The ScMaSigPro object

Two resolutions and the results

An analysis works at two resolutions: the cells, with their pseudotime and path, and the pseudotime bins whose pseudo-bulk counts the models are fitted to. A ScMaSigPro object holds both as SingleCellExperiment objects, one per slot, because their columns differ: altExp() needs the columns of the main experiment, and bins kept in metadata() would lose the checks and the accessors of an experiment. The other slots hold the results of the steps, which have no place in a SingleCellExperiment, and a record of every setting. Every step returns the object with one more slot filled, and checks it with validObject().

Slot	Class	Filled by	Read with
`Sparse`	`SingleCellExperiment`	`as_scmp()`, `create_scmp()`	`cSparse()`, `eSparse()`
`Dense`	`SingleCellExperiment`	`sc.squeeze()`, `sc.restruct()`	`cDense()`, `eDense()`
`Design`	`MatrixDesign`	`sc.set.poly()`	`predictors()`, `pathAssign()`, `showPoly()`
`Profile`	`VariableProfiles`	`sc.p.vector()`	`scmpResults()`
`Estimate`	`Estimates`	`sc.t.fit()`	`showSol()`, `showCoeff()`, `showGroupCoeff()`, `showTS()`, `showInflu()`, `showSigProf()`
`Significant`	`Significant`	`sc.filter()`, `sc.cluster.trend()`	`scmpResults()`
`Parameters`	`ParameterConfig`	every step	`showParams()`
`version`	`character`	the creation of the object	`updateObject()`

The bundled object scmp.ob holds the analysis of the Quick start vignette; printing it summarises every step that has run:

library(scMaSigPro)
data("scmp.ob", package = "scMaSigPro")
scmp.ob
#> Class: ScMaSigProClass
#> nCells: 200
#> nFeatures: 100
#> Pseudotime Range: 1 100
#> Branching Paths: Path2, Path1
#> Binned Pseudotime: 1-8(Range), 4.5(Mean), 
#> Number of bins-> Path1: 8 Path2: 8
#> Average bin Size-> Path1: 12 Path2: 13
#> Polynomial Order: 1
#> No. of Significant Profiles: 50
#> No. of Influential Features: 8

Cells and bins

cSparse() and cDense() return the metadata of the cells and of the bins as data frames, and eSparse() and eDense() their counts, by default the counts and bulk.counts assays. Each has a replacement form (cSparse<- and so on) that checks the new value and the object.

head(cSparse(scmp.ob)[, c("Pseudotime", "Path", "scmp_binned_pseudotime")], 3)
#>       Pseudotime  Path scmp_binned_pseudotime
#> Cell1         42 Path2                      4
#> Cell2         41 Path1                      4
#> Cell3         23 Path1                      2
bins <- cDense(scmp.ob)
head(bins[, c("Path", "scmp_binned_pseudotime", "scmp_bin_size")], 3)
#>              Path scmp_binned_pseudotime scmp_bin_size
#> Path1_bin_1 Path1                      1            14
#> Path1_bin_2 Path1                      2            15
#> Path1_bin_3 Path1                      3             7
eSparse(scmp.ob)[1:3, 1:4]
#>       Cell1 Cell2 Cell3 Cell4
#> Gene1     0     2     1     2
#> Gene2   436   371   432   427
#> Gene3    35    13    17    18
eDense(scmp.ob)[1:3, 1:4]
#>       Path1_bin_1 Path1_bin_2 Path1_bin_3 Path1_bin_4
#> Gene1          16          24          11          31
#> Gene2        6335        7337        3375        9161
#> Gene3         201         245         105         216

The names of the columns are kept in the parameters (ptime_col, path_col, bin_col and others), which showParams() lists.

