Sccomp is a generalised method for differential composition and variability analyses.
Bioconductor
if (!requireNamespace("BiocManager")) install.packages("BiocManager")
BiocManager::install("sccomp")
Github
devtools::install_github("stemangiola/sccomp")
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.
single_cell_object |>
sccomp_estimate(
formula_composition = ~ type,
.sample = sample,
.cell_group = cell_group,
bimodal_mean_variability_association = TRUE,
cores = 1
)
counts_obj |>
sccomp_estimate(
formula_composition = ~ type,
.sample = sample,
.cell_group = cell_group,
.count = count,
bimodal_mean_variability_association = TRUE,
cores = 1
)
##
## SAMPLING FOR MODEL 'glm_multi_beta_binomial' NOW (CHAIN 1).
## Chain 1:
## Chain 1: Gradient evaluation took 0.000693 seconds
## Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 6.93 seconds.
## Chain 1: Adjust your expectations accordingly!
## Chain 1:
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## Chain 1:
## Chain 1: Elapsed Time: 4.419 seconds (Warm-up)
## Chain 1: 33.207 seconds (Sampling)
## Chain 1: 37.626 seconds (Total)
## Chain 1:
## # A tibble: 72 × 14
## cell_group parameter factor c_lower c_effect c_upper c_n_eff c_R_k_hat
## <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 B1 (Intercept) <NA> 0.880 1.12 1.35 4970. 1.00
## 2 B1 typecancer type -1.06 -0.647 -0.253 4152. 1.00
## 3 B2 (Intercept) <NA> 0.373 0.697 0.981 5682. 1.00
## 4 B2 typecancer type -1.19 -0.717 -0.247 4503. 1.00
## 5 B3 (Intercept) <NA> -0.677 -0.394 -0.128 4389. 1.00
## 6 B3 typecancer type -0.753 -0.308 0.0938 4722. 1.00
## 7 BM (Intercept) <NA> -1.33 -1.03 -0.757 4090. 1.00
## 8 BM typecancer type -0.735 -0.306 0.109 3374. 1.00
## 9 CD4 1 (Intercept) <NA> 0.0721 0.296 0.507 4243. 1.00
## 10 CD4 1 typecancer type -0.0987 0.187 0.479 3491. 1.00
## # ℹ 62 more rows
## # ℹ 6 more variables: v_lower <dbl>, v_effect <dbl>, v_upper <dbl>,
## # v_n_eff <dbl>, v_R_k_hat <dbl>, count_data <list>
Of the output table, the estimate columns start with the prefix c_
indicate composition
, or with v_
indicate variability
(when formula_variability is set).
seurat_obj |>
sccomp_estimate(
formula_composition = ~ 0 + type,
.sample = sample,
.cell_group = cell_group,
bimodal_mean_variability_association = TRUE,
cores = 1
) |>
sccomp_test( contrasts = c("typecancer - typehealthy", "typehealthy - typecancer"))
##
## SAMPLING FOR MODEL 'glm_multi_beta_binomial' NOW (CHAIN 1).
## Chain 1:
## Chain 1: Gradient evaluation took 0.000516 seconds
## Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 5.16 seconds.
## Chain 1: Adjust your expectations accordingly!
## Chain 1:
## Chain 1:
## Chain 1: Iteration: 1 / 4300 [ 0%] (Warmup)
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## Chain 1:
## Chain 1: Elapsed Time: 3.336 seconds (Warm-up)
## Chain 1: 27.295 seconds (Sampling)
## Chain 1: 30.631 seconds (Total)
## Chain 1:
## # A tibble: 60 × 18
## cell_group parameter factor c_lower c_effect c_upper c_pH0 c_FDR c_n_eff
## <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 B immature typecanc… <NA> -1.91 -1.39 -0.884 0 0 NA
## 2 B immature typeheal… <NA> 0.884 1.39 1.91 0 0 NA
## 3 B mem typecanc… <NA> -2.35 -1.71 -1.06 0 0 NA
## 4 B mem typeheal… <NA> 1.06 1.71 2.35 0 0 NA
## 5 CD4 cm S10… typecanc… <NA> -1.49 -1.03 -0.594 2.50e-4 6.25e-5 NA
## 6 CD4 cm S10… typeheal… <NA> 0.594 1.03 1.49 2.50e-4 6.25e-5 NA
## 7 CD4 cm hig… typecanc… <NA> 0.792 1.76 2.87 2.50e-4 1.00e-4 NA
## 8 CD4 cm hig… typeheal… <NA> -2.87 -1.76 -0.792 2.50e-4 1.00e-4 NA
## 9 CD4 cm rib… typecanc… <NA> 0.298 0.986 1.70 1.42e-2 4.32e-3 NA
## 10 CD4 cm rib… typeheal… <NA> -1.70 -0.986 -0.298 1.42e-2 4.32e-3 NA
## # ℹ 50 more rows
## # ℹ 9 more variables: c_R_k_hat <dbl>, v_lower <dbl>, v_effect <dbl>,
## # v_upper <dbl>, v_pH0 <dbl>, v_FDR <dbl>, v_n_eff <dbl>, v_R_k_hat <dbl>,
## # count_data <list>
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 comparisons check_outliers
must be set to FALSE as the leave-one-out must work with the same amount of data, while outlier elimination does not guarantee it.
If elpd_diff
is away from zero of > 5 se_diff
difference of 5, we are confident that a model is better than the other reference.
In this case, -79.9 / 11.5 = -6.9, therefore we can conclude that model one, the one with factor association, is better than model two.
library(loo)
# Fit first model
model_with_factor_association =
seurat_obj |>
sccomp_estimate(
formula_composition = ~ type,
.sample = sample,
.cell_group = cell_group,
check_outliers = FALSE,
bimodal_mean_variability_association = TRUE,
cores = 1,
enable_loo = TRUE
)
# Fit second model
model_without_association =
seurat_obj |>
sccomp_estimate(
formula_composition = ~ 1,
.sample = sample,
.cell_group = cell_group,
check_outliers = FALSE,
bimodal_mean_variability_association = TRUE,
cores = 1 ,
enable_loo = TRUE
)
# Compare models
loo_compare(
model_with_factor_association |> attr("fit") |> loo(),
model_without_association |> attr("fit") |> loo()
)
We can model the cell-group variability also dependent on the type, and so test differences in variability
res =
seurat_obj |>
sccomp_estimate(
formula_composition = ~ type,
formula_variability = ~ type,
.sample = sample,
.cell_group = cell_group,
bimodal_mean_variability_association = TRUE,
cores = 1
)
##
## SAMPLING FOR MODEL 'glm_multi_beta_binomial' NOW (CHAIN 1).
## Chain 1:
## Chain 1: Gradient evaluation took 0.000496 seconds
## Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 4.96 seconds.
