Package {SCORPION}

Type:	Package
Title:	Single Cell Oriented Reconstruction of PANDA Individually Optimized Networks
Version:	1.3.3
Description:	Constructs cell-type-specific gene regulatory networks from single-cell RNA-sequencing data. The method implements the SCORPION algorithm, which first aggregates individual cells into super-cells and then applies PANDA (Passing Attributes between Networks for Data Assimilation) to infer transcription factor-target regulatory relationships. It also provides statistical methods for differential edge analysis.
License:	GPL-3
Encoding:	UTF-8
LazyData:	true
Depends:	R (≥ 3.5.0)
Imports:	cli, methods, irlba, igraph, RANN, Matrix, pbapply, dplyr, furrr, future
Suggests:	RhpcBLASctl, testthat, mori, circlize, biomaRt, fgsea
URL:	https://github.com/kuijjerlab/SCORPION
BugReports:	https://github.com/kuijjerlab/SCORPION/issues
RoxygenNote:	7.3.3
NeedsCompilation:	no
Packaged:	2026-09-02 14:32:26 UTC; dcosorioh
Author:	Daniel Osorio [aut, cre], Marieke L. Kuijjer [aut]
Maintainer:	Daniel Osorio <daniecos@uio.no>
Repository:	CRAN
Date/Publication:	2026-09-02 14:50:02 UTC

Draw effect-size, novelty and gene-set legends on a Circos plot.

Description

Draw effect-size, novelty and gene-set legends on a Circos plot.

Usage

.drawCircosLegends(
  col_fun,
  valueRange,
  colorBy,
  hasPrior,
  knownColor,
  novelColor,
  set_colors,
  lwdRange = NULL,
  widthShown = FALSE,
  degCol_fun = NULL,
  degRange = NULL,
  degColBy = "log2FoldChange"
)

Download gene coordinates from Ensembl via biomaRt.

Description

Download gene coordinates from Ensembl via biomaRt.

Usage

.fetchGeneCoords(genes, species, mirror)

Natural-sort chromosome names (1..N, then X, Y, MT, then the rest).

Description

Natural-sort chromosome names (1..N, then X, Y, MT, then the rest).

Usage

.orderChr(chr)

Parse a GMT gene-set file into a named list.

Description

Parse a GMT gene-set file into a named list.

Usage

.parseGMT(path)

Validate a user-supplied gene coordinate table.

Description

Validate a user-supplied gene coordinate table.

Usage

.validateGeneCoords(geneCoords)

Circos plot of differential network edges

Description

Draws a circular (Circos) plot of transcription factor to target links from a testEdges two-sample result. Genes are placed on their genomic coordinates, links are coloured continuously by significance, flagged as known or novel against an optional a priori network, and genes belonging to supplied gene sets can be labelled around the circle.

Usage

circosEdges(
  edgesDF,
  species = "hsapiens_gene_ensembl",
  geneCoords = NULL,
  priorNet = NULL,
  geneSets = NULL,
  pAdjThreshold = 0.05,
  log2FCThreshold = 0,
  maxEdges = 500L,
  colorBy = "log2FoldChange",
  linkColors = c("#2166AC", "#F7F7F7", "#B2182B"),
  lwdRange = c(0.5, 4),
  nmaxTF = 20L,
  nmaxTarget = 20L,
  knownColor = "grey60",
  novelColor = "#D95F02",
  geneSetColors = NULL,
  chromosomes = NULL,
  mainChromosomesOnly = TRUE,
  ensemblMirror = "www",
  transparency = 0.5,
  hRatio = 0.6,
  fontFamily = "sans",
  legend = TRUE
)

Arguments

Details

Requires the circlize package, and biomaRt when gene coordinates are downloaded automatically (geneCoords = NULL). Genes without coordinates, and links whose TF or target lacks coordinates, are dropped with a message.

Value

Invisibly, a list with edges (the plotted links annotated with coordinates and novelty) and coords (the gene coordinate table with outDegree, inDegree and total degree columns, each the sum of log2FoldChange over a gene's outgoing / incoming links). The function is called for the side effect of drawing the plot.

