1 Introduction

levi (Landscape Expression Visualization Interface) integrates gene expression data with biological network topology to produce a continuous landscape — a 2-D heatmap that reveals which regions of a network are collectively over- or under-expressed.

The core idea is that genes positioned close to each other in the network influence each other’s landscape score through normalised Gaussian convolution implemented in C++. A gene expressed in isolation contributes mainly to its own score; a gene that sits in a highly expressed neighbourhood contributes to the weighted average over that region.

Key features in this release:

Feature	Function
Landscape computation	`levi()`
Interactive GUI	`LEVIui()`
Comparison builder	`readExpColumn()`
DESeq2 / edgeR / limma / Seurat adapters	`leviFromDESeq2()` etc.
SummarizedExperiment adapter	`leviFromSE()`
STRING network retrieval	`leviFromSTRING()`
Side-by-side comparison	`leviGrid()`
Landscape subtraction	`leviDiff()`
GO / KEGG enrichment	`leviEnrich()`

2 Installation

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install("levi")

# Optional — needed for specific features
BiocManager::install(c(
    "DESeq2", "edgeR", "limma",           # DE adapters
    "SummarizedExperiment",               # leviFromSE
    "STRINGdb",                           # leviFromSTRING
    "clusterProfiler", "org.Hs.eg.db",   # leviEnrich
    "airway"                              # example dataset
))
install.packages(c("Seurat", "ggrepel", "patchwork", "plotly"))

3 Core concepts

3.1 The landscape score

Each node (gene) in the network receives a landscape score in \[0, 1\]:

1.0 — high transformed signal in the local neighbourhood
0.5 — neutral input for two-column ratio and logFC; in zscore, the mean logFC of measured support points
0.0 — low transformed signal in the local neighbourhood

A single ratio column measures abundance/(abundance + 1), without a control.

The score is computed in three steps:

Signal transformation (signal_mode) — converts raw expression values to a signal in \[0, 1\].
Gaussian convolution (C++) — node and edge-midpoint signals are projected onto a regular grid and smoothed as normalised weighted averages.
Occupancy masking — cells outside the network silhouette become NA. Grid width is as.integer((resolutionValueInput / 100) * 210 + 30) after clipping the input to 1–100.

3.2 The `readExpColumn()` helper

readExpColumn("Test-Control") tells levi() which columns of the expression file represent the test and control conditions. Multiple comparisons produce one landscape per comparison (batch mode):

# Single comparison
readExpColumn("TumorCurrentSmoker-NormalNeverSmoker")
#> [[1]]
#> readExpColumn
#> 
#> [[2]]
#> [1] "TumorCurrentSmoker-NormalNeverSmoker"

# Two comparisons — levi() will return a list of two results
readExpColumn(
    "TumorCurrentSmoker-NormalNeverSmoker",
    "TumorFormerSmoker-NormalFormerSmoker"
)
#> [[1]]
#> readExpColumn
#> 
#> [[2]]
#> [1] "TumorCurrentSmoker-NormalNeverSmoker"
#> 
#> [[3]]
#> [1] "TumorFormerSmoker-NormalFormerSmoker"

4 Signal transformation modes

signal_mode determines how raw expression values are converted to scores. Choose based on the scale of your data:

`signal_mode`	Formula	Score 0.5 =	Best for
`"ratio"` (default)	Test / (Test + Control)	Test ≈ Control	Counts, TPM, FPKM, linear LFQ
`"logfc"`	1 / (1 + e^{−k · logFC})	logFC = 0	RMA, VST/rlog, log2-proteomics, scRNA-seq avg_log2FC
`"zscore"`	pnorm(z)	Mean logFC of support points	Relative position within a comparison

logfc_k controls sigmoid steepness (default 1). Use k = 0.5 for scRNA-seq (large FC values ±5) and k = 2 for microarray (tight FC ±1).

logfc_net  <- file.path(system.file(package="levi"), "extdata",
                         "logfc_network.dat")
logfc_expr <- file.path(system.file(package="levi"), "extdata",
                         "logfc_expression.dat")

base_call <- list(networkCoordinatesInput = logfc_net,
                  expressionInput         = logfc_expr,
                  fileTypeInput           = "dat",
                  geneSymbolInput         = "ID",
                  readExpColumn           = readExpColumn("Test-Control"),
                  contrastValueInput      = 50,
                  resolutionValueInput    = 20,
                  zoomValueInput          = 50,
                  smoothValueInput        = 5)

res_ratio  <- do.call(levi, c(base_call, list(signal_mode = "ratio")))

res_logfc  <- do.call(levi, c(base_call, list(signal_mode = "logfc")))

res_zscore <- do.call(levi, c(base_call, list(signal_mode = "zscore")))


cat(sprintf(
    "Score range  ratio: %.3f  |  logfc: %.3f  |  zscore: %.3f\n",
    diff(range(res_ratio$scores$LandscapeScore)),
    diff(range(res_logfc$scores$LandscapeScore)),
    diff(range(res_zscore$scores$LandscapeScore))
))
#> Score range  ratio: 0.283  |  logfc: 0.874  |  zscore: 0.879

5 Toy datasets

Seven toy datasets ship with levi in inst/extdata/. Each tests a specific aspect of the package with known expected outcomes.