## Chain 1: Adjust your expectations accordingly!
## Chain 1:
## Chain 1:
## Chain 1: Iteration: 1 / 4300 [ 0%] (Warmup)
## Chain 1: Iteration: 301 / 4300 [ 7%] (Sampling)
## Chain 1: Iteration: 1300 / 4300 [ 30%] (Sampling)
## Chain 1: Iteration: 2300 / 4300 [ 53%] (Sampling)
## Chain 1: Iteration: 3300 / 4300 [ 76%] (Sampling)
## Chain 1: Iteration: 4300 / 4300 [100%] (Sampling)
## Chain 1:
## Chain 1: Elapsed Time: 6.123 seconds (Warm-up)
## Chain 1: 53.761 seconds (Sampling)
## Chain 1: 59.884 seconds (Total)
## Chain 1:
res
## # A tibble: 60 × 14
## cell_group parameter factor c_lower c_effect c_upper c_n_eff c_R_k_hat
## <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 B immature (Interce… <NA> 0.367 0.769 1.18 5805. 1.00
## 2 B immature typeheal… type 0.842 1.43 1.99 5061. 1.00
## 3 B mem (Interce… <NA> -1.49 -0.866 -0.161 4036. 1.00
## 4 B mem typeheal… type 1.04 1.86 2.64 3728. 1.00
## 5 CD4 cm S100A4 (Interce… <NA> 1.31 1.66 1.98 5708. 1.00
## 6 CD4 cm S100A4 typeheal… type 0.474 0.939 1.39 5281. 1.00
## 7 CD4 cm high cyt… (Interce… <NA> -1.03 -0.542 0.00954 5234. 1.00
## 8 CD4 cm high cyt… typeheal… type -3.06 -1.22 1.16 3103. 1.00
## 9 CD4 cm ribosome (Interce… <NA> -0.0538 0.306 0.694 4884. 1.00
## 10 CD4 cm ribosome typeheal… type -1.81 -0.964 0.0781 4678. 1.00
## # ℹ 50 more rows
## # ℹ 6 more variables: v_lower <dbl>, v_effect <dbl>, v_upper <dbl>,
## # v_n_eff <dbl>, v_R_k_hat <dbl>, count_data <list>
We recommend setting bimodal_mean_variability_association = TRUE
. The bimodality of the mean-variability association can be confirmed from the plots$credible_intervals_2D (see below).
We recommend setting bimodal_mean_variability_association = FALSE
(Default).
plots = res |> sccomp_test() |> plot_summary()
## Warning: `plot_summary()` was deprecated in sccomp 1.7.1.
## ℹ sccomp says: plot_summary() is soft-deprecated. Please use sccomp_test().
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## cell_group parameter factor c_lower c_effect