Author(s)

Daniel Osorio <daniecos@uio.no>

See Also

testEdges, runSCORPION

Examples

## Not run: 
data(scorpionTest)
nets <- runSCORPION(
  gexMatrix = scorpionTest$gex,
  tfMotifs = scorpionTest$tf,
  ppiNet = scorpionTest$ppi,
  cellsMetadata = scorpionTest$metadata,
  groupBy = c("donor", "region")
)
res <- testEdges(
  networksDF = nets,
  testType = "two.sample",
  group1 = grep("--T$", colnames(nets), value = TRUE),
  group2 = grep("--N$", colnames(nets), value = TRUE)
)

# Human coordinates auto-downloaded from Ensembl, known/novel vs a prior net
circosEdges(
  edgesDF = res,
  species = "hsapiens_gene_ensembl",
  priorNet = scorpionTest$tf,
  geneSets = "hallmark.gmt"
)

## End(Not run)

Gene set enrichment analysis of TF-target edges

Description

Performs gene set enrichment analysis separately for each transcription factor (TF) using the edge-level values supplied in numericValue. Enrichment is performed with the multilevel implementation of fgsea. Calculations for individual TFs are performed in parallel.

Usage

enrichEdges(edgesDF, geneSets, numericValue, nCores = 3, seed = 1)

Arguments

Details

For each TF, the values in numericValue are used as ranked statistics for its target genes. Edges with missing targets or missing or non-finite values in the selected numeric column are excluded before enrichment analysis. If a target occurs more than once for a TF, only the observation with the largest absolute value of the selected ranking statistic is retained.

Gene set enrichment is performed using fgsea::fgseaMultilevel; the fgsea package (Bioconductor) is required. The leadingEdge column returned by fgseaMultilevel is not included in the output. P-values are adjusted across all TF-gene set enrichment tests using the Benjamini-Hochberg procedure.

Value

A data.frame of enrichment results with one row per TF-gene set pair:

• tf: Transcription factor

• geneSet: Gene set identifier

• pValue: Raw enrichment p-value

• pAdj: Benjamini-Hochberg adjusted p-value

• log2Err: Expected log2 error of the p-value estimate

• ES: Enrichment score

• NES: Normalized enrichment score

• geneSetSize: Number of genes from the set found among the targets

Author(s)

Daniel Osorio <daniecos@uio.no>

See Also

testEdges, maEdges

Examples

## Not run: 
data(scorpionTest)
nets <- runSCORPION(
  gexMatrix = scorpionTest$gex,
  tfMotifs = scorpionTest$tf,
  ppiNet = scorpionTest$ppi,
  cellsMetadata = scorpionTest$metadata,
  groupBy = c("donor", "region")
)
res <- testEdges(
  networksDF = nets,
  testType = "two.sample",
  group1 = grep("--T$", colnames(nets), value = TRUE),
  group2 = grep("--N$", colnames(nets), value = TRUE)
)

geneSets <- list(SetA = c("ACKR1", "ACTA2"), SetB = c("ACTG2", "ADAMDEC1"))
enr <- enrichEdges(
  edgesDF = res,
  geneSets = geneSets,
  numericValue = "log2FoldChange"
)

## End(Not run)

Meta-analysis of TF-target edges across studies

Description

Performs a meta-analysis of TF-target edges across multiple studies using either a fixed-effect or DerSimonian-Laird random-effects model. Missing or non-finite effect sizes and standard errors are excluded from the corresponding study. A TF-target pair is only counted as contributing to a study when both its effect size and SE are valid.

