Help for package SCORPION

Type:

Package

Title:

Single Cell Oriented Reconstruction of PANDA Individual Optimized Networks

Version:

1.3.0

Description:

Constructs gene regulatory networks from single-cell gene expression data using the PANDA (Passing Attributes between Networks for Data Assimilation) algorithm.

License:

GPL-3

Encoding:

UTF-8

LazyData:

true

Depends:

R (≥ 3.5.0)

Imports:

cli, methods, irlba, igraph, RANN, Matrix, pbapply, dplyr

Suggests:

RhpcBLASctl, testthat (≥ 3.0.0)

RoxygenNote:

7.3.3

NeedsCompilation:

Packaged:

2026-02-04 23:26:35 UTC; dcosorioh

Author:

Daniel Osorio

[aut, cre], Marieke L. Kuijjer

[aut]

Maintainer:

Daniel Osorio <daniecos@uio.no>

Repository:

CRAN

Date/Publication:

2026-02-04 23:50:02 UTC

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

networksDF

A data.frame output from runSCORPION containing TF-target pairs as rows and network identifiers as columns.

orderedGroups

A named list where each element is a character vector of column names in networksDF. Names represent ordered conditions (e.g., list(Normal = c("P31–N", "P32–N"), Border = c("P31–B", "P32–B"), Tumor = c("P31–T", "P32–T"))). The order of list elements defines the progression (first to last).

padjustMethod

Character specifying the p-value adjustment method for multiple testing correction. See p.adjust for options. Default "BH" (Benjamini-Hochberg FDR).

minMeanEdge

Numeric threshold for minimum mean absolute edge weight to include in testing. Edges with mean absolute weight below this threshold are excluded. Default 0 (no filtering).

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

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

gexMatrix

An expression dataset with genes in the rows and barcodes (cells) in the columns.

tfMotifs

A motif dataset, a data.frame or a matrix containing 3 columns. Each row describes a motif associated with a transcription factor (column 1) a gene (column 2) and a score (column 3).

ppiNet

A Protein-Protein-Interaction dataset, a data.frame or matrix containing 3 columns. Each row describes a protein-protein interaction between transcription factor 1 (column 1), transcription factor 2 (column 2) and a score (column 3).

cellsMetadata

A data.frame with cell-level metadata; must contain columns specified in groupBy.

groupBy

Character vector of one or more column names in cellsMetadata to use for grouping cells into networks.

normalizeData

Boolean to indicate normalization of expression data. Default TRUE performs log normalization.

removeBatchEffect

Boolean to indicate batch effect correction. Default FALSE.

batch

Factor or vector giving batch assignment for each cell; required if removeBatchEffect = TRUE.

minCells

Minimum number of cells per group required to build a network. Default is 30.

computingEngine

Either 'cpu' or 'gpu'. Passed to scorpion.

nCores

Number of processors to be used if BLAS or MPI is active.

gammaValue

Graining level of data (proportion of number of single cells to super-cells). Default 10.

nPC

Number of principal components to use for kNN network construction. Default 25.

assocMethod

Association method. Must be one of 'pearson', 'spearman' or 'pcNet'. Default 'pearson'.

alphaValue

Value to be used for update variable in PANDA. Default 0.1.

hammingValue

Value at which to terminate the process based on Hamming distance. Default 0.001.

nIter

Sets the maximum number of iterations PANDA can run before exiting. Default Inf.

outNet

Character specifying which network to extract. Options include "regNet", "coregNet", "coopNet". Default "regNet".

zScaling

Boolean to indicate use of Z-Scores in output. FALSE will use [0,1] scale. Default TRUE.

showProgress

Boolean to indicate printing of output for algorithm progress. Default TRUE.

randomizationMethod

Method by which to randomize gene expression matrix. Default "None". Must be one of "None", "within.gene", "by.gene".

scaleByPresent

Boolean to indicate scaling of correlations by percentage of positive samples. Default FALSE.

filterExpr

Boolean to indicate whether or not to remove genes with 0 expression across all cells. Default FALSE.

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.

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

tfMotifs

A motif dataset (data.frame or matrix) with 3 columns: TF, target gene, and motif score. Pass NULL for co-expression analysis only.

gexMatrix

An expression dataset, with genes in the rows and barcodes (cells) in the columns.

ppiNet

A Protein-Protein-Interaction dataset (data.frame or matrix) with 3 columns: protein 1, protein 2, and interaction score. Pass NULL to disable protein interaction integration.

computingEngine

Character specifying computing device: 'cpu' or 'gpu' (if available). Default 'cpu'.

nCores

Number of processors to be used if BLAS or MPI is active.

gammaValue

Graining level of data (proportion of number of single cells in the initial dataset to the number of super-cells in the final dataset)

nPC

Number of principal components to use for construction of single-cell kNN network.

assocMethod

Association method. Must be one of 'pearson', 'spearman' or 'pcNet'.

alphaValue

Numeric update parameter (0 to 1) controlling relative contribution of prior networks. Default 0.1.

hammingValue

Numeric convergence threshold based on Hamming distance. Algorithm stops when updates fall below this. Default 0.001.

nIter

Sets the maximum number of iterations PANDA can run before exiting.

outNet

A vector containing which networks to return. Options include "regNet", "coregNet", "coopNet".

zScaling

Boolean to indicate use of Z-Scores in output. FALSE will use [0,1] scale.

showProgress

Boolean to indicate printing of output for algorithm progress.

randomizationMethod

Method by which to randomize gene expression matrix. Default "None". Must be one of "None", "within.gene", "by.genes". "within.gene" randomization scrambles each row of the gene expression matrix, "by.gene" scrambles gene labels.

scaleByPresent

Boolean to indicate scaling of correlations by percentage of positive samples.

filterExpr

Boolean to remove genes with zero expression across all cells before network inference. Default FALSE.