## 1 B immature (Intercept) <NA> 0.36714960 0.769063346
## 2 B immature typehealthy type 0.84180431 1.434520538
## 3 B mem (Intercept) <NA> -1.48921622 -0.866042080
## 4 B mem typehealthy type 1.04093816 1.861888385
## 5 CD4 cm S100A4 (Intercept) <NA> 1.30946655 1.662220821
## 6 CD4 cm S100A4 typehealthy type 0.47432050 0.938966484
## 7 CD4 cm high cytokine (Intercept) <NA> -1.03279974 -0.541792777
## 8 CD4 cm high cytokine typehealthy type -3.06131684 -1.218953275
## 9 CD4 cm ribosome (Intercept) <NA> -0.05380041 0.305894694
## 10 CD4 cm ribosome typehealthy type -1.80627582 -0.964037534
## 11 CD4 em high cytokine (Intercept) <NA> -1.16827385 -0.707911378
## 12 CD4 em high cytokine typehealthy type -2.32982524 -1.267436140
## 13 CD4 naive (Intercept) <NA> 0.74838491 1.211643519
## 14 CD4 naive typehealthy type 0.45151064 1.142218863
## 15 CD4 ribosome (Intercept) <NA> -0.29726785 0.046538669
## 16 CD4 ribosome typehealthy type 1.44939833 2.056778591
## 17 CD8 em 1 (Intercept) <NA> 0.28301490 0.683160799
## 18 CD8 em 1 typehealthy type -0.03043376 0.781722909
## 19 CD8 em 2 (Intercept) <NA> -0.91347221 0.138266684
## 20 CD8 em 2 typehealthy type -3.54389720 -1.134040355
## 21 CD8 em 3 (Intercept) <NA> -1.39088676 -0.662575055
## 22 CD8 em 3 typehealthy type -1.74806697 -0.814719668
## 23 CD8 naive (Intercept) <NA> 0.58731122 1.020907451
## 24 CD8 naive typehealthy type -0.70878641 0.005401482
## 25 CD8 transitional (Intercept) <NA> 0.90096497 1.209803385
## 26 CD8 transitional typehealthy type 0.17957566 0.683288824
## 27 MAIT (Intercept) <NA> -1.05144268 -0.597450401
## 28 MAIT typehealthy type 0.46635056 1.209515514
## 29 Mac M1 (Intercept) <NA> 0.62583442 0.949116764
## 30 Mac M1 typehealthy type 0.03165886 0.593128278
## 31 Mac M1 FCER1A (Intercept) <NA> -1.33254732 -0.983485510
## 32 Mac M1 FCER1A typehealthy type -1.79881252 -1.068023548
## 33 Mono (Intercept) <NA> -1.80820415 -1.337378989
## 34 Mono typehealthy type -0.84397959 -0.118718215
## 35 Mono NKG7 1 (Intercept) <NA> -1.47715155 -0.873120573
## 36 Mono NKG7 1 typehealthy type -4.00459122 -2.864291927
## 37 Mono NKG7 2 (Intercept) <NA> -0.42176260 -0.014464654
## 38 Mono NKG7 2 typehealthy type -2.43766291 -1.292219635
## 39 Mono classic inflam (Intercept) <NA> 1.49888142 1.783381517
## 40 Mono classic inflam typehealthy type -1.24301291 -0.450991681
## 41 Mono non-classic (Intercept) <NA> 0.61714585 0.963521050
## 42 Mono non-classic typehealthy type -0.65137438 0.005515399
## 43 Myeloid migratory (Intercept) <NA> -0.36121811 0.022998608
## 44 Myeloid migratory typehealthy type -1.34255050 -0.563779831
## 45 NK (Intercept) <NA> 1.12159264 1.434522221
## 46 NK typehealthy type -0.04635236 0.539163697
## 47 NK cycling (Intercept) <NA> -1.67444697 -1.209958145
## 48 NK cycling typehealthy type -0.79650498 -0.186326087
## 49 NK high cytokine (Intercept) <NA> -2.22419952 -1.640829858
## 50 NK high cytokine typehealthy type -1.18688802 0.008488138
## 51 Stem (Intercept) <NA> -1.08664179 -0.569628573
## 52 Stem typehealthy type -3.22879492 -2.239123013
## 53 T gd1 (Intercept) <NA> -1.06777916 -0.689845024
## 54 T gd1 typehealthy type 0.04203475 0.920521874
## 55 T gd2 (Intercept) <NA> -1.24443601 -0.668973219
## 56 T gd2 typehealthy type -0.42754553 0.511537788
## 57 cDC2 (Intercept) <NA> -0.75392563 -0.296024832
## 58 cDC2 typehealthy type -0.34412558 0.310340803
## 59 pDC (Intercept) <NA> -1.15399879 -0.672606034
## 60 pDC typehealthy type -0.08755515 0.668205977
## c_upper c_pH0 c_FDR c_n_eff c_R_k_hat v_lower v_effect
## 1 1.183132826 0.00475 4.423077e-04 5804.832 1.0000831 -4.49425352 -3.82358583
## 2 1.994177904 0.00000 0.000000e+00 5061.157 0.9998241 -1.66080948 -0.52051568
## 3 -0.161027248 0.02975 5.058824e-03 4035.640 0.9999466 -5.30849510 -4.56068059
## 4 2.641768018 0.00050 1.666667e-04 3728.405 0.9998594 -2.07579401 -0.95967928
## 5 1.984312313 0.00000 0.000000e+00 5707.761 1.0000218 -3.81280686 -3.20068858
## 6 1.387948444 0.00050 2.500000e-04 5280.886 0.9999268 -2.04575627 -0.92366410
## 7 0.009540860 0.11375 2.150000e-02 5233.777 0.9999258 -5.44334446 -4.70472262
## 8 1.159410683 0.20850 5.860714e-02 3103.410 0.9997532 -0.44392772 1.53301428
## 9 0.694424086 0.27725 3.568000e-02 4883.767 0.9997599 -5.39572536 -4.60644556
## 10 0.078117977 0.06800 2.067308e-02 4677.789 0.9997866 -0.60058289 0.65709209
## 11 -0.204860178 0.02425 3.515625e-03 4780.251 1.0001014 -5.93855098 -5.13019205
## 12 0.201051958 0.06275 1.672917e-02 2895.990 0.9998174 -1.10290540 0.35584273
## 13 1.655136157 0.00000 0.000000e+00 5285.436 0.9999870 -3.67413841 -3.06665923
## 