Usage

maEdges(
  edgesList,
  method = c("random", "fixed"),
  minStudies = 2L,
  padjustMethod = "BH",
  moderateVariance = TRUE,
  s0 = NULL
)

Arguments

Value

A data.frame containing:

• tf: Transcription factor

• target: Target gene

• k: Number of studies contributing to the meta-analysis

• log2FoldChange: Meta-analytic effect size

• SE: Meta-analysis standard error

• ciLow: Lower bound of the 95% confidence interval

• ciHigh: Upper bound of the 95% confidence interval

• zStatistic: Test statistic

• pValue: Raw p-value

• pAdj: Adjusted p-value

• Q: Cochran's Q heterogeneity statistic

• iSquared: I-squared heterogeneity (percentage)

• tauSquared: DerSimonian-Laird between-study variance

Author(s)

Daniel Osorio <daniecos@uio.no>

See Also

runSCORPION, testEdges, circosEdges

Regression analysis of edges across ordered conditions

Description

Performs linear regression on network edges from runSCORPION output to identify edges that show significant trends across ordered conditions (e.g., disease progression: Normal -> Border -> Tumor).

Usage

regressEdges(networksDF, orderedGroups, padjustMethod = "BH", minMeanEdge = 0)

Arguments

Details

This function performs simple linear regression for each edge, modeling edge weight as a function of an ordered categorical variable (coded as 0, 1, 2, ... for each condition level).

The slope coefficient indicates the average change in edge weight per step along the ordered progression. Positive slopes indicate increasing edge weights, negative slopes indicate decreasing edge weights.

The function uses vectorized computations for efficiency with large datasets.

Value

A data.frame containing:

• tf: Transcription factor

• target: Target gene

• slope: Regression slope (change in edge weight per condition step)

• intercept: Regression intercept

• rSquared: R-squared value (proportion of variance explained)

• fStatistic: F-statistic for the regression

• pValue: Raw p-value for the slope

• pAdj: Adjusted p-value

• meanEdge: Overall mean edge weight across all conditions

• One column per condition showing mean edge weight in that condition

Author(s)

Daniel Osorio <daniecos@uio.no>

See Also

runSCORPION, testEdges

Examples

## Not run: 
# Load test data and build networks by donor and region
# Note: T = Tumor, N = Normal, B = Border regions
data(scorpionTest)
nets <- runSCORPION(
  gexMatrix = scorpionTest$gex,
  tfMotifs = scorpionTest$tf,
  ppiNet = scorpionTest$ppi,
  cellsMetadata = scorpionTest$metadata,
  groupBy = c("donor", "region")
)

# Define ordered progression: Normal -> Border -> Tumor
normal_nets <- grep("--N$", colnames(nets), value = TRUE)
border_nets <- grep("--B$", colnames(nets), value = TRUE)
tumor_nets <- grep("--T$", colnames(nets), value = TRUE)

ordered_conditions <- list(
  Normal = normal_nets,
  Border = border_nets,
  Tumor = tumor_nets
)

# Perform regression analysis
results_regression <- regressEdges(
  networksDF = nets,
  orderedGroups = ordered_conditions
)

# View top edges with strongest trends
head(results_regression[order(results_regression$pAdj), ])

# Edges with positive slopes (increasing from N to T)
increasing <- results_regression[results_regression$pAdj < 0.05 &
                                  results_regression$slope > 0, ]
print(paste("Edges increasing along N->B->T:", nrow(increasing)))

# Edges with negative slopes (decreasing from N to T)
decreasing <- results_regression[results_regression$pAdj < 0.05 &
                                  results_regression$slope < 0, ]
print(paste("Edges decreasing along N->B->T:", nrow(decreasing)))

# Filter by minimum edge weight and R-squared
strong_trends <- results_regression[results_regression$pAdj < 0.05 &
                                     results_regression$rSquared > 0.7 &
                                     abs(results_regression$meanEdge) > 0.1, ]

## End(Not run)

Run SCORPION across cell groups and return combined networks

Description

Builds per-group regulatory networks by running scorpion on subsets of cells defined by cellsMetadata and combining the resulting networks into a wide-format data frame where each column corresponds to a network.