The design

predictors() returns the design of the model, one row per bin and one column per term besides the intercept; pathAssign() the binned pseudotime and the path of each bin; showPoly() the model as a formula.

showPoly(scmp.ob)
#> [1] "beta0 + beta1*Path2vsPath1 + beta2*scmp_binned_pseudotime + beta3*scmp_binned_pseudotimexPath2"
head(predictors(scmp.ob), 3)
#>             Path2vsPath1 scmp_binned_pseudotime scmp_binned_pseudotimexPath2
#> Path1_bin_1            0                      1                            0
#> Path1_bin_2            0                      2                            0
#> Path1_bin_3            0                      3                            0
head(pathAssign(scmp.ob), 3)
#>             scmp_binned_pseudotime Replicate Path1 Path2
#> Path1_bin_1                      1         1     1     0
#> Path1_bin_2                      2         2     1     0
#> Path1_bin_3                      3         3     1     0

The results

scmpResults() gathers the results of every gene in one table: the test of the full model, the selected model, the gene lists and the cluster. scmpToRowData() writes the same columns into the rowData() of a SingleCellExperiment. The show*() readers return the parts of the selected models: showSol() their p-values and R-squared, showCoeff() their coefficients, showGroupCoeff() the coefficients of the polynomial of each path, showTS() the t statistics, showInflu() the bins with influential observations and showSigProf() the pseudo-bulk counts of the genes with a selected model.

head(scmpResults(scmp.ob), 3)
#> DataFrame with 3 rows and 9 columns
#>           p_value adj_p_value  non_flat model_p_value r_squared influential
#>         <numeric>   <numeric> <logical>     <numeric> <numeric>   <logical>
#> Gene1 2.21646e-01 3.46322e-01     FALSE            NA        NA       FALSE
#> Gene2 1.30835e-13 4.84576e-13      TRUE   1.60139e-09  0.726015       FALSE
#> Gene3 2.51249e-24 1.14204e-23      TRUE   3.98589e-20  0.873163       FALSE
#>       sig_Path2vsPath1 sig_Path1   cluster
#>              <logical> <logical> <integer>
#> Gene1            FALSE     FALSE        NA
#> Gene2             TRUE     FALSE         1
#> Gene3             TRUE      TRUE         2
head(showSol(scmp.ob), 3)
#>            p-value R-squared p.valor_beta0 p.valor_Path2vsPath1
#> Gene2 1.601390e-09 0.7260152  3.214402e-32                   NA
#> Gene3 3.985890e-20 0.8731628  1.206436e-18                   NA
#> Gene4 1.230465e-66 0.9553623  3.898555e-28                   NA
#>       p.valor_scmp_binned_pseudotime p.valor_scmp_binned_pseudotimexPath2
#> Gene2                             NA                         2.584140e-05
#> Gene3                   1.880938e-06                         6.631152e-07
#> Gene4                             NA                         1.143322e-10
head(showGroupCoeff(scmp.ob), 3)
#>       Path1_beta0 Path1_beta1 Path2_beta0 Path2_beta1
#> Gene2    8.681729   0.0000000    8.681729 -0.02510003
#> Gene3    5.385045  -0.1262038    5.385045 -0.01183913
#> Gene4    6.886018   0.0000000    6.886018  0.10643888

The settings

showParams() lists every setting that the steps used, and the steps take their defaults from them: sc.t.fit() uses the family, the link and the level of sc.p.vector() unless told otherwise.

showParams(scmp.ob)
#>            parameters                  value
#> 1       bin_ptime_col scmp_binned_pseudotime
#> 2         path_prefix                   Path
#> 3          root_label                   root
#> 4           ptime_col             Pseudotime
#> 5    selection_method               backward
#> 6          bin_method                Sturges
#> 7            path_col                   Path
#> 8             bin_col               scmp_bin
#> 9        bin_size_col          scmp_bin_size
#> 10        bin_mem_col       scmp_bin_members
#> 11           anno_col              cell_type
#> 12                  g                    100
#> 13            p_value                   0.05
#> 14             min_na                      6
#> 15      mt_correction                     BH
#> 16               link                    log
#> 17            epsilon                  1e-08
#> 18             offset                   TRUE
#> 19             max_it                    100
#> 20        poly_degree                      1
#> 21     cluster_method                 hclust
#> 22            use_dim                    col
#> 23            fill_na                   zero
#> 24         min_counts                      0
#> 25 min_bins_expressed                      0
#> 26               test                  chisq
#> 27       distribution  Negative Binomial(10)