14 1.793717568 0.00475 1.625000e-03 5405.964 0.9997956 -0.85541465 0.20563263
## 15 0.429904455 0.80150 9.215179e-02 5246.524 0.9998781 -5.68149382 -4.89247233
## 16 2.619605775 0.00000 0.000000e+00 6002.252 0.9998448 -0.60909191 0.56178635
## 17 1.095717492 0.00925 1.071429e-03 4554.364 0.9997555 -4.77020511 -4.06351740
## 18 1.587738529 0.07650 2.801667e-02 5604.482 0.9999109 0.02303552 1.07856220
## 19 1.204499556 0.54700 6.587963e-02 3939.377 0.9997515 -3.68320039 -1.98962494
## 20 1.298021163 0.24225 6.695455e-02 4757.427 0.9997847 -1.61667240 0.52709121
## 21 0.171016844 0.12025 2.561458e-02 4073.178 1.0002938 -4.73115288 -3.94816269
## 22 0.059869878 0.08325 3.444118e-02 4185.374 1.0008161 -4.08976614 -2.69211811
## 23 1.424392767 0.00000 0.000000e+00 5106.271 0.9997750 -4.15480611 -3.52264186
## 24 0.764224605 0.70400 1.646552e-01 5720.550 0.9999066 -0.96716520 0.02866965
## 25 1.509690170 0.00000 0.000000e+00 5063.679 0.9999499 -4.84064851 -4.08000028
## 26 1.170887508 0.03225 8.200000e-03 4955.100 1.0000125 -1.14741183 0.06981578
## 27 -0.107181538 0.05200 1.050000e-02 4971.923 1.0000566 -5.84097162 -5.07193711
## 28 1.994107339 0.00400 1.000000e-03 5257.759 1.0000176 -0.73510840 0.37131706
## 29 1.272474270 0.00025 2.500000e-05 4409.177 0.9997518 -4.97692567 -4.23413718
## 30 1.189804639 0.08200 3.139062e-02 5201.672 1.0001217 -0.80065900 0.32442542
## 31 -0.569027745 0.00050 8.333333e-05 4830.164 0.9998641 -6.99648526 -5.99416465
## 32 -0.232526205 0.02125 5.527778e-03 5182.060 0.9997500 -1.49146819 -0.04611469
## 33 -0.770142514 0.00025 4.545455e-05 5001.958 0.9998030 -6.64371797 -5.72459045
## 34 0.678280270 0.58800 1.283241e-01 4668.201 0.9997748 -2.13823677 -0.77018561
## 35 -0.129099907 0.03600 6.777778e-03 5439.393 0.9997927 -5.23724861 -4.49122738
## 36 -0.802901636 0.01175 3.562500e-03 1658.449 0.9997710 -5.12773436 -2.30922799
## 37 0.418256355 0.82575 1.406500e-01 4820.927 1.0000209 -5.42796707 -4.68582016
## 38 0.347809883 0.07500 2.455357e-02 3288.742 0.9998232 -0.15878334 1.19736475
## 39 2.043633191 0.00000 0.000000e+00 4091.411 0.9997501 -4.33538469 -3.59449821
## 40 0.339799146 0.26025 8.273958e-02 6458.211 0.9999115 0.37413719 1.44243821
## 41 1.314842463 0.00000 0.000000e+00 5000.956 1.0002260 -4.69962115 -4.01304728
## 42 0.697524601 0.72025 1.831750e-01 5622.655 0.9999752 -0.87949469 0.26184492
## 43 0.413704376 0.81350 1.170259e-01 4197.125 0.9999760 -5.60232320 -4.82230577
## 44 0.326072004 0.19575 5.111250e-02 4953.893 0.9999792 -0.76973453 0.36538390
## 45 1.724650445 0.00000 0.000000e+00 4542.086 0.9999133 -4.53828869 -3.82212997
## 46 1.119820329 0.12375 4.350000e-02 6669.402 0.9999531 -0.54556570 0.63920081
## 47 -0.723752570 0.00000 0.000000e+00 5799.727 1.0002459 -6.62859084 -5.78883275
## 48 0.407401864 0.51675 1.106442e-01 5297.126 1.0008828 -3.36471236 -1.60589786
## 49 -0.981964853 0.00000 0.000000e+00 5666.606 0.9997554 -6.42438087 -5.55654861
## 50 1.712619969 0.60625 1.453929e-01 3096.247 0.9998278 -0.69309414 0.67627679
## 51 0.001981351 0.09425 1.730682e-02 4315.549 1.0002616 -5.47641541 -4.75648605
## 52 -0.759296871 0.00700 2.392857e-03 2898.335 1.0008208 -2.74683116 -0.85498719
## 53 -0.247686364 0.01700 2.133333e-03 4820.365 0.9997553 -6.45661301 -5.57265623
## 54 1.900919770 0.05600 1.254545e-02 5756.661 1.0000903 0.15042828 1.35109723
## 55 -0.004348855 0.07650 1.364286e-02 5405.766 0.9997821 -5.29849773 -4.55772137
## 56 1.541217415 0.25250 7.502174e-02 5943.765 0.9999870 -1.19153685 -0.10591760
## 57 0.200285213 0.33975 4.737500e-02 4855.585 0.9997861 -5.36179925 -4.62170153
## 58 1.001998000 0.37425 9.440000e-02 4453.938 1.0000085 -1.76171017 -0.63317498
## 59 -0.151674725 0.03600 8.315789e-03 5020.575 0.9997530 -5.81014918 -5.04923079
## 60 1.497681456 0.11725 3.904167e-02 5016.910 1.0001961 -1.11464141 -0.01018327
## v_upper v_pH0 v_FDR v_n_eff v_R_k_hat count_data
## 1 -3.01594734 0.00000 0.00000000 5409.314 0.9999002 c("10x_6....
## 2 0.64489741 0.29950 0.14586111 6159.312 0.9997500 c("10x_6....
## 3 -3.57864093 0.00000 0.00000000 3519.519 1.0005457 c("10x_6....
## 4 0.23494621 0.10400 0.05611111 4261.362 1.0008244 c("10x_6....
## 5 -2.44650791 0.00000 0.00000000 5312.461 0.9997632 c("10x_6....
## 6 0.28833540 0.11550 0.06205000 4377.167 1.0002416 c("10x_6....
## 7 -3.79514969 0.00000 0.00000000 6881.401 0.9997504 c("10x_6....
## 8 3.51682878 0.09825 0.05012500 3613.886 0.9997533 c("10x_6....
## 9 -3.70263597 0.00000 0.00000000 8045.754 0.9998078 c("10x_6....
## 10 1.96292805 0.24000 0.11041071 5328.321 0.9998238 c("10x_6....