Usage

runSCORPION(
  gexMatrix,
  tfMotifs,
  ppiNet,
  cellsMetadata,
  groupBy,
  normalizeData = TRUE,
  removeBatchEffect = FALSE,
  batch = NULL,
  minCells = 30,
  computingEngine = "cpu",
  nCores = 1,
  gammaValue = 10,
  nPC = 25,
  assocMethod = "pearson",
  alphaValue = 0.1,
  hammingValue = 0.001,
  nIter = Inf,
  outNet = "regNet",
  zScaling = TRUE,
  showProgress = TRUE,
  randomizationMethod = "None",
  scaleByPresent = FALSE,
  filterExpr = FALSE
)

Arguments

Details

This function is a wrapper around scorpion that groups cells according to metadata columns, filters out groups with insufficient cells, runs network inference on each remaining group independently, and finally combines all resulting networks into a single wide-format data frame.

Value

A data.frame in wide format where rows represent TF-target pairs (union across all networks) and columns represent network identifiers. Cell values are edge weights from the corresponding network. When multiple network types are requested via outNet, an additional leading edge_type column identifies the network each row comes from and the network types are stacked in long format.

Author(s)

Daniel Osorio <daniecos@uio.no>

See Also

scorpion, testEdges, regressEdges

Examples

## Not run: 
# Load test data
data(scorpionTest)

# Example 1: Group by single column (region)
nets_by_region <- runSCORPION(
  gexMatrix = scorpionTest$gex,
  tfMotifs = scorpionTest$tf,
  ppiNet = scorpionTest$ppi,
  cellsMetadata = scorpionTest$metadata,
  groupBy = "region"
)

# -- SCORPION ----------------------------------------------------------------
# + Normalizing data (log scale)
# i 3 networks requested
# + 3 networks meet the minimum cell requirement (30)
# i Computing networks
# + Networks successfully constructed
# + Networks successfully combined

# head(nets_by_region)
#                           tf target           T          B           N
# 1                       AATF  ACKR1 -0.31433856 -0.3569918 -0.33734920
# 2                       ABL1  ACKR1 -0.32915008 -0.3648895 -0.34437341
# 3                      ACSS2  ACKR1 -0.31418599 -0.3557854 -0.33663144
# 4                       ADNP  ACKR1  0.04105895  0.1109288  0.09910822
# 5                      AEBP2  ACKR1 -0.18964574 -0.2202269 -0.17558140
# 6 AEBP2_EED_EZH2_RBBP4_SUZ12  ACKR1 -0.31024700 -0.3508320 -0.33054519

# Example 2: Group by single column (donor)
nets_by_donor <- runSCORPION(
  gexMatrix = scorpionTest$gex,
  tfMotifs = scorpionTest$tf,
  ppiNet = scorpionTest$ppi,
  cellsMetadata = scorpionTest$metadata,
  groupBy = "donor"
)

# -- SCORPION ----------------------------------------------------------------
# + Normalizing data (log scale)
# i 3 networks requested
# + 3 networks meet the minimum cell requirement (30)
# i Computing networks
# + Networks successfully constructed
# + Networks successfully combined
# head(nets_by_donor)
#                           tf target         P31        P32         P33
# 1                       AATF  ACKR1 -0.34869366 -0.3557884 -0.35010835
# 2                       ABL1  ACKR1 -0.33724323 -0.3575331 -0.32875974
# 3                      ACSS2  ACKR1 -0.34569954 -0.3573108 -0.34980657
# 4                       ADNP  ACKR1  0.09933951  0.1045316  0.06046914
# 5                      AEBP2  ACKR1 -0.25111137 -0.2245655 -0.23157035
# 6 AEBP2_EED_EZH2_RBBP4_SUZ12  ACKR1 -0.34148264 -0.3518686 -0.34398594

# Example 3: Group by two columns (donor and region)
nets_by_donor_region <- runSCORPION(
  gexMatrix = scorpionTest$gex,
  tfMotifs = scorpionTest$tf,
  ppiNet = scorpionTest$ppi,
  cellsMetadata = scorpionTest$metadata,
  groupBy = c("donor", "region")
)

