial	●	●	●	●	●	●
scCODA	2021	Dirichlet-multinomial	●	●
quasi-binom.	2021	Quasi-binomial	●	●
rlm	2021	Robust-log-linear	●	●
propeller	2021	Logit-linear + limma	●	●	●
ANCOM-BC	2020	Log-linear	●	●
corncob	2020	Beta-binomial	●	●
scDC	2019	Log-linear	●	●
dmbvs	2017	Dirichlet-multinomial	●	●
MixMC	2016	Zero-inflated Log-linear	●	●
ALDEx2	2014	Dirichlet-multinomial	●	●

Cite

Mangiola, 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

Talk

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.

Characteristics

Complex linear models with continuous and categorical covariates
Multilevel modelling, with population fixed and random effects/intercept
Modelling data from counts
Testing differences in cell-type proportionality
Testing differences in cell-type specific variability
Cell-type information share for variability adaptive shrinkage
Testing differential variability
Probabilistic outlier identification
Cross-dataset learning (hyperpriors).

Installation

sccomp is based on cmdstanr which provides the latest version ofcmdstan 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).

R installation of sccomp
R installation of cmdstanr
cmdstanr call to cmdstan installation

Bioconductor

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")))

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")))

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_variation	Removes the variability for unwanted factors
plot	Plots summary plots to assess significance

Analysis

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.

Binary factor

Of the output table, the estimate columns start with the prefix c_indicate composition, or with v_ indicate variability (when formula_variability is set).

From Seurat, SingleCellExperiment, metadata objects

sccomp_result = sce_obj |> sccomp_estimate( formula_composition = ~ type, sample = "sample", cell_group = "cell_group", cores = 1 ) |> sccomp_test()

From counts

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 andformula_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>    0.880     1.17   1.46   0       0         1.00
##  2 B1         typecancer  type   -1.10     -0.667 -0.236  0.00650 1.34e-3   1.00
##  3 B2         (Intercept) <NA>    0.420     0.747  1.06   0       0         1.00
##  4 B2         typecancer  type   -1.18     -0.712 -0.253  0.00625 8.25e-4   1.00
##  5 B3         (Intercept) <NA>   -0.665    -0.361 -0.0613 0.0447  4.36e-3   1.00
##  6 B3         typecancer  type   -0.754    -0.303  0.141  0.186   5.66e-2   1.00
##  7 BM         (Intercept) <NA>   -1.31     -0.991 -0.677  0       0         1.00
##  8 BM         typecancer  type   -0.749    -0.304  0.150  0.180   5.07e-2   1.00
##  9 CD4 1      (Intercept) <NA>    0.148     0.339  0.526  0.00750 1.12e-3   1.00
## 10 CD4 1      typecancer  type   -0.0789    0.167  0.417  0.293   8.12e-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>

Outlier identification

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.

Summary plots

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)`

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)`

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()

## sccomp says: Some FDR-significant populations may cross the fold change threshold. 
##  This because, as sccomp is a Bayesian method, the FDR is calculated according to Stephens (doi: 10.1093/biostatistics/kxw041), 
##  by sorting the probability of the null hypothesis in ascending order and calculating the cumulative average.

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()

You can produce the series of plots calling the plot method.

Model proportions directly (e.g. from deconvolution)

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()

Continuous factor

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>    0.384    0.758   1.14     1.00      3559.
##  2 B immature        typeheal… type    0.841    1.35    1.87     1.00      3633.
##  3 B immature        continuo… conti… -0.222    0.0555  0.350    1.00      3969.
##  4 B mem             (Interce… <NA>   -1.25    -0.815  -0.385    1.00      3786.
##  5 B mem             typeheal… type    1.07     1.68    2.25     1.00      3905.
##  6 B mem             continuo… conti… -0.243    0.0836  0.405    1.00      4212.
##  7 CD4 cm S100A4     (Interce… <NA>    1.19     1.50    1.82     1.00      3826.
##  8 CD4 cm S100A4     typeheal… type    0.703    1.12    1.53     1.00      3694.
##  9 CD4 cm S100A4     continuo… conti… -0.0581   0.186   0.440    1.00      4163.
## 10 CD4 cm high cyto… (Interce… <NA>   -0.901   -0.455   0.0224   1.00      3716.
## # 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>

Random Effect Modeling (mixed-effect modeling, multilevel-modeling, hierarchical modeling)

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.