5.1 Hub topology

A 9-node star network where the central hub and close spokes are over-expressed and the outer corners are under-expressed.

hub_net  <- file.path(system.file(package="levi"), "extdata",
                       "hub_network.dat")
hub_expr <- file.path(system.file(package="levi"), "extdata",
                       "hub_expression.dat")

res_hub <- levi(
    networkCoordinatesInput = hub_net,
    expressionInput         = hub_expr,
    fileTypeInput           = "dat",
    geneSymbolInput         = "ID",
    readExpColumn           = readExpColumn("Test-Control"),
    contrastValueInput      = 50,
    resolutionValueInput    = 20,
    zoomValueInput          = 50,
    smoothValueInput        = 5,
    contourLevi             = TRUE
)

res_hub$scores[, c("Gene", "LandscapeScore", "Rank")]
#>   Gene LandscapeScore Rank
#> 1  HUB         0.9524    1
#> 2   N1         0.9524    2
#> 3   N2         0.9524    3
#> 4   N3         0.9524    4
#> 5   N4         0.9524    5
#> 6   N5         0.0244    6
#> 7   N6         0.0244    7
#> 8   N7         0.0244    8
#> 9   N8         0.0244    9

5.2 Gradient topology

A 6-node linear chain with a monotone expression gradient. Verifies that landscape scores preserve strict rank order.

grad_net  <- file.path(system.file(package="levi"), "extdata",
                        "gradient_network.dat")
grad_expr <- file.path(system.file(package="levi"), "extdata",
                        "gradient_expression.dat")

res_grad <- levi(
    networkCoordinatesInput = grad_net,
    expressionInput         = grad_expr,
    fileTypeInput           = "dat",
    geneSymbolInput         = "ID",
    readExpColumn           = readExpColumn("Test-Control"),
    contrastValueInput      = 50,
    resolutionValueInput    = 20,
    zoomValueInput          = 50,
    smoothValueInput        = 5
)


scores_ord <- res_grad$scores[order(res_grad$scores$Rank),
                               c("Gene", "LandscapeScore")]
scores_ord
#>   Gene LandscapeScore
#> 1   GF         0.9524
#> 2   GE         0.8000
#> 3   GD         0.5995
#> 4   GC         0.4005
#> 5   GB         0.2000
#> 6   GA         0.0476

5.3 Bimodal topology — simultaneous peak and valley

Two disconnected star clusters: cluster A (over-expressed) and cluster B (under-expressed). Demonstrates bilateral permutation test contours and leviDiff.

bim_net  <- file.path(system.file(package="levi"), "extdata",
                       "bimodal_network.dat")
bim_expr <- file.path(system.file(package="levi"), "extdata",
                       "bimodal_expression.dat")

res_bim <- levi(
    networkCoordinatesInput = bim_net,
    expressionInput         = bim_expr,
    fileTypeInput           = "dat",
    geneSymbolInput         = "ID",
    readExpColumn           = readExpColumn("Test-Control"),
    contrastValueInput      = 50,
    resolutionValueInput    = 20,
    zoomValueInput          = 50,
    smoothValueInput        = 5,
    contourLevi             = TRUE
)

cat("A_HUB score:", round(res_bim$scores$LandscapeScore[
    res_bim$scores$Gene == "A_HUB"], 3), "\n")
#> A_HUB score: 0.952
cat("B_HUB score:", round(res_bim$scores$LandscapeScore[
    res_bim$scores$Gene == "B_HUB"], 3), "\n")
#> B_HUB score: 0.048

5.4 Flat expression — null control

A 3×3 grid where all genes have identical expression (Test = Control = 100). Validates absence of false-positive peaks.