# -- SCORPION ----------------------------------------------------------------
# + Normalizing data (log scale)
# i 9 networks requested
# + 9 networks meet the minimum cell requirement (30)
# i Computing networks
# + Networks successfully constructed
# + Networks successfully combined
# head(nets_by_donor_region)
#                           tf target      P31--T      P31--B     P31--N
# 1                       AATF  ACKR1 -0.32634975 -0.33717677 -0.3442886
# 2                       ABL1  ACKR1 -0.34048759 -0.33890429 -0.3509986
# 3                      ACSS2  ACKR1 -0.32570697 -0.33600811 -0.3436603
# 4                       ADNP  ACKR1  0.07975735  0.05354279  0.1048301
# 5                      AEBP2  ACKR1 -0.21472437 -0.20545660 -0.1815737
# 6 AEBP2_EED_EZH2_RBBP4_SUZ12  ACKR1 -0.31861592 -0.32809314 -0.3375652

# Example 4: Group by three columns (donor, region, and cell_type)
nets_by_donor_region_cell_type <- runSCORPION(
  gexMatrix = scorpionTest$gex,
  tfMotifs = scorpionTest$tf,
  ppiNet = scorpionTest$ppi,
  cellsMetadata = scorpionTest$metadata,
  groupBy = c("donor", "region", "cell_type")
)

# -- SCORPION ----------------------------------------------------------------
# + Normalizing data (log scale)
# i 9 networks requested
# + 9 networks meet the minimum cell requirement (30)
# i Computing networks
# + Networks successfully constructed
# + Networks successfully combined
# head(nets_by_donor_region_cell_type)
#                           tf target P31--T--Epithelial P31--B--Epithelial
# 1                       AATF  ACKR1        -0.32634975        -0.33717677
# 2                       ABL1  ACKR1        -0.34048759        -0.33890429
# 3                      ACSS2  ACKR1        -0.32570697        -0.33600811
# 4                       ADNP  ACKR1         0.07975735         0.05354279
# 5                      AEBP2  ACKR1        -0.21472437        -0.20545660
# 6 AEBP2_EED_EZH2_RBBP4_SUZ12  ACKR1        -0.31861592        -0.32809314

# Example 5: Using GPU computing engine (if available)
nets_gpu <- runSCORPION(
  gexMatrix = scorpionTest$gex,
  tfMotifs = scorpionTest$tf,
  ppiNet = scorpionTest$ppi,
  cellsMetadata = scorpionTest$metadata,
  groupBy = "region",
  computingEngine = "gpu"
)

# -- SCORPION ----------------------------------------------------------------
# + Normalizing data (log scale)
# i 3 networks requested
# + 3 networks meet the minimum cell requirement (30)
# i Computing networks
# + Networks successfully constructed
# + Networks successfully combined
# head(nets_gpu)
#                           tf target           T          B           N
# 1                       AATF  ACKR1 -0.31433821 -0.3569913 -0.33734894
# 2                       ABL1  ACKR1 -0.32915005 -0.3648892 -0.34437302
# 3                      ACSS2  ACKR1 -0.31418574 -0.3557851 -0.33663106
# 4                       ADNP  ACKR1  0.04105883  0.1109285  0.09910798
# 5                      AEBP2  ACKR1 -0.18964562 -0.2202267 -0.17558131
# 6 AEBP2_EED_EZH2_RBBP4_SUZ12  ACKR1 -0.31024694 -0.3508317 -0.33054504

# Example 6: Removing batch effect using donor as batch
nets_batch_corrected <- runSCORPION(
  gexMatrix = scorpionTest$gex,
  tfMotifs = scorpionTest$tf,
  ppiNet = scorpionTest$ppi,
  cellsMetadata = scorpionTest$metadata,
  groupBy = "region",
  removeBatchEffect = TRUE,
  batch = scorpionTest$metadata$donor
)