Objects from earlier versions

The version slot records the version of scMaSigPro that created an object. An object saved by an earlier version, with saveRDS() or save(), may lack a slot or a setting that the current version needs; the steps then stop and ask for updateObject(), which adds what is missing and says what it changed:

scmp_ob <- updateObject(readRDS("analysis.rds"))

A current object is returned unchanged:

identical(updateObject(scmp.ob), scmp.ob)
#> [1] FALSE
validObject(scmp.ob)
#> [1] TRUE

Equivalence with maSigPro

scMaSigPro fits the models of maSigPro (Conesa et al. 2006; Nueda et al. 2014) to pseudo-bulk counts. Its design, tests, stepwise selection and gene lists are implemented anew, without calling maSigPro, and with the settings of maSigPro, without the offset, they give the same results. This part runs both packages on the data of maSigPro and checks that every result is identical. It runs when maSigPro is installed:

BiocManager::install("maSigPro")

A Gaussian model on microarray data

data.abiotic holds the expression of 1000 genes in a time-course microarray experiment on potato plants: a control and three abiotic stresses (cold, heat and salt), at 3, 9 and 27 hours, with three replicates each, 36 samples in all. edesign.abiotic describes the samples with one 0/1 column per group; scMaSigPro takes one column with the group instead.

library(maSigPro)
data("data.abiotic", "edesign.abiotic", package = "maSigPro")
head(edesign.abiotic, 3)
#>              Time Replicate Control Cold Heat Salt
#> Control_3H_1    3         1       1    0    0    0
#> Control_3H_2    3         1       1    0    0    0
#> Control_3H_3    3         1       1    0    0    0

samples <- as.data.frame(edesign.abiotic)
groups <- c("Control", "Cold", "Heat", "Salt")
samples$Group <- groups[max.col(samples[, groups], ties.method = "first")]
samples <- samples[, c("Time", "Replicate", "Group")]
head(samples, 3)
#>              Time Replicate   Group
#> Control_3H_1    3         1 Control
#> Control_3H_2    3         1 Control
#> Control_3H_3    3         1 Control

Each sample is a bin of its own (use_as_bin = TRUE), so sc.squeeze() is not needed. The design of sc.set.poly() is that of make.design.matrix():

scmp_ab <- create_scmp(
    counts = as.matrix(data.abiotic),
    cell_data = samples,
    ptime_col = "Time",
    path_col = "Group",
    use_as_bin = TRUE
)
scmp_ab <- sc.set.poly(scmp_ab, poly_degree = 2)
design <- make.design.matrix(edesign.abiotic, degree = 2)

stopifnot(
    identical(predictors(scmp_ab), design$dis),
    all.equal(pathAssign(scmp_ab), as.matrix(design$edesign))
)

sc.p.vector() with the Gaussian family, the identity link and no offset is p.vector(), and sc.t.fit() is T.fit(). maSigPro prints its progress; capture.output() keeps it out of the vignette.

invisible(capture.output(
    fit <- p.vector(data.abiotic, design,
        Q = 0.05, MT.adjust = "BH", min.obs = 20
    )
))
scmp_ab <- sc.p.vector(scmp_ab,
    family = gaussian(), link = "identity", offset = FALSE,
    min_na = 20, epsilon = 1e-5, max_it = 25, verbose = FALSE
)

invisible(capture.output(
    tstep <- T.fit(fit, step.method = "backward", alfa = 0.05)
))
scmp_ab <- sc.t.fit(scmp_ab, verbose = FALSE)

res <- scmpResults(scmp_ab)
fitted <- rownames(fit$p.vector)
stopifnot(
    identical(res[fitted, "p_value"], unname(fit$p.vector[, 1])),
    identical(res[fitted, "adj_p_value"], fit$p.adjusted),
    identical(rownames(res)[res$non_flat], rownames(fit$SELEC)),
    identical(showSol(scmp_ab), tstep$sol),
    identical(showCoeff(scmp_ab), tstep$coefficients),
    identical(showGroupCoeff(scmp_ab), tstep$group.coeffs),
    identical(as.data.frame(showTS(scmp_ab)), tstep$t.score)
)
sum(res$non_flat)
#> [1] 398