flat_net  <- file.path(system.file(package="levi"), "extdata",
                        "flat_network.dat")
flat_expr <- file.path(system.file(package="levi"), "extdata",
                        "flat_expression.dat")

res_flat <- levi(
    networkCoordinatesInput = flat_net,
    expressionInput         = flat_expr,
    fileTypeInput           = "dat",
    geneSymbolInput         = "ID",
    readExpColumn           = readExpColumn("Test-Control"),
    contrastValueInput      = 50,
    resolutionValueInput    = 20,
    zoomValueInput          = 50,
    smoothValueInput        = 5
)

cat(sprintf("Score SD = %.4f  (expected < 0.15)\n",
            sd(res_flat$scores$LandscapeScore)))
#> Score SD = 0.0000  (expected < 0.15)

5.5 Sparse expression — missing gene handling

A 15-node network where only 5 genes have measured expression. Missing genes receive a mode-appropriate neutral value.

sparse_net  <- file.path(system.file(package="levi"), "extdata",
                          "sparse_network.dat")
sparse_expr <- file.path(system.file(package="levi"), "extdata",
                          "sparse_expression.dat")

res_sparse <- levi(
    networkCoordinatesInput = sparse_net,
    expressionInput         = sparse_expr,
    fileTypeInput           = "dat",
    geneSymbolInput         = "ID",
    readExpColumn           = readExpColumn("Test-Control"),
    contrastValueInput      = 50,
    resolutionValueInput    = 20,
    zoomValueInput          = 50,
    smoothValueInput        = 5
)

cat("Nodes in scores table:", nrow(res_sparse$scores),
    "(expected 15)\n")
#> Nodes in scores table: 15 (expected 15)

6 Node scores and peak detection

Every levi() call returns an invisible list with three analytical objects:

# res_hub was computed above; the same six fields come back from every call.
names(res_hub)
#>  [1] "comparison"  "landscape"   "scores"      "peaks"       "regions"    
#>  [6] "pvalues"     "plot"        "plot3d"      "raw_pvalues" "metadata"

str(res_hub$scores)          # Gene, X, Y, LandscapeScore, Rank
#> 'data.frame':    9 obs. of  5 variables:
#>  $ Gene          : chr  "HUB" "N1" "N2" "N3" ...
#>  $ X             : num  0.5 0.5 0.7 0.5 0.3 0.85 0.85 0.15 0.15
#>  $ Y             : num  0.5 0.7 0.5 0.3 0.5 0.85 0.15 0.85 0.15
#>  $ LandscapeScore: num  0.952 0.952 0.952 0.952 0.952 ...
#>  $ Rank          : int  1 2 3 4 5 6 7 8 9
str(res_hub$peaks)           # Type (peak/valley), NearestGene, ..., Score
#> 'data.frame':    9 obs. of  5 variables:
#>  $ Type       : chr  "peak" "peak" "peak" "peak" ...
#>  $ NearestGene: chr  "N3" "HUB" "N4" "N2" ...
#>  $ MatrixRow  : int  35 35 20 45 35 65 9 8 63
#>  $ MatrixCol  : int  21 29 35 35 45 8 9 63 63
#>  $ Score      : num  0.952 0.952 0.952 0.952 0.952 ...
str(res_hub$landscape)       # the plotted surface: Var1, Var2, z
#> 'data.frame':    5184 obs. of  3 variables:
#>  $ Var1: int  1 2 3 4 5 6 7 8 9 10 ...
#>  $ Var2: int  1 1 1 1 1 1 1 1 1 1 ...
#>  $ z   : num  NA NA NA NA NA NA NA NA NA NA ...
res_hub$pvalues              # NULL here, because n_perm = 0
#> NULL
class(res_hub$plot)          # the ggplot object
#> [1] "ggplot2::ggplot" "ggplot"          "ggplot2::gg"     "S7_object"      
#> [5] "gg"

# Top 5 genes by landscape score
head(res_hub$scores[, c("Gene", "LandscapeScore", "Rank")], 5)
#>   Gene LandscapeScore Rank
#> 1  HUB         0.9524    1
#> 2   N1         0.9524    2
#> 3   N2         0.9524    3
#> 4   N3         0.9524    4
#> 5   N4         0.9524    5

# Detected peaks and valleys
if (!is.null(res_bim$peaks) && nrow(res_bim$peaks) > 0)
    res_bim$peaks[, c("Type", "NearestGene", "Score")]
#>      Type NearestGene  Score
#> 1    peak          A5 0.9524
#> 2    peak          A3 0.9524
#> 3    peak          A4 0.9524
#> 4    peak       A_HUB 0.9524
#> 5    peak          A2 0.9524
#> 6    peak          A1 0.9524
#> 7  valley          B6 0.0476
#> 8  valley          B2 0.0476
#> 9  valley          B1 0.0476
#> 10 valley       B_HUB 0.0476
#> 11 valley          B4 0.0476
#> 12 valley          B3 0.0476
#> 13 valley          B5 0.0476