# -- SCORPION ----------------------------------------------------------------
# + Normalizing data (log scale)
# + Correcting for batch effects
# i 3 networks requested
# + 3 networks meet the minimum cell requirement (30)
# i Computing networks
# + Networks successfully constructed
# + Networks successfully combined
# head(nets_batch_corrected)
#                           tf target          T           B           N
# 1                       AATF  ACKR1 -0.3337298 -0.34885471 -0.13011777
# 2                       ABL1  ACKR1 -0.3408020 -0.35409813 -0.17694266
# 3                      ACSS2  ACKR1 -0.3325270 -0.35115311 -0.12661518
# 4                       ADNP  ACKR1  0.1117504  0.08691481  0.01608898
# 5                      AEBP2  ACKR1 -0.2334648 -0.22113011  0.12519312
# 6 AEBP2_EED_EZH2_RBBP4_SUZ12  ACKR1 -0.3274770 -0.34475499 -0.12449908

## End(Not run)

Build gene regulatory networks from single-cell RNA-seq data using PANDA

Description

Constructs gene regulatory networks from single-cell/nuclei RNA-seq data by first applying coarse-graining to reduce sparsity, then running the PANDA (Passing Attributes between Networks for Data Assimilation) message-passing algorithm to integrate transcription factor motifs, protein-protein interactions, and gene expression data into unified regulatory networks.

Usage

scorpion(
  tfMotifs = NULL,
  gexMatrix,
  ppiNet = NULL,
  computingEngine = "cpu",
  nCores = 1,
  gammaValue = 10,
  nPC = 25,
  assocMethod = "pearson",
  alphaValue = 0.1,
  hammingValue = 0.001,
  nIter = Inf,
  outNet = c("regNet", "coregNet", "coopNet"),
  zScaling = TRUE,
  showProgress = TRUE,
  randomizationMethod = "None",
  scaleByPresent = FALSE,
  filterExpr = FALSE
)

Arguments

Value

A list of 6 elements describing the inferred networks at convergence:

• regNet: Regulatory network matrix (TFs × genes)

• coregNet: Co-regulation network matrix (genes × genes)

• coopNet: Cooperation network matrix (TFs × TFs)

• numGenes: Number of genes in the network

• numTFs: Number of transcription factors

• numEdges: Total number of edges in regulatory network

Author(s)

Daniel Osorio <daniecos@uio.no>

See Also

runSCORPION for building networks across cell groups.

Examples

# Loading example data
data(scorpionTest)

# The structure of the data
str(scorpionTest)

# List of 4
# $ gex     :Formal class 'dgCMatrix' [package "Matrix"] with 6 slots
# .. ..@ i       : int [1:46171] 29 32 41 43 61 170 208 245 251 269 ...
# .. ..@ p       : int [1:1955] 0 11 62 97 112 163 184 215 257 274 ...
# .. ..@ Dim     : int [1:2] 300 1954
# .. ..@ Dimnames:List of 2
# .. .. ..$ : chr [1:300] "IGHM" "IGHG2" "IGLC3" "IGLL5" ...
# .. .. ..$ : chr [1:1954] "P31-T_AAACGGGTCGGTTAAC" "P31-T_AAAGATGGTGGCCCTA" ...
# .. ..@ x       : num [1:46171] 1 1 1 1 2 2 1 1 2 1 ...
# .. ..@ factors : list()
# $ tf      :'data.frame':	371738 obs. of  3 variables:
# ..$ source_genesymbol: chr [1:371738] "MYC" "SPI1" "JUN_JUND" "FOS_JUND" ...
# ..$ target_genesymbol: chr [1:371738] "TERT" "BGLAP" "JUN" "JUN" ...
# ..$ weight           : num [1:371738] 1 1 1 1 1 1 1 1 1 1 ...
# ..- attr(*, "origin")= chr "cache"
# ..- attr(*, "url")= chr "https://omnipathdb.org/interactions? __truncated__
# $ ppi     :'data.frame':	4076 obs. of  3 variables:
# ..$ source_genesymbol: chr [1:4076] "ZIC1" "HES5" "ATOH1" "DLL1" ...
# ..$ target_genesymbol: chr [1:4076] "ATOH1" "ATOH1" "HES5" "NOTCH1" ...
# ..$ weight           : num [1:4076] 1 1 1 1 1 1 1 1 1 1 ...
# ..- attr(*, "origin")= chr "cache"
# ..- attr(*, "url")= chr "https://omnipathdb.org/interactions?__truncated__
# $ metadata:'data.frame':	1954 obs. of  4 variables:
# ..$ cell_id  : chr [1:1954] "P31-T_AAACGGGTCGGTTAAC" "P31-T_AAAGATGGTGGCCCTA"...
# ..$ donor    : chr [1:1954] "P31" "P31" "P31" "P31" ...
# ..$ region   : chr [1:1954] "T" "T" "T" "T" ...
# ..$ cell_type: Factor w/ 1 level "Epithelial": 1 1 1 1 1 1 1 1 1 1 ...