## 11 -4.16504193 0.00000 0.00000000 7844.006 0.9997662 c("10x_6....
## 12 1.94510705 0.41900 0.20035870 4015.516 0.9997925 c("10x_6....
## 13 -2.25344017 0.00000 0.00000000 4686.363 1.0021748 c("10x_6....
## 14 1.28787048 0.49425 0.22224000 4874.766 1.0007906 c("10x_6....
## 15 -3.99899991 0.00000 0.00000000 5816.735 0.9997749 c("10x_6....
## 16 1.82008789 0.27650 0.13682353 5464.671 0.9998611 c("10x_6....
## 17 -3.22230735 0.00000 0.00000000 7598.794 0.9997555 c("10x_6....
## 18 2.19544084 0.05200 0.02680000 4961.944 0.9997587 c("10x_6....
## 19 -0.60663354 0.00375 0.00012500 3276.876 0.9998203 c("10x_6....
## 20 2.66022070 0.37450 0.15789474 4677.905 0.9999591 c("10x_6....
## 21 -2.67633748 0.00000 0.00000000 2524.829 1.0000877 c("10x_6....
## 22 -1.35504439 0.00050 0.00050000 3574.751 0.9999963 c("10x_6....
## 23 -2.73242973 0.00000 0.00000000 6046.399 0.9997716 c("10x_6....
## 24 1.07409430 0.62875 0.28440833 4146.001 0.9997579 c("10x_6....
## 25 -3.25130320 0.00000 0.00000000 6257.730 1.0001361 c("10x_6....
## 26 1.32194977 0.58200 0.25992857 5480.366 0.9997586 c("10x_6....
## 27 -4.17176192 0.00000 0.00000000 6200.257 0.9997548 c("10x_6....
## 28 1.54555210 0.38525 0.16926250 5547.914 0.9997650 c("10x_6....
## 29 -3.41533777 0.00000 0.00000000 4768.682 0.9997587 c("10x_6....
## 30 1.49281552 0.41350 0.19042045 4414.391 0.9997528 c("10x_6....
## 31 -4.93406855 0.00000 0.00000000 5913.986 1.0004125 c("10x_6....
## 32 1.42011291 0.58000 0.24800000 4469.144 1.0017238 c("10x_6....
## 33 -4.67247613 0.00000 0.00000000 4841.088 1.0010174 c("10x_6....
## 34 0.69247644 0.22100 0.07650000 4836.522 1.0013204 c("10x_6....
## 35 -3.47612063 0.00000 0.00000000 4929.612 0.9998365 c("10x_6....
## 36 0.67789815 0.08400 0.03633333 2328.391 0.9999460 c("10x_6....
## 37 -3.81334114 0.00000 0.00000000 6741.012 0.9997905 c("10x_6....
## 38 2.71988969 0.08475 0.04325000 3806.932 0.9997547 c("10x_6....
## 39 -2.74397225 0.00000 0.00000000 6197.531 0.9997701 c("10x_6....
## 40 2.58005633 0.01425 0.00737500 5120.894 0.9998826 c("10x_6....
## 41 -3.20985712 0.00000 0.00000000 6615.290 0.9997545 c("10x_6....
## 42 1.47759316 0.45350 0.21090625 4925.025 1.0000988 c("10x_6....
## 43 -3.94847912 0.00000 0.00000000 6388.722 1.0006200 c("10x_6....
## 44 1.62040948 0.39050 0.17979762 5114.924 0.9998885 c("10x_6....
## 45 -2.97726301 0.00000 0.00000000 6076.717 0.9999879 c("10x_6....
## 46 1.79098166 0.23375 0.10044231 6150.762 1.0000851 c("10x_6....
## 47 -4.82573753 0.00000 0.00000000 5072.326 0.9997768 c("10x_6....
## 48 -0.07313994 0.03675 0.02050000 5370.470 1.0000990 c("10x_6....
## 49 -4.51508835 0.00000 0.00000000 7366.782 1.0004126 c("10x_6....
## 50 2.18789925 0.24700 0.11951667 3798.662 1.0004409 c("10x_6....
## 51 -3.84185317 0.00000 0.00000000 6667.789 0.9997500 c("10x_6....
## 52 1.05148876 0.25675 0.12809375 3206.943 0.9997500 c("10x_6....
## 53 -4.58170979 0.00000 0.00000000 5372.081 0.9997860 c("10x_6....
## 54 2.67644598 0.03050 0.01508333 4389.058 0.9998464 c("10x_6....
## 55 -3.58871639 0.00000 0.00000000 6109.684 0.9999189 c("10x_6....
## 56 1.06580944 0.56000 0.23523077 4692.084 1.0000305 c("10x_6....
## 57 -3.71581854 0.00000 0.00000000 5725.641 0.9997870 c("10x_6....
## 58 0.51819715 0.23050 0.08933333 5026.768 1.0000341 c("10x_6....
## 59 -4.08480824 0.00000 0.00000000 5956.403 0.9998031 c("10x_6....
## 60 1.15204092 0.62550 0.27253448 5516.992 0.9997873 c("10x_6....
A plot of group proportion, faceted by groups. The blue boxplots represent the posterior predictive check. If the model is likely to be
descriptively adequate to the data, the blue box plot should roughly overlay with the black box plot, which represents the observed data. The
outliers are coloured in red. A box plot will be returned for every (discrete) covariate present in formula_composition
. The colour coding
represents the significant associations for composition and/or variability.
plots$boxplot
## Warning: Unknown or uninitialised column: `boxplot`.
## NULL
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 bigger than the minimal effect according to the 95% credible interval. Facets represent the covariates in the model.
plots$credible_intervals_1D
## Warning: Unknown or uninitialised column: `credible_intervals_1D`.
## NULL
It is possible to directly evaluate the posterior distribution. In this example, we plot the Monte Carlo chain for the slope parameter of the first cell type. We can see that it has converged and is negative with probability 1.
res %>% attr("fit") %>% rstan::traceplot("beta[2,1]")
Plot 1D significance plot
plots = res |> sccomp_test() |> plot_summary()