7 Permutation significance test

Set n_perm > 0 to build a null distribution by randomly shuffling expression pairs across measured network nodes, with edge signals recalculated each time. The network and layout stay fixed. By default (inference_unit = "region") regions are redetected in every permutation and each observed region gets a p-value against the maximum regional mass under the null; significant regions are outlined in white. The legacy inference_unit = "cell" instead draws contours from BY-adjusted p-values over both tails of occupied cells: dashed (higher score) and dotted (lower score).

set.seed(42)
res_perm <- levi(
    networkCoordinatesInput = grad_net,
    expressionInput         = grad_expr,
    fileTypeInput           = "dat",
    geneSymbolInput         = "ID",
    readExpColumn           = readExpColumn("Test-Control"),
    contrastValueInput      = 50,
    resolutionValueInput    = 20,
    zoomValueInput          = 50,
    smoothValueInput        = 5,
    contourLevi             = TRUE,
    n_perm                  = 50,
    perm_side               = "both",
    sig_level               = 0.05
)

res_perm$regions$summary[, c("Region", "Direction", "Cells", "Mass",
                             "PSpatial", "Significant")]
#>      Region Direction Cells         Mass  PSpatial Significant
#> 1   over_04      over    12 1.371304e-03 0.8627451       FALSE
#> 2  under_01     under    12 1.371304e-03 0.8627451       FALSE
#> 3   over_03      over     8 7.213581e-04 1.0000000       FALSE
#> 4  under_02     under     8 7.213581e-04 1.0000000       FALSE
#> 5   over_02      over    10 6.485895e-04 1.0000000       FALSE
#> 6  under_03     under    10 6.485895e-04 1.0000000       FALSE
#> 7   over_01      over    12 3.891537e-04 1.0000000       FALSE
#> 8  under_04     under    12 3.891537e-04 1.0000000       FALSE
#> 9  under_05     under     4 7.200790e-20 1.0000000       FALSE
#> 10  over_05      over     4 0.000000e+00 1.0000000       FALSE

`perm_side`	Regions outlined (region mode)	Contour(s) drawn (cell mode)
`"both"` (default)	Over and under	Dashed (over) + dotted (under)
`"over"`	Over only	Dashed only
`"under"`	Under only	Dotted only

In cell mode the adjusted p-value matrices are available in result$pvalues$over and result$pvalues$under (dimensions: resolutionValue × resolutionValue). The vignette levi_inference compares the two modes and the sample-label and graph-based tests.

8 Batch mode and multi-comparison

Provide multiple comparison strings to readExpColumn() and levi() returns a list — one result per comparison.

hub_net <- file.path(system.file(package="levi"), "extdata",
                      "hub_network.dat")
mc_expr <- file.path(system.file(package="levi"), "extdata",
                      "hub_multicomp_expression.dat")

res_list <- levi(
    networkCoordinatesInput = hub_net,
    expressionInput         = mc_expr,
    fileTypeInput           = "dat",
    geneSymbolInput         = "ID",
    readExpColumn           = readExpColumn("Cond_A-Cond_B",
                                            "Cond_A-Cond_C"),
    contrastValueInput      = 50,
    resolutionValueInput    = 20,
    zoomValueInput          = 50,
    smoothValueInput        = 5
)

cat("Number of comparisons:", length(res_list), "\n")
#> Number of comparisons: 2
cat("Cond_A-Cond_B score range:",
    round(diff(range(res_list[[1]]$scores$LandscapeScore)), 3), "\n")
#> Cond_A-Cond_B score range: 0.9
cat("Cond_A-Cond_C score range:",
    round(diff(range(res_list[[2]]$scores$LandscapeScore)), 3), "\n")
#> Cond_A-Cond_C score range: 0.5

8.1 leviGrid — side-by-side comparison

leviGrid(res_list, titles = c("Cond A vs B", "Cond A vs C"), ncol = 2)

8.2 leviDiff — landscape subtraction

leviDiff() subtracts two landscapes cell-by-cell and generates a diverging blue-to-red map of regional change. Blue = region decreased; red = increased.

leviDiff(
    res_list[[1]], res_list[[2]],
    label_a = "Cond_A vs Cond_B",
    label_b = "Cond_A vs Cond_C"
)

9 Differential expression adapters

levi provides four adapter functions that convert the outputs of DESeq2 (Love, Huber, and Anders 2014), edgeR (Robinson, McCarthy, and Smyth 2010), limma (Ritchie et al. 2015) and Seurat (Hao et al. 2021) directly into the data.frame format expected by expressionInput.