The gene lists of sc.filter() are those of get.siggenes(); maSigPro pads its lists with blanks to the same length:

sigs <- get.siggenes(tstep, rsq = 0.6, vars = "groups")
scmp_ab <- sc.filter(scmp_ab, rsq = 0.6, vars = "groups")
res <- scmpResults(scmp_ab)
for (list in colnames(sigs$summary)) {
    expected <- sigs$summary[[list]]
    expected <- expected[expected != " "]
    found <- rownames(res)[res[[paste0("sig_", list)]]]
    stopifnot(identical(found, expected))
}
colSums(as.data.frame(res[, grep("^sig_", colnames(res))]))
#> sig_ColdvsControl sig_HeatvsControl sig_SaltvsControl       sig_Control 
#>               223               130               191                86

plotTrend() plays the part of PlotGroups() of maSigPro: the expression of one gene in each group over time, with the fitted curve of each group.

plotTrend(scmp_ab, "STMDE66",
    logs = FALSE, pseudoCount = 0, summary_mode = "mean",
    significant = FALSE, lines = TRUE
)

Expression of the gene STMDE66 over time in the four groups of data.abiotic, with the fitted curve of each group, drawn by scMaSigPro.

A negative binomial model on counts

NBdata holds counts of 100 genes simulated with a negative binomial distribution, in two groups with six time points and three replicates each; the first 20 genes change over time. With the negative binomial family, theta 10 and no offset, sc.p.vector() and sc.t.fit() are p.vector(counts = TRUE) and T.fit():

data("NBdata", "NBdesign", package = "maSigPro")
samples <- as.data.frame(NBdesign)
samples$Group <- c("Group.1", "Group.2")[
    max.col(samples[, 3:4], ties.method = "first")
]
samples <- samples[, c("Time", "Replicates", "Group")]

nb_family <- MASS::negative.binomial(theta = 10)
design <- make.design.matrix(NBdesign, degree = 2)
invisible(capture.output(
    fit <- p.vector(NBdata, design,
        Q = 0.05, MT.adjust = "BH", min.obs = 6, counts = TRUE, theta = 10
    )
))
invisible(capture.output(
    tstep <- T.fit(fit,
        step.method = "backward", alfa = 0.05, family = nb_family,
        epsilon = 1e-5
    )
))

scmp_nb <- create_scmp(
    counts = as.matrix(NBdata),
    cell_data = samples,
    ptime_col = "Time",
    path_col = "Group",
    use_as_bin = TRUE
)
scmp_nb <- sc.set.poly(scmp_nb, poly_degree = 2)
scmp_nb <- sc.p.vector(scmp_nb,
    family = nb_family, offset = FALSE, min_na = 6, epsilon = 1e-5,
    max_it = 25, verbose = FALSE
)
scmp_nb <- sc.t.fit(scmp_nb, verbose = FALSE)

res <- scmpResults(scmp_nb)
stopifnot(
    identical(res$p_value, unname(fit$p.vector[, 1])),
    identical(res$adj_p_value, fit$p.adjusted),
    identical(rownames(res)[res$non_flat], rownames(fit$SELEC)),
    identical(showSol(scmp_nb), tstep$sol),
    identical(showCoeff(scmp_nb), tstep$coefficients)
)
sum(res$non_flat)
#> [1] 21

What scMaSigPro adds

On single cells, scMaSigPro adds what the cells need before the model of maSigPro applies (Srivastava et al. 2024): the bins along pseudotime (sc.squeeze()), the shared root (sc.restruct()), the size-factor offset for the bins, the choice of the reference path, and the estimation of theta. It also fits the genes in parallel with BiocParallel, offers the F test of the full model, and returns the results as a DataFrame or in the rowData() of the data. The Model and tuning vignette describes these settings.

