Run scTenifoldNet network comparison
Usage
RunscTenifoldNet(
object,
y = NULL,
group.by = NULL,
condition1 = NULL,
condition2 = NULL,
assay = NULL,
layer = "counts",
features = NULL,
qc = TRUE,
qc_min_library_size = 1000,
qc_remove_outlier_cells = TRUE,
qc_min_pct = 0.05,
qc_max_mt_ratio = 0.1,
nc_nNet = 10,
nc_nCells = 500,
nc_nComp = 3,
nc_symmetric = FALSE,
nc_scaleScores = TRUE,
nc_q = 0.05,
td_K = 3,
td_nDecimal = 1,
td_maxIter = 1000,
td_maxError = 1e-05,
ma_nDim = 30,
cores = 1,
store_networks = TRUE,
store_manifold = TRUE,
tool_name = "scTenifoldNet",
verbose = TRUE
)Arguments
- object
A
Seuratobject or a raw count matrix with genes in rows and cells in columns.- y
A second raw count matrix. Required when
objectis a matrix and ignored whenobjectis aSeuratobject.- group.by
Metadata column used to split a
Seuratobject into the two conditions being compared.- condition1, condition2
Condition labels from
group.by. If omitted, the first two group levels are used.- assay, layer
Assay and layer used as the count matrix when
objectis aSeuratobject.- features
Optional genes to retain before running the comparison.
- qc
Whether to apply scTenifoldNet-style quality control.
- qc_min_library_size, qc_remove_outlier_cells, qc_min_pct, qc_max_mt_ratio
Quality-control parameters forwarded to
scTenifoldNet::scQC().- nc_nNet, nc_nCells, nc_nComp, nc_symmetric, nc_scaleScores, nc_q
Network construction parameters forwarded to
scTenifoldNet::makeNetworks().- td_K, td_nDecimal, td_maxIter, td_maxError
Tensor decomposition parameters forwarded to
scTenifoldNet::tensorDecomposition().- ma_nDim
Manifold-alignment dimension forwarded to
scTenifoldNet::manifoldAlignment().- cores
Number of cores forwarded to
scTenifoldNet::scTenifoldNet().- store_networks
Whether to keep tensor networks in the stored result when
objectis aSeuratobject.- store_manifold
Whether to keep manifold-alignment coordinates in the stored result when
objectis aSeuratobject.- tool_name
Name of the
object@toolsentry whenobjectis aSeuratobject.- verbose
Whether to print the message. Default is
TRUE.
Value
A scTenifoldNet result list for matrix input, or a Seurat object
with results stored in object@tools[[tool_name]].
Examples
data(pancreas_sub)
counts <- GetAssayData5(pancreas_sub, assay = "RNA", layer = "counts")
detected <- names(sort(Matrix::rowSums(counts > 0), decreasing = TRUE))
features_use <- head(detected, 300)
pancreas_sub <- RunscTenifoldNet(
pancreas_sub,
group.by = "CellType",
condition1 = "Ductal",
condition2 = "Endocrine",
features = features_use,
qc = FALSE,
nc_nNet = 3,
nc_nCells = 200,
td_maxIter = 200,
ma_nDim = 2,
store_networks = FALSE,
store_manifold = TRUE
)
#> ℹ [2026-07-02 10:10:57] Run scTenifoldNet comparison using upstream implementation
#>
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#> ✔ [2026-07-02 10:11:02] scTenifoldNet results stored in `object@tools[[scTenifoldNet]]`
dr <- pancreas_sub@tools$scTenifoldNet$diffRegulation
head(dr)
#> gene distance Z FC p.value p.adj
#> 25 Hsp90ab1 0.004553045 1.660546 20.824368 5.033841e-06 0.001510152
#> 26 Rps2 0.004154439 1.590437 17.337751 3.129086e-05 0.004693629
#> 125 Hspa8 0.003136144 1.383049 9.880067 1.670788e-03 0.167078817
#> 47 Ftl1 0.002977463 1.346001 8.905553 2.843048e-03 0.193654167
#> 177 Hsp90aa1 0.002938524 1.336669 8.674141 3.227569e-03 0.193654167
#> 155 Sec61b 0.002786133 1.299164 7.797794 5.231006e-03 0.240336435
scTenifoldNetPlot(pancreas_sub, plot_type = "effect")