9.1 leviFromDESeq2

library(DESeq2)

dds    <- DESeqDataSetFromMatrix(counts, colData, design = ~condition)
dds    <- DESeq(dds)
res_de <- results(dds, contrast = c("condition", "treated", "untreated"))

expr_df <- leviFromDESeq2(res_de, gene_col = "GeneID")
# Columns: GeneID, baseMean (abundance annotation), log2FoldChange (logFC signal)

levi(
    expressionInput = expr_df,
    # ...
    readExpColumn   = readExpColumn("log2FoldChange-log2FoldChange"),
    signal_mode     = "logfc"
)

9.2 leviFromEdgeR

library(edgeR)

dge  <- DGEList(counts = counts, group = group)
dge  <- calcNormFactors(dge)
fit  <- glmQLFit(dge, design)
res  <- glmQLFTest(fit, coef = 2)

expr_df <- leviFromEdgeR(res, gene_col = "GeneID")
# Columns: GeneID, logCPM (abundance annotation), logFC (logFC signal)

9.3 leviFromLimma

library(limma)

fit  <- lmFit(eset, design)
fit2 <- contrasts.fit(fit, makeContrasts(TvsC = Tumor - Control,
                                          levels = design))
fit2 <- eBayes(fit2)

expr_df <- leviFromLimma(fit2, coef = "TvsC", gene_col = "GeneID")
# Columns: GeneID, AveExpr (abundance annotation), logFC (logFC signal)

9.4 leviFromSeurat

library(Seurat)

markers <- FindMarkers(seurat_obj,
                        ident.1 = "CD4_T",
                        ident.2 = "B_cell",
                        min.pct = 0.25)

expr_df <- leviFromSeurat(markers, gene_col = "GeneID")
# Columns: GeneID, Control (pct.2 annotation), Test (avg_log2FC signal)

9.5 leviFromSE — SummarizedExperiment

library(SummarizedExperiment)

# Option A: condition labels from colData
expr_df <- leviFromSE(
    se            = my_se,
    assay_name    = "counts",
    condition_col = "treatment",
    test_level    = "treated",
    ctrl_level    = "control",
    gene_col      = "GeneID"
)

# Option B: explicit sample names
expr_df <- leviFromSE(
    se       = my_se,
    test_col = c("Sample1", "Sample3"),
    ctrl_col = c("Sample2", "Sample4")
)

9.6 Running the adapters here

The four differential-expression adapters accept a data.frame with the columns their tool produces, and leviFromSE() needs only SummarizedExperiment, which levi already depends on. So the whole conversion layer can be exercised without installing DESeq2, edgeR, limma or Seurat:

genes <- c("HUB", paste0("N", 1:8))

# DESeq2-shaped results table
res_de <- data.frame(
    baseMean       = rep(1000, 9),
    log2FoldChange = c(4.3, 4.1, 4.4, 4.2, 4.3, -5.3, -5.1, -5.4, -5.2),
    row.names      = genes)
expr_de <- leviFromDESeq2(res_de, gene_col = "GeneID")
head(expr_de, 3)
#>   GeneID baseMean log2FoldChange
#> 1    HUB     1000            4.3
#> 2     N1     1000            4.1
#> 3     N2     1000            4.4

# edgeR-shaped and limma-shaped tables
leviFromEdgeR(data.frame(logFC = 4.3, logCPM = 10.2, row.names = "HUB"),
              gene_col = "GeneID")
#>   GeneID logCPM logFC
#> 1    HUB   10.2   4.3
leviFromLimma(data.frame(logFC = 4.3, AveExpr = 8.1, row.names = "HUB"),
              gene_col = "GeneID")
#>   GeneID AveExpr logFC
#> 1    HUB     8.1   4.3

# Seurat-shaped markers: pct.2 becomes Control, avg_log2FC becomes Test
leviFromSeurat(data.frame(avg_log2FC = 2.5, pct.2 = 0.3, row.names = "HUB"),
               gene_col = "GeneID")
#>   GeneID Control Test
#> 1    HUB     0.3  2.5

# A SummarizedExperiment, aggregated by condition label
counts <- matrix(c(200, 200, 200, 200, 200, 5, 5, 5, 5,
                   190, 210, 195, 205, 200, 6, 4, 5, 5,
                    10,  10,  10,  10,  10, 200, 200, 200, 200),
                 nrow = 9,
                 dimnames = list(genes, c("t1", "t2", "n1")))
se <- SummarizedExperiment::SummarizedExperiment(
    assays  = list(counts = counts),
    colData = data.frame(condition = c("Tumor", "Tumor", "Normal"),
                         row.names = colnames(counts)))

expr_se <- leviFromSE(se, assay_name = "counts", condition_col = "condition",
                      test_level = "Tumor", ctrl_level = "Normal",
                      gene_col = "GeneID")
head(expr_se, 3)
#>   GeneID  Test Control
#> 1    HUB 195.0      10
#> 2     N1 205.0      10
#> 3     N2 197.5      10