## cell_group parameter factor c_lower c_effect
## 1 B immature (Intercept) <NA> 0.36714960 0.769063346
## 2 B immature typehealthy type 0.84180431 1.434520538
## 3 B mem (Intercept) <NA> -1.48921622 -0.866042080
## 4 B mem typehealthy type 1.04093816 1.861888385
## 5 CD4 cm S100A4 (Intercept) <NA> 1.30946655 1.662220821
## 6 CD4 cm S100A4 typehealthy type 0.47432050 0.938966484
## 7 CD4 cm high cytokine (Intercept) <NA> -1.03279974 -0.541792777
## 8 CD4 cm high cytokine typehealthy type -3.06131684 -1.218953275
## 9 CD4 cm ribosome (Intercept) <NA> -0.05380041 0.305894694
## 10 CD4 cm ribosome typehealthy type -1.80627582 -0.964037534
## 11 CD4 em high cytokine (Intercept) <NA> -1.16827385 -0.707911378
## 12 CD4 em high cytokine typehealthy type -2.32982524 -1.267436140
## 13 CD4 naive (Intercept) <NA> 0.74838491 1.211643519
## 14 CD4 naive typehealthy type 0.45151064 1.142218863
## 15 CD4 ribosome (Intercept) <NA> -0.29726785 0.046538669
## 16 CD4 ribosome typehealthy type 1.44939833 2.056778591
## 17 CD8 em 1 (Intercept) <NA> 0.28301490 0.683160799
## 18 CD8 em 1 typehealthy type -0.03043376 0.781722909
## 19 CD8 em 2 (Intercept) <NA> -0.91347221 0.138266684
## 20 CD8 em 2 typehealthy type -3.54389720 -1.134040355
## 21 CD8 em 3 (Intercept) <NA> -1.39088676 -0.662575055
## 22 CD8 em 3 typehealthy type -1.74806697 -0.814719668
## 23 CD8 naive (Intercept) <NA> 0.58731122 1.020907451
## 24 CD8 naive typehealthy type -0.70878641 0.005401482
## 25 CD8 transitional (Intercept) <NA> 0.90096497 1.209803385
## 26 CD8 transitional typehealthy type 0.17957566 0.683288824
## 27 MAIT (Intercept) <NA> -1.05144268 -0.597450401
## 28 MAIT typehealthy type 0.46635056 1.209515514
## 29 Mac M1 (Intercept) <NA> 0.62583442 0.949116764
## 30 Mac M1 typehealthy type 0.03165886 0.593128278
## 31 Mac M1 FCER1A (Intercept) <NA> -1.33254732 -0.983485510
## 32 Mac M1 FCER1A typehealthy type -1.79881252 -1.068023548
## 33 Mono (Intercept) <NA> -1.80820415 -1.337378989
## 34 Mono typehealthy type -0.84397959 -0.118718215
## 35 Mono NKG7 1 (Intercept) <NA> -1.47715155 -0.873120573
## 36 Mono NKG7 1 typehealthy type -4.00459122 -2.864291927
## 37 Mono NKG7 2 (Intercept) <NA> -0.42176260 -0.014464654
## 38 Mono NKG7 2 typehealthy type -2.43766291 -1.292219635
## 39 Mono classic inflam (Intercept) <NA> 1.49888142 1.783381517
## 40 Mono classic inflam typehealthy type -1.24301291 -0.450991681
## 41 Mono non-classic (Intercept) <NA> 0.61714585 0.963521050
## 42 Mono non-classic typehealthy type -0.65137438 0.005515399
## 43 Myeloid migratory (Intercept) <NA> -0.36121811 0.022998608
## 44 Myeloid migratory typehealthy type -1.34255050 -0.563779831
## 45 NK (Intercept) <NA> 1.12159264 1.434522221
## 46 NK typehealthy type -0.04635236 0.539163697
## 47 NK cycling (Intercept) <NA> -1.67444697 -1.209958145
## 48 NK cycling typehealthy type -0.79650498 -0.186326087
## 49 NK high cytokine (Intercept) <NA> -2.22419952 -1.640829858
## 50 NK high cytokine typehealthy type -1.18688802 0.008488138
## 51 Stem (Intercept) <NA> -1.08664179 -0.569628573
## 52 Stem typehealthy type -3.22879492 -2.239123013
## 53 T gd1 (Intercept) <NA> -1.06777916 -0.689845024
## 54 T gd1 typehealthy type 0.04203475 0.920521874
## 55 T gd2 (Intercept) <NA> -1.24443601 -0.668973219
## 56 T gd2 typehealthy type -0.42754553 0.511537788
## 57 cDC2 (Intercept) <NA> -0.75392563 -0.296024832
## 58 cDC2 typehealthy type -0.34412558 0.310340803
## 59 pDC (Intercept) <NA> -1.15399879 -0.672606034
## 60 pDC typehealthy type -0.08755515 0.668205977
## c_upper c_pH0 c_FDR c_n_eff c_R_k_hat v_lower v_effect
## 1 1.183132826 0.00475 4.423077e-04 5804.832 1.0000831 -4.49425352 -3.82358583
## 2 1.994177904 0.00000 0.000000e+00 5061.157 0.9998241 -1.66080948 -0.52051568
## 3 -0.161027248 0.02975 5.058824e-03 4035.640 0.9999466 -5.30849510 -4.56068059
## 4 2.641768018 0.00050 1.666667e-04 3728.405 0.9998594 -2.07579401 -0.95967928
## 5 1.984312313 0.00000 0.000000e+00 5707.761 1.0000218 -3.81280686 -3.20068858
## 6 1.387948444 0.00050 2.500000e-04 5280.886 0.9999268 -2.04575627 -0.92366410
## 7 0.009540860 0.11375 2.150000e-02 5233.777 0.9999258 -5.44334446 -4.70472262
## 8 1.159410683 0.20850 5.860714e-02 3103.410 0.9997532 -0.44392772 1.53301428
## 9 0.694424086 0.27725 3.568000e-02 4883.767 0.9997599 -5.39572536 -4.60644556
## 10 0.078117977 0.06800 2.067308e-02 4677.789 0.9997866 -0.60058289 0.65709209
## 11 -0.204860178 0.02425 3.515625e-03 4780.251 1.0001014 -5.93855098 -5.13019205
## 12 0.201051958 0.06275 1.672917e-02 2895.990 0.9998174 -1.10290540 0.35584273
## 13 1.655136157 0.00000 0.000000e+00 5285.436 0.9999870 -3.67413841 -3.06665923
## 14 1.793717568 0.00475 1.625000e-03 5405.964 0.9997956 -0.85541465 0.20563263
## 15 0.429904455 0.80150 9.215179e-02 5246.524 0.9998781 -5.68149382 -4.89247233
## 16 2.619605775 0.00000 0.000000e+00 6002.252 0.9998448 -0.60909191 0.56178635
## 17 1.095717492 0.00925 1.071429e-03 4554.364 0.9997555 -4.77020511 -4.06351740
## 18 1.587738529 0.07650 2.801667e-02 5604.482 0.9999109 0.02303552 1.07856220
## 19 1.204499556 0.54700 6.587963e-02 3939.377 0.9997515 -3.68320039 -1.98962494
## 20 1.298021163 0.24225 6.695455e-02 4757.427 0.9997847 -1.61667240 0.52709121
## 21 0.171016844 