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>

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 gene expression, motif, and ppi data

Description

This data is a list containing three objects. The motif data.frame describes a set of pairwise connections where a specific known sequence motif of a transcription factor was found upstream of the corresponding gene. The expression dgCMatrix is a set of 230 gene expression levels measured across 80 cells. Finally, the ppi data.frame describes a set of known pairwise protein-protein interactions.

Usage

data(scorpionTest)

Format

A list containing three datasets.

gex: A subsetted version of 10X Genomics' 3k PBMC dataset provided by the Seurat package.
tf: Subset of the transcription-factor and target gene list provided by the dorothea package for Homo sapiens.
ppi: The known protein-protein interactions and the combined score downloaded from the STRING database

Examples

# Loading example data
data(scorpionTest)

# The structure of the data
str(scorpionTest)

# List of 3
# $ gex:Formal class 'dgCMatrix' [package "Matrix"] with 6 slots
# .. ..@ i       : int [1:4456] 1 5 8 11 22 30 33 34 36 38 ...
# .. ..@ p       : int [1:81] 0 47 99 149 205 258 306 342 387 423 ...
# .. ..@ Dim     : int [1:2] 230 80
# .. ..@ Dimnames:List of 2
# .. .. ..$ : chr [1:230] "MS4A1" "CD79B" "CD79A" "HLA-DRA" ...
# .. .. ..$ : chr [1:80] "ATGCCAGAACGACT" "CATGGCCTGTGCAT" "GAACCTGATGAACC" "TGACTGGATTCTCA" ...
# .. ..@ x       : num [1:4456] 1 1 3 1 1 4 1 5 1 1 ...
# .. ..@ factors : list()
# $ tf :'data.frame':	4485 obs. of  3 variables:
#   ..$ tf    : chr [1:4485] "ADNP" "ADNP" "ADNP" "AEBP2" ...
# ..$ target: chr [1:4485] "PRF1" "TMEM40" "TNFRSF1B" "CFP" ...
# ..$ mor   : num [1:4485] 1 1 1 1 1 1 1 1 1 1 ...
# $ ppi:'data.frame':	12754 obs. of  3 variables:
#   ..$ X.node1       : chr [1:12754] "ADNP" "ADNP" "ADNP" "AEBP2" ...
# ..$ node2         : chr [1:12754] "ZBTB14" "NFIA" "CDC5L" "YY1" ...
# ..$ combined_score: num [1:12754] 0.769 0.64 0.581 0.597 0.54 0.753 0.659 0.548 0.59 0.654 ...

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",
  minMeanEdge = 0
)

Arguments

networksDF

A data.frame output from runSCORPION containing TF-target pairs as rows and network identifiers as columns.

testType

Character specifying the test type. Options are:

"single": Single-sample test (one-sample t-test against zero)
"two.sample": Two-sample comparison (t-test between two groups)

group1

Character vector of column names in networksDF representing the first group (or the only group for single-sample tests).

group2

Character vector of column names in networksDF representing the second group. Required for two-sample tests, ignored for single-sample tests.

paired

Logical indicating whether to perform a paired t-test. Default FALSE. When TRUE, group1 and group2 must have the same length and be in matched order (e.g., group1[1] is paired with group2[1]). Useful for comparing matched samples such as Tumor vs Normal from the same patient.

alternative

Character specifying the alternative hypothesis. Options: "two.sided" (default), "greater", or "less".

padjustMethod

Character specifying the p-value adjustment method for multiple testing correction. See p.adjust for options. Default "BH" (Benjamini-Hochberg FDR).

minMeanEdge

Numeric threshold for minimum mean absolute edge weight to include in testing. Edges with mean absolute weight below this threshold are excluded. Default 0 (no filtering).

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).

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
tStatistic: Test statistic
pValue: Raw p-value
pAdj: Adjusted p-value
For two-sample tests: meanGroup1, meanGroup2, diffMean (Group1 - Group2), log2FoldChange

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)  # T = Tumor
results_single <- testEdges(
  networksDF = nets,
  testType = "single",
  group1 = tumor_nets
)

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

# Filter by minimum edge weight for focused analysis
results_filtered <- testEdges(
  networksDF = nets,
  testType = "two.sample",
  group1 = tumor_nets,
  group2 = normal_nets,
  minMeanEdge = 0.1  # Only test edges with |mean| >= 0.1
)

# Paired t-test: Compare matched Tumor vs Normal samples (same patient)
# Ensure columns are ordered by patient: P31--T with P31--N, P32--T with P32--N, etc.
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)

Regression analysis of edges across ordered conditions

Description

Usage

Arguments

Details

Value

Examples

Run SCORPION across cell groups and return combined networks

Description

Usage

Arguments

Details

Value

Examples

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

Description

Usage

Arguments

Value

Author(s)

See Also

Examples

Example single-cell gene expression, motif, and ppi data

Description

Usage

Format

Examples

Test edges from SCORPION networks

Description

Usage

Arguments

Details

Value

Examples