The output of any adapter goes straight into expressionInput. Remember that a table carrying a ready-made logFC must be used in single-column mode:

hub_net <- system.file("extdata", "hub_network.dat", package = "levi")

res_from_de <- levi(
    expressionInput         = expr_de,
    networkCoordinatesInput = hub_net,
    fileTypeInput           = "dat",
    geneSymbolInput         = "GeneID",
    readExpColumn           = readExpColumn("log2FoldChange-log2FoldChange"),
    resolutionValueInput    = 40,
    signal_mode             = "logfc")


head(res_from_de$scores[, c("Gene", "LandscapeScore", "Rank")], 4)
#>   Gene LandscapeScore Rank
#> 1   N2         0.9878    1
#> 2   N4         0.9866    2
#> 3  HUB         0.9865    3
#> 4   N3         0.9853    4

10 Building networks from STRING

leviFromSTRING() retrieves a protein interaction network from the STRING database (Szklarczyk et al. 2021) and computes a 2-D layout with igraph (Csárdi and Nepusz 2006) automatically. No manual file download is required.

library(levi)
# BiocManager::install("STRINGdb")

# MAPK pathway genes (human)
mapk_genes <- c("EGFR", "KRAS", "BRAF", "MAP2K1", "MAPK1",
                 "MAPK3", "RPS6KA1", "MYC", "JUN", "FOS")

set.seed(42)   # layout is stochastic; fix seed for reproducibility
net <- leviFromSTRING(
    genes           = mapk_genes,
    species         = 9606,          # human
    score_threshold = 400,           # medium confidence
    layout          = "fr"           # Fruchterman-Reingold
)

# net$nodes : data.frame — name, x, y
# net$edges : data.frame — V1, V2
# net$graph : igraph object

levi(
    expressionInput          = my_de_results,
    networkCoordinatesInput  = net$nodes,
    networkInteractionsInput = net$edges,
    fileTypeInput            = "stg",
    geneSymbolInput          = "GeneID",
    readExpColumn            = readExpColumn("log2FoldChange-log2FoldChange"),
    signal_mode              = "logfc"
)

10.1 Layout algorithms

`layout`	Algorithm	Best for
`"fr"`	Fruchterman-Reingold (Fruchterman and Reingold 1991)	General use, 50–500 nodes
`"kk"`	Kamada-Kawai (Kamada and Kawai 1989)	Small networks (≤ 100 nodes)
`"lgl"`	Large Graph Layout	Large networks (> 500 nodes)
`"dh"`	Davidson-Harel	Highest quality, slower
`"circle"`	Ring	Pathway-like chains

10.2 Species codes

Organism	`species`
H. sapiens	`9606`
M. musculus	`10090`
R. norvegicus	`10116`
D. rerio	`7955`
D. melanogaster	`7227`
C. elegans	`6239`
S. cerevisiae	`4932`

10.3 Saving and reusing networks

# Save to TSV — readable directly by levi() as file paths
write.table(net$nodes, "string_nodes.tsv",
            sep = "\t", row.names = FALSE, quote = FALSE)
write.table(net$edges, "string_edges.tsv",
            sep = "\t", row.names = FALSE, quote = FALSE)

# Save full R object (preserves igraph + layout)
saveRDS(net, "string_network.rds")

# Reload in future sessions
net2 <- readRDS("string_network.rds")
levi(networkCoordinatesInput  = net2$nodes,
     networkInteractionsInput = net2$edges,
     fileTypeInput = "stg", ...)

# Or use STRINGdb local cache to avoid re-downloading raw files
net3 <- leviFromSTRING(genes, input_directory = "~/.stringdb_cache")

11 Real-data examples

11.1 RNA-seq: airway (DESeq2 + STRING)

The airway dataset (Himes et al. 2014) contains RNA-seq counts from airway smooth muscle cells treated with dexamethasone (DEX) vs untreated controls (4 cell lines, ~64k genes). We use DESeq2 to identify DE genes and leviFromSTRING() to build the interaction network.