0.12025 2.561458e-02 4073.178 1.0002938 -4.73115288 -3.94816269
## 22 0.059869878 0.08325 3.444118e-02 4185.374 1.0008161 -4.08976614 -2.69211811
## 23 1.424392767 0.00000 0.000000e+00 5106.271 0.9997750 -4.15480611 -3.52264186
## 24 0.764224605 0.70400 1.646552e-01 5720.550 0.9999066 -0.96716520 0.02866965
## 25 1.509690170 0.00000 0.000000e+00 5063.679 0.9999499 -4.84064851 -4.08000028
## 26 1.170887508 0.03225 8.200000e-03 4955.100 1.0000125 -1.14741183 0.06981578
## 27 -0.107181538 0.05200 1.050000e-02 4971.923 1.0000566 -5.84097162 -5.07193711
## 28 1.994107339 0.00400 1.000000e-03 5257.759 1.0000176 -0.73510840 0.37131706
## 29 1.272474270 0.00025 2.500000e-05 4409.177 0.9997518 -4.97692567 -4.23413718
## 30 1.189804639 0.08200 3.139062e-02 5201.672 1.0001217 -0.80065900 0.32442542
## 31 -0.569027745 0.00050 8.333333e-05 4830.164 0.9998641 -6.99648526 -5.99416465
## 32 -0.232526205 0.02125 5.527778e-03 5182.060 0.9997500 -1.49146819 -0.04611469
## 33 -0.770142514 0.00025 4.545455e-05 5001.958 0.9998030 -6.64371797 -5.72459045
## 34 0.678280270 0.58800 1.283241e-01 4668.201 0.9997748 -2.13823677 -0.77018561
## 35 -0.129099907 0.03600 6.777778e-03 5439.393 0.9997927 -5.23724861 -4.49122738
## 36 -0.802901636 0.01175 3.562500e-03 1658.449 0.9997710 -5.12773436 -2.30922799
## 37 0.418256355 0.82575 1.406500e-01 4820.927 1.0000209 -5.42796707 -4.68582016
## 38 0.347809883 0.07500 2.455357e-02 3288.742 0.9998232 -0.15878334 1.19736475
## 39 2.043633191 0.00000 0.000000e+00 4091.411 0.9997501 -4.33538469 -3.59449821
## 40 0.339799146 0.26025 8.273958e-02 6458.211 0.9999115 0.37413719 1.44243821
## 41 1.314842463 0.00000 0.000000e+00 5000.956 1.0002260 -4.69962115 -4.01304728
## 42 0.697524601 0.72025 1.831750e-01 5622.655 0.9999752 -0.87949469 0.26184492
## 43 0.413704376 0.81350 1.170259e-01 4197.125 0.9999760 -5.60232320 -4.82230577
## 44 0.326072004 0.19575 5.111250e-02 4953.893 0.9999792 -0.76973453 0.36538390
## 45 1.724650445 0.00000 0.000000e+00 4542.086 0.9999133 -4.53828869 -3.82212997
## 46 1.119820329 0.12375 4.350000e-02 6669.402 0.9999531 -0.54556570 0.63920081
## 47 -0.723752570 0.00000 0.000000e+00 5799.727 1.0002459 -6.62859084 -5.78883275
## 48 0.407401864 0.51675 1.106442e-01 5297.126 1.0008828 -3.36471236 -1.60589786
## 49 -0.981964853 0.00000 0.000000e+00 5666.606 0.9997554 -6.42438087 -5.55654861
## 50 1.712619969 0.60625 1.453929e-01 3096.247 0.9998278 -0.69309414 0.67627679
## 51 0.001981351 0.09425 1.730682e-02 4315.549 1.0002616 -5.47641541 -4.75648605
## 52 -0.759296871 0.00700 2.392857e-03 2898.335 1.0008208 -2.74683116 -0.85498719
## 53 -0.247686364 0.01700 2.133333e-03 4820.365 0.9997553 -6.45661301 -5.57265623
## 54 1.900919770 0.05600 1.254545e-02 5756.661 1.0000903 0.15042828 1.35109723
## 55 -0.004348855 0.07650 1.364286e-02 5405.766 0.9997821 -5.29849773 -4.55772137
## 56 1.541217415 0.25250 7.502174e-02 5943.765 0.9999870 -1.19153685 -0.10591760
## 57 0.200285213 0.33975 4.737500e-02 4855.585 0.9997861 -5.36179925 -4.62170153
## 58 1.001998000 0.37425 9.440000e-02 4453.938 1.0000085 -1.76171017 -0.63317498
## 59 -0.151674725 0.03600 8.315789e-03 5020.575 0.9997530 -5.81014918 -5.04923079
## 60 1.497681456 0.11725 3.904167e-02 5016.910 1.0001961 -1.11464141 -0.01018327
## v_upper v_pH0 v_FDR v_n_eff v_R_k_hat count_data
## 1 -3.01594734 0.00000 0.00000000 5409.314 0.9999002 c("10x_6....
## 2 0.64489741 0.29950 0.14586111 6159.312 0.9997500 c("10x_6....
## 3 -3.57864093 0.00000 0.00000000 3519.519 1.0005457 c("10x_6....
## 4 0.23494621 0.10400 0.05611111 4261.362 1.0008244 c("10x_6....
## 5 -2.44650791 0.00000 0.00000000 5312.461 0.9997632 c("10x_6....
## 6 0.28833540 0.11550 0.06205000 4377.167 1.0002416 c("10x_6....
## 7 -3.79514969 0.00000 0.00000000 6881.401 0.9997504 c("10x_6....
## 8 3.51682878 0.09825 0.05012500 3613.886 0.9997533 c("10x_6....
## 9 -3.70263597 0.00000 0.00000000 8045.754 0.9998078 c("10x_6....
## 10 1.96292805 0.24000 0.11041071 5328.321 0.9998238 c("10x_6....
## 11 -4.16504193 0.00000 0.00000000 7844.006 0.9997662 c("10x_6....
## 12 1.94510705 0.41900 0.20035870 4015.516 0.9997925 c("10x_6....
## 13 -2.25344017 0.00000 0.00000000 4686.363 1.0021748 c("10x_6....
## 14 1.28787048 0.49425 0.22224000 4874.766 1.0007906 c("10x_6....
## 15 -3.99899991 0.00000 0.00000000 5816.735 0.9997749 c("10x_6....
## 16 1.82008789 0.27650 0.13682353 5464.671 0.9998611 c("10x_6....
## 17 -3.22230735 0.00000 0.00000000 7598.794 0.9997555 c("10x_6....
## 18 2.19544084 0.05200 0.02680000 4961.944 0.9997587 c("10x_6....
## 19 -0.60663354 0.00375 0.00012500 3276.876 0.9998203 c("10x_6....
## 20 2.66022070 0.37450 0.15789474 4677.905 0.9999591 c("10x_6....
## 21 -2.67633748 0.00000 0.00000000 2524.829 1.0000877 c("10x_6....
## 22 -1.35504439 0.00050 0.00050000 3574.751 0.9999963 c("10x_6....
## 23 -2.73242973 0.00000 0.00000000 6046.399 0.9997716 c("10x_6....
## 24 1.07409430 0.62875 0.28440833 4146.001 0.9997579 c("10x_6....
## 25 -3.25130320 0.00000 0.00000000 6257.730 1.0001361 c("10x_6....
## 26 1.32194977 0.58200 0.25992857 5480.366 0.9997586 c("10x_6....
## 27 -4.17176192 0.00000 0.00000000 6200.257 0.9997548 c("10x_6....
## 28 1.54555210 0.38525 0.16926250 5547.914 0.9997650 c("10x_6....