# Running SCORPION for epithelial cells from the normal tissue
# We are using alphaValue = 0.8 for testing purposes (Default = 0.1).
scorpionOutput <- scorpion(
  tfMotifs = scorpionTest$tf,
  gexMatrix = scorpionTest$gex[, scorpionTest$metadata$region == "N"],
  ppiNet = scorpionTest$ppi,
  alphaValue = 0.8
)

# -- SCORPION --------------------------------------------------------------------------------------
# + Initializing and validating
# + Verified sufficient samples
# i Normalizing networks
# i Learning Network
# i Using tanimoto similarity
# + Successfully ran SCORPION on 281 Genes and 963 TFs

# Structure of the output.
str(scorpionOutput)

# List of 6
# $ regNet  : num [1:963, 1:281] -0.1556 -0.0455 -0.1461 1.6881 0.8746 ...
# ..- attr(*, "dimnames")=List of 2
# .. ..$ : chr [1:963] "AATF" "ABL1" "ACSS2" "ADNP" ...
# .. ..$ : chr [1:281] "ACKR1" "ACTA2" "ACTG2" "ADAMDEC1" ...
# $ coregNet: num [1:281, 1:281] 2.02e+06 3.84 4.10 -1.26 8.81e-01 ...
# ..- attr(*, "dimnames")=List of 2
# .. ..$ : chr [1:281] "ACKR1" "ACTA2" "ACTG2" "ADAMDEC1" ...
# .. ..$ : chr [1:281] "ACKR1" "ACTA2" "ACTG2" "ADAMDEC1" ...
# $ coopNet : num [1:963, 1:963] 1.17e+07 -2.66 8.13 -1.31 4.95 ...
# ..- attr(*, "dimnames")=List of 2
# .. ..$ : chr [1:963] "AATF" "ABL1" "ACSS2" "ADNP" ...
# .. ..$ : chr [1:963] "AATF" "ABL1" "ACSS2" "ADNP" ...
# $ numGenes: int 281
# $ numTFs  : int 963
# $ numEdges: int 270603

Example single-cell colorectal cancer data for SCORPION

Description

A list bundling the inputs required to build and compare gene regulatory networks with SCORPION, derived from a colorectal cancer single-cell RNA-sequencing experiment. It contains a gene expression matrix, a transcription factor motif prior, a protein-protein interaction prior, and cell-level metadata.

Usage

data(scorpionTest)

Format

A named list with four elements:

gex

A dgCMatrix gene expression matrix with 300 genes (rows) and 1,954 cells (columns).

tf

A data.frame of transcription factor-target motif pairs from DoRothEA with columns source_genesymbol, target_genesymbol and weight (371,738 rows).

ppi

A data.frame of protein-protein interactions with columns source_genesymbol, target_genesymbol and weight (4,076 rows).

metadata

A data.frame of cell-level annotations with columns cell_id, donor, region and cell_type (1,954 rows). Region codes are T (tumor), B (border) and N (normal).

Examples

# Loading example data
data(scorpionTest)

# The structure of the data
str(scorpionTest)

Test edges from SCORPION networks

Description

Performs statistical testing of network edges from runSCORPION output. Supports single-sample tests (testing if edges differ from zero) and two-sample tests (comparing edges between two groups).