BiocManager::install(c("airway", "DESeq2", "STRINGdb"))
library(airway); library(DESeq2); library(levi)

data(airway)
dds <- DESeqDataSet(airway, design = ~cell + dex)
dds <- DESeq(dds)
res <- results(dds, contrast = c("dex", "trt", "untrt"))

expr_df <- leviFromDESeq2(res)

# Top 80 DE genes — build STRING network
top80 <- head(expr_df$GeneID[order(abs(expr_df$log2FoldChange),
                                    decreasing = TRUE)], 80)
set.seed(42)
net <- leviFromSTRING(top80, species = 9606, score_threshold = 400)

levi(
    expressionInput          = expr_df,
    networkCoordinatesInput  = net$nodes,
    networkInteractionsInput = net$edges,
    fileTypeInput            = "stg",
    geneSymbolInput          = "GeneID",
    readExpColumn            = readExpColumn("log2FoldChange-log2FoldChange"),
    signal_mode              = "logfc",
    n_perm                   = 500,
    perm_side                = "both"
)

Biological interpretation: Nodes inside the dashed contour are network hubs whose entire neighbourhood is up-regulated by DEX treatment — strong candidates for pathway-level drug targets or effectors.

11.2 Microarray: ALL leukemia (limma + STRING)

The ALL dataset (Chiaretti et al. 2004) (Affymetrix HG-U95Av2, 128 patients) compares B-cell and T-cell subtypes of Acute Lymphoblastic Leukemia. Data are RMA-normalized (log2 scale), so signal_mode = "logfc" with logfc_k = 2 (tight microarray fold-changes) is appropriate.

BiocManager::install(c("ALL", "limma", "STRINGdb"))
library(ALL); library(limma); library(levi)

data(ALL)
design   <- model.matrix(~0 + ALL$BT)
colnames(design) <- c("B", "T")
contrast <- makeContrasts(B - T, levels = design)

fit  <- lmFit(ALL, design)
fit2 <- contrasts.fit(fit, contrast)
fit2 <- eBayes(fit2)

expr_df <- leviFromLimma(fit2, coef = 1)

set.seed(7)
net <- leviFromSTRING(expr_df$GeneID, species = 9606,
                       score_threshold = 700,   # high confidence
                       layout = "kk")

levi(
    expressionInput          = expr_df,
    networkCoordinatesInput  = net$nodes,
    networkInteractionsInput = net$edges,
    fileTypeInput            = "stg",
    geneSymbolInput          = "GeneID",
    readExpColumn            = readExpColumn("logFC-logFC"),
    signal_mode              = "logfc",
    logfc_k                  = 2
)

11.3 scRNA-seq: PBMC (Seurat + STRING)

The 3k PBMC dataset from 10x Genomics (10x Genomics 2016) is analysed with Seurat. FindMarkers identifies genes with avg_log2FC values per cluster. Since scRNA-seq fold-changes can reach ±5, use logfc_k = 0.5 for a softer sigmoid.

BiocManager::install("TENxPBMCData")
install.packages("Seurat")
library(TENxPBMCData); library(Seurat); library(levi)

pbmc_sce <- TENxPBMCData("pbmc3k")
pbmc     <- as.Seurat(pbmc_sce)
pbmc     <- NormalizeData(pbmc)
pbmc     <- FindVariableFeatures(pbmc)
pbmc     <- ScaleData(pbmc)
pbmc     <- RunPCA(pbmc)
pbmc     <- FindNeighbors(pbmc)
pbmc     <- FindClusters(pbmc, resolution = 0.5)

markers <- FindMarkers(pbmc,
                        ident.1 = "CD4 T cells",
                        ident.2 = "B cells",
                        min.pct = 0.25)
expr_df <- leviFromSeurat(markers, gene_col = "GeneID")

set.seed(21)
net <- leviFromSTRING(rownames(markers), species = 9606,
                       score_threshold = 400, layout = "fr")

levi(
    expressionInput          = expr_df,
    networkCoordinatesInput  = net$nodes,
    networkInteractionsInput = net$edges,
    fileTypeInput            = "stg",
    geneSymbolInput          = "GeneID",
    readExpColumn            = readExpColumn("avg_log2FC-avg_log2FC"),
    signal_mode              = "logfc",
    logfc_k                  = 0.5
)

12 Enrichment analysis — leviEnrich

After computing the landscape, leviEnrich() tests the top-scoring genes (peaks) and bottom-scoring genes (valleys) for GO and KEGG enrichment using clusterProfiler.