## 29 -3.41533777 0.00000 0.00000000 4768.682 0.9997587 c("10x_6....
## 30 1.49281552 0.41350 0.19042045 4414.391 0.9997528 c("10x_6....
## 31 -4.93406855 0.00000 0.00000000 5913.986 1.0004125 c("10x_6....
## 32 1.42011291 0.58000 0.24800000 4469.144 1.0017238 c("10x_6....
## 33 -4.67247613 0.00000 0.00000000 4841.088 1.0010174 c("10x_6....
## 34 0.69247644 0.22100 0.07650000 4836.522 1.0013204 c("10x_6....
## 35 -3.47612063 0.00000 0.00000000 4929.612 0.9998365 c("10x_6....
## 36 0.67789815 0.08400 0.03633333 2328.391 0.9999460 c("10x_6....
## 37 -3.81334114 0.00000 0.00000000 6741.012 0.9997905 c("10x_6....
## 38 2.71988969 0.08475 0.04325000 3806.932 0.9997547 c("10x_6....
## 39 -2.74397225 0.00000 0.00000000 6197.531 0.9997701 c("10x_6....
## 40 2.58005633 0.01425 0.00737500 5120.894 0.9998826 c("10x_6....
## 41 -3.20985712 0.00000 0.00000000 6615.290 0.9997545 c("10x_6....
## 42 1.47759316 0.45350 0.21090625 4925.025 1.0000988 c("10x_6....
## 43 -3.94847912 0.00000 0.00000000 6388.722 1.0006200 c("10x_6....
## 44 1.62040948 0.39050 0.17979762 5114.924 0.9998885 c("10x_6....
## 45 -2.97726301 0.00000 0.00000000 6076.717 0.9999879 c("10x_6....
## 46 1.79098166 0.23375 0.10044231 6150.762 1.0000851 c("10x_6....
## 47 -4.82573753 0.00000 0.00000000 5072.326 0.9997768 c("10x_6....
## 48 -0.07313994 0.03675 0.02050000 5370.470 1.0000990 c("10x_6....
## 49 -4.51508835 0.00000 0.00000000 7366.782 1.0004126 c("10x_6....
## 50 2.18789925 0.24700 0.11951667 3798.662 1.0004409 c("10x_6....
## 51 -3.84185317 0.00000 0.00000000 6667.789 0.9997500 c("10x_6....
## 52 1.05148876 0.25675 0.12809375 3206.943 0.9997500 c("10x_6....
## 53 -4.58170979 0.00000 0.00000000 5372.081 0.9997860 c("10x_6....
## 54 2.67644598 0.03050 0.01508333 4389.058 0.9998464 c("10x_6....
## 55 -3.58871639 0.00000 0.00000000 6109.684 0.9999189 c("10x_6....
## 56 1.06580944 0.56000 0.23523077 4692.084 1.0000305 c("10x_6....
## 57 -3.71581854 0.00000 0.00000000 5725.641 0.9997870 c("10x_6....
## 58 0.51819715 0.23050 0.08933333 5026.768 1.0000341 c("10x_6....
## 59 -4.08480824 0.00000 0.00000000 5956.403 0.9998031 c("10x_6....
## 60 1.15204092 0.62550 0.27253448 5516.992 0.9997873 c("10x_6....
plots$credible_intervals_1D
## Warning: Unknown or uninitialised column: `credible_intervals_1D`.
## NULL
Plot 2D significance plot. Data points are cell groups. Error bars are the 95% credible interval. The dashed lines represent the default threshold fold change for which the probabilities (c_pH0, v_pH0) are calculated. pH0 of 0 represent the rejection of the null hypothesis that no effect is observed.
This plot is provided only if differential variability has been tested. The differential variability estimates are reliable only if the linear association between mean and variability for (intercept)
(left-hand side facet) is satisfied. A scatterplot (besides the Intercept) is provided for each category of interest. The for each category of interest, the composition and variability effects should be generally uncorrelated.
plots$credible_intervals_2D
## Warning: Unknown or uninitialised column: `credible_intervals_2D`.
## NULL
sessionInfo()
## R Under development (unstable) (2024-01-16 r85808)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 22.04.3 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.19-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_GB LC_COLLATE=C
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: America/New_York
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] rstan_2.32.5 StanHeaders_2.32.5 tidyr_1.3.0 forcats_1.0.0
## [5] ggplot2_3.4.4 sccomp_1.7.5 dplyr_1.1.4
##
## loaded via a namespace (and not attached):
## [1] tidyselect_1.2.0 farver_2.1.1
## [3] loo_2.6.0 bitops_1.0-7
## [5] SingleCellExperiment_1.25.0 RCurl_1.98-1.14
## [7] digest_0.6.34 dotCall64_1.1-1
## [9] lifecycle_1.0.4 SeuratObject_5.0.1
## [11] magrittr_2.0.3 compiler_4.4.0
## [13] rlang_1.1.3 tools_4.4.0
## [15] utf8_1.2.4 knitr_1.45
## [17] labeling_0.4.3 S4Arrays_1.3.2
## [19] sp_2.1-2 pkgbuild_1.4.3
## [21] curl_5.2.0 DelayedArray_0.29.0
## [23] abind_1.4-5 withr_3.0.0
## [25] purrr_1.0.2 BiocGenerics_0.49.1
## [27] grid_4.4.0 stats4_4.4.0
## [29] fansi_1.0.6 colorspace_2.1-0
## [31] future_1.33.1 inline_0.3.19
## [33] progressr_0.14.0 globals_0.16.2
## [35] scales_1.3.0 SummarizedExperiment_1.33.2
## [37] cli_3.6.2 crayon_1.5.2
## [39] generics_0.1.3 RcppParallel_5.1.7
## [41] future.apply_1.11.1 tzdb_0.4.0
## [43] stringr_1.5.1 zlibbioc_1.49.0
## [45] parallel_4.4.0 XVector_0.43.1
## [47] matrixStats_1.2.0 vctrs_0.6.5
## [49] V8_4.4.1 boot_1.3-28.1
## [51] Matrix_1.6-5 jsonlite_1.8.8
## [53] IRanges_2.37.1 hms_1.1.3
## [55] patchwork_1.2.0 S4Vectors_0.41.3
## [57] ggrepel_0.9.5 listenv_0.9.0
## [59] glue_1.7.0 parallelly_1.36.0
## [61] spam_2.10-0 codetools_0.2-19
## [63] stringi_1.8.3 gtable_0.3.4
## [65] GenomeInfoDb_1.39.5 QuickJSR_1.1.0
## [67] GenomicRanges_1.55.1 munsell_0.5.0
## [69] tibble_3.2.1 pillar_1.9.0
## [71] GenomeInfoDbData_1.2.11 R6_2.5.1
## [73] evaluate_0.23 lattice_0.22-5
## [75] Biobase_2.63.0 highr_0.10
## [77] readr_2.1.5 rstantools_2.3.1.1
## [79] Rcpp_1.0.12 gridExtra_2.3
## [81] SparseArray_1.3.3 xfun_0.41
## [83] MatrixGenerics_1.15.0 pkgconfig_2.0.3