Session information

sessionInfo()
#> R version 4.6.1 (2026-06-24)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 26.04.1 LTS
#> 
#> Matrix products: default
#> BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
#> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.32.so;  LAPACK version 3.12.0
#> 
#> locale:
#>  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
#>  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
#>  [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: Etc/UTC
#> tzcode source: system (glibc)
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#> [1] maSigPro_1.85.0  scMaSigPro_0.6.0 BiocStyle_2.41.0
#> 
#> loaded via a namespace (and not attached):
#>  [1] sass_0.4.10                 generics_0.1.4             
#>  [3] SparseArray_1.13.4          lattice_0.23-1             
#>  [5] digest_0.6.39               RColorBrewer_1.1-3         
#>  [7] evaluate_1.0.5              grid_4.6.1                 
#>  [9] fastmap_1.2.0               jsonlite_2.0.0             
#> [11] Matrix_1.7-6                mclust_6.1.3               
#> [13] BiocManager_1.30.27         SingleCellExperiment_1.35.2
#> [15] scales_1.4.0                codetools_0.2-20           
#> [17] jquerylib_0.1.4             abind_1.4-8                
#> [19] cli_3.6.6                   rlang_1.3.0                
#> [21] XVector_0.53.0              Biobase_2.73.2             
#> [23] withr_3.0.3                 cachem_1.1.0               
#> [25] DelayedArray_0.39.8         yaml_2.3.12                
#> [27] otel_0.2.0                  S4Arrays_1.13.2            
#> [29] tools_4.6.1                 parallel_4.6.1             
#> [31] BiocParallel_1.47.0         ggplot2_4.0.3              
#> [33] SummarizedExperiment_1.43.0 BiocGenerics_0.59.12       
#> [35] vctrs_0.7.3                 buildtools_1.0.0           
#> [37] R6_2.6.1                    matrixStats_1.5.0          
#> [39] stats4_4.6.1                lifecycle_1.0.5            
#> [41] Seqinfo_1.3.2               admisc_0.41                
#> [43] S4Vectors_0.51.10           IRanges_2.47.5             
#> [45] MASS_7.3-66                 bslib_0.12.0               
#> [47] gtable_0.3.6                glue_1.8.1                 
#> [49] xfun_0.62                   GenomicRanges_1.65.4       
#> [51] sys_3.4.3                   MatrixGenerics_1.25.0      
#> [53] knitr_1.52                  farver_2.1.2               
#> [55] htmltools_0.5.9             labeling_0.4.3             
#> [57] rmarkdown_2.32              maketools_1.3.2            
#> [59] compiler_4.6.1              S7_0.2.2                   
#> [61] venn_1.13

References

Conesa, Ana, María José Nueda, Alberto Ferrer, and Manuel Talón. 2006. “maSigPro: A Method to Identify Significantly Differential Expression Profiles in Time-Course Microarray Experiments.” Bioinformatics 22 (9): 1096–102. https://doi.org/10.1093/bioinformatics/btl056.

Nueda, María José, Sonia Tarazona, and Ana Conesa. 2014. “Next maSigPro: Updating maSigPro Bioconductor Package for RNA-seq Time Series.” Bioinformatics 30 (18): 2598–602. https://doi.org/10.1093/bioinformatics/btu333.

Srivastava, Priyansh, Marta Benegas Coll, Stefan Götz, María José Nueda, and Ana Conesa. 2024. “scMaSigPro: Differential Expression Analysis Along Single-Cell Trajectories.” Bioinformatics 40 (7): btae443. https://doi.org/10.1093/bioinformatics/btae443.

maSigPro and the ScMaSigPro class