Usage

testEdges(
  networksDF,
  testType = c("single", "two.sample"),
  group1,
  group2 = NULL,
  paired = FALSE,
  alternative = c("two.sided", "greater", "less"),
  padjustMethod = "BH",
  minLog2FC = 0,
  moderateVariance = TRUE,
  empiricalNull = TRUE,
  nCores = 1L,
  batchSize = NULL
)

Arguments

Details

For single-sample tests, the function tests whether the mean edge weight across replicates significantly differs from zero using a one-sample t-test.

For two-sample tests, the function compares edge weights between two groups using Welch's t-test (unequal variances assumed).

For paired tests, the function calculates the difference between matched pairs and performs a one-sample t-test on the differences (testing if mean difference differs from zero). This is appropriate when samples are matched (e.g., Tumor and Normal from the same patient).

The returned SE is the raw sampling standard error before optional SAM-style variance moderation. It is intended for downstream effect-size meta-analysis. The moderated SE is used only internally for calculating the test statistic and p-value.

Edges are tested independently, and p-values are adjusted for multiple testing using the specified method.

The function uses fully vectorized computations for efficiency, making it suitable for large-scale analyses with millions of edges. T-statistics and p-values are calculated using matrix operations without iteration.

Value

A data.frame containing:

• tf: Transcription factor

• target: Target gene

• meanEdge: Mean edge weight

• SE: Raw, unmoderated sampling standard error

• tStatistic: Test statistic

• pValue: Raw p-value

• pAdj: Adjusted p-value

• For two-sample tests: meanGroup1, meanGroup2, cohensD, log2FoldChange (Group1 - Group2)

Author(s)

Daniel Osorio <daniecos@uio.no>

See Also

runSCORPION, maEdges, circosEdges

Examples

## Not run: 
# Load test data and build networks by donor and region
# Note: T = Tumor, N = Normal, B = Border regions
data(scorpionTest)
nets <- runSCORPION(
  gexMatrix = scorpionTest$gex,
  tfMotifs = scorpionTest$tf,
  ppiNet = scorpionTest$ppi,
  cellsMetadata = scorpionTest$metadata,
  groupBy = c("donor", "region")
)

# Single-sample test: Test if edges in Tumor region differ from zero
tumor_nets <- grep("--T$", colnames(nets), value = TRUE)
results_single <- testEdges(
  networksDF = nets,
  testType = "single",
  group1 = tumor_nets
)

# Two-sample test: Compare Tumor vs Border regions
tumor_nets <- grep("--T$", colnames(nets), value = TRUE)
border_nets <- grep("--B$", colnames(nets), value = TRUE)
results_tumor_vs_border <- testEdges(
  networksDF = nets,
  testType = "two.sample",
  group1 = tumor_nets,
  group2 = border_nets
)

# View top differential edges (Tumor vs Border)
head(results_tumor_vs_border[order(results_tumor_vs_border$pAdj), ])

# Compare Tumor vs Normal regions
normal_nets <- grep("--N$", colnames(nets), value = TRUE)
results_tumor_vs_normal <- testEdges(
  networksDF = nets,
  testType = "two.sample",
  group1 = tumor_nets,
  group2 = normal_nets
)

# Filter by minimum log2 fold change for focused analysis
results_filtered <- testEdges(
  networksDF = nets,
  testType = "two.sample",
  group1 = tumor_nets,
  group2 = normal_nets,
  minLog2FC = 0.5
)

# Paired t-test: Compare matched Tumor vs Normal samples
tumor_nets_ordered <- c("P31--T", "P32--T", "P33--T")
normal_nets_ordered <- c("P31--N", "P32--N", "P33--N")
results_paired <- testEdges(
  networksDF = nets,
  testType = "two.sample",
  group1 = tumor_nets_ordered,
  group2 = normal_nets_ordered,
  paired = TRUE
)

## End(Not run)