Random Intercept Model

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>    0.545    0.843   1.20     1.00      194. 
##  2 B immature typehealthy      type    0.664    1.08    1.56     1.04       88.4
##  3 B immature (Intercept)___G… <NA>   -0.558    0.0837  0.675   NA          NA  
##  4 B immature (Intercept)___G… <NA>   -0.107    0.259   0.735   NA          NA  
##  5 B immature (Intercept)___G… <NA>   -0.0625   0.254   0.693   NA          NA  
##  6 B immature (Intercept)___G… <NA>   -0.742   -0.302   0.0243  NA          NA  
##  7 B mem      (Intercept)      <NA>   -0.797   -0.334   0.125    1.00      106. 
##  8 B mem      typehealthy      type    0.324    1.02    1.59     1.02       76.4
##  9 B mem      (Intercept)___G… <NA>   -0.316    0.0623  0.704   NA          NA  
## 10 B mem      (Intercept)___G… <NA>   -0.0551   0.302   0.842   NA          NA  
## # 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>

Random Effect Model (random slopes)

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>    0.429    0.798   1.14     1.04       49.3
##  2 B immature typehealthy      type    0.587    1.03    1.53     1.05       44.0
##  3 B immature (Intercept)___G… <NA>   -0.183    0.0625  0.519   NA          NA  
##  4 B immature typehealthy___G… <NA>   -0.183    0.0619  0.456   NA          NA  
##  5 B immature (Intercept)___G… <NA>   -0.0765   0.157   0.579   NA          NA  
##  6 B immature typehealthy___G… <NA>   -0.0869   0.154   0.572   NA          NA  
##  7 B immature (Intercept)___G… <NA>   -0.0811   0.171   0.601   NA          NA  
##  8 B immature (Intercept)___G… <NA>   -0.659   -0.222   0.0436  NA          NA  
##  9 B mem      (Intercept)      <NA>   -0.793   -0.376   0.0568   1.01       58.1
## 10 B mem      typehealthy      type    0.419    1.01    1.62     1.01       48.9
## # 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>

Nested Random Effects

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>    0.412    0.784   1.27      1.00       92.5
##  2 B immature typehealthy     type    0.569    1.08    1.55      1.00       79.9
##  3 B immature (Intercept)___… <NA>   -0.432    0.116   0.566    NA          NA  
##  4 B immature typehealthy___… <NA>   -0.140    0.110   0.570    NA          NA  
##  5 B immature (Intercept)___… <NA>   -0.519    0.0756  0.442    NA          NA  
##  6 B immature typehealthy___… <NA>   -0.145    0.0978  0.497    NA          NA  
##  7 B immature (Intercept)___… <NA>   -0.143    0.202   0.587    NA          NA  
##  8 B immature (Intercept)___… <NA>   -0.798   -0.323  -0.00219  NA          NA  
##  9 B immature (Intercept)___… <NA>   -0.385   -0.0444  0.234    NA          NA  
## 10 B immature (Intercept)___… <NA>   -0.0520   0.249   0.680    NA          NA  
## # 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>

An aid to result interpretation and communication

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 0.0591 to 0.0297)  
##  2 B2         2.1-fold decrease (from 0.0386 to 0.0182)
##  3 B3         1.4-fold decrease (from 0.0129 to 0.009) 
##  4 BM         1.3-fold decrease (from 0.0065 to 0.0049)
##  5 CD4 1      1.2-fold increase (from 0.0249 to 0.03)  
##  6 CD4 2      1.4-fold increase (from 0.0515 to 0.0732)
##  7 CD4 3      2.5-fold decrease (from 0.0838 to 0.0331)
##  8 CD4 4      1-fold increase (from 0.0017 to 0.0018)  
##  9 CD4 5      1-fold increase (from 0.0303 to 0.0312)  
## 10 CD8 1      1.1-fold increase (from 0.1107 to 0.1205)
## # ℹ 26 more rows

Contrasts

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>    -1.88    -1.34   -0.797 0       0           NA
##  2 B immature   typeheal… <NA>     0.797    1.34    1.88  0       0           NA
##  3 B mem        typecanc… <NA>    -2.23    -1.63   -1.02  0       0           NA
##  4 B mem        typeheal… <NA>     1.02     1.63    2.23  0       0           NA
##  5 CD4 cm S100… typecanc… <NA>    -1.43    -0.983  -0.529 2.50e-4 5.00e-5     NA
##  6 CD4 cm S100… typeheal… <NA>     0.529    0.983   1.43  2.50e-4 5.00e-5     NA
##  7 CD4 cm high… typecanc… <NA>     0.831    1.54    2.24  2.50e-4 8.33e-5     NA
##  8 CD4 cm high… typeheal… <NA>    -2.24    -1.54   -0.831 2.50e-4 8.33e-5     NA
##  9 CD4 cm ribo… typecanc… <NA>     0.296    0.951   1.56  6.75e-3 2.09e-3     NA
## 10 CD4 cm ribo… typeheal… <NA>    -1.56    -0.951  -0.296 6.75e-3 2.09e-3     NA
## # ℹ 50 more rows
## # ℹ 2 more variables: c_ess_bulk <dbl>, c_ess_tail <dbl>

Categorical factor (e.g. Bayesian ANOVA)

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 old framework

The new tidy framework was introduced in 2024. To understand the differences and improvements compared to the old framework, please read this blog post.