BiocManager::install(c("clusterProfiler", "org.Hs.eg.db"))

enrich_res <- leviEnrich(
    result     = res_hub,
    top_n      = 20,          # top-scoring genes (peaks)
    bottom_n   = 20,          # bottom-scoring genes (valleys)
    organism   = "hsa",       # KEGG organism code
    orgdb      = "org.Hs.eg.db",
    keytype    = "SYMBOL",
    pval_cutoff = 0.05,
    types      = c("GO_BP", "KEGG")
)

# enrich_res$over  — enrichment on peak genes
# enrich_res$under — enrichment on valley genes

# Dotplots are printed automatically
# Access results programmatically:
head(as.data.frame(enrich_res$over$GO_BP))

13 3-D interactive visualization

When plotly is installed, plot3d = TRUE generates an interactive 3-D surface alongside the standard 2-D heatmap. Users can rotate, zoom, and inspect individual scores.

install.packages("plotly")

levi(
    networkCoordinatesInput = hub_net,
    expressionInput         = hub_expr,
    fileTypeInput           = "dat",
    geneSymbolInput         = "ID",
    readExpColumn           = readExpColumn("Test-Control"),
    contrastValueInput      = 50,
    resolutionValueInput    = 20,
    zoomValueInput          = 50,
    smoothValueInput        = 5,
    plot3d                  = TRUE
)

14 Interactive GUI — LEVIui

The Shiny-based GUI provides interactive access to all levi features:

LEVIui(browser = FALSE)  # open in RStudio Viewer
LEVIui(browser = TRUE)   # open in system browser

The side panel has a File tab (network and expression inputs, colour scale, contour, 3D surface, gene highlight, Run) and a Settings tab (contrast, resolution, smoothing, zoom, signal mode and the permutation test). The main panel shows the 2D landscape and the 3D surface in two tabs, and the tables Genes, Node scores, Peaks and valleys and Regions in four more. The interface calls levi() with the chosen parameters, so it agrees with script mode.

GUI-only features: - Brush selection — click and drag on the 2D map to select an area; the summed score is shown as Expression area and the genes under the selection fill the Genes tab (shown only while the 2D map is in front). - Gene highlight — list gene names to circle them on the map in a chosen colour. - Peak labels — checkbox overlays gene names at detected peak/valley positions (uses ggrepel if installed). - 3D surface — interactive surface with an HTML download that keeps the current view, and the camera printed as code for leviSave3D(). - Progress indicator — shown while the landscape or the permutation test is computed. - Download buttons — 2D map (TIFF, BMP, JPEG, PNG), 3D surface (HTML), node score, peak and region tables (CSV).

The interface is described with screenshots in vignette("levi").

15 Visualization parameters

Parameter	Range	Effect
`contrastValueInput`	0–100	Contrast stretch of the colour scale
`resolutionValueInput`	1–100	Grid resolution (higher = finer landscape)
`zoomValueInput`	0–100	Spatial zoom (higher = wider neighbourhood)
`smoothValueInput`	0–100	Gaussian kernel width

15.1 Colour palettes

# Multicolor (default)
levi(..., setcolor = "default")

# Two-color options
levi(..., setcolor = "purple_pink")
levi(..., setcolor = "green_blue")
levi(..., setcolor = "blue_yellow")
levi(..., setcolor = "pink_green")
levi(..., setcolor = "orange_purple")
levi(..., setcolor = "green_marine")

16 Session Information

sessionInfo()
#> R version 4.6.1 (2026-06-24)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.5 LTS
#> 
#> Matrix products: default
#> BLAS:   /home/biocbuild/bbs-3.24-bioc/R/lib/libRblas.so 
#> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0  LAPACK version 3.12.0
#> 
#> 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] levi_1.99.0      BiocStyle_2.41.0
#> 
#> loaded via a namespace (and not attached):
#>  [1] SummarizedExperiment_1.43.0 gtable_0.3.6               
#>  [3] xfun_0.61                   bslib_0.12.0               
#>  [5] ggplot2_4.0.3               Biobase_2.73.2             
#>  [7] lattice_0.23-1              vctrs_0.7.3                
#>  [9] tools_4.6.1                 generics_0.1.4             
#> [11] stats4_4.6.1                parallel_4.6.1             
#> [13] tibble_3.3.1                pkgconfig_2.0.3            
#> [15] Matrix_1.7-6                RColorBrewer_1.1-3         
#> [17] S7_0.2.2                    S4Vectors_0.51.10          
#> [19] lifecycle_1.0.5             compiler_4.6.1             
#> [21] farver_2.1.2                stringr_1.6.0              
#> [23] statmod_1.5.2               tinytex_0.61               
#> [25] Seqinfo_1.3.2               codetools_0.2-20           
#> [27] htmltools_0.5.9             sass_0.4.10                
#> [29] yaml_2.3.12                 pillar_1.11.1              
#> [31] jquerylib_0.1.4             BiocParallel_1.47.0        
#> [33] limma_3.99.0                DelayedArray_0.39.6        
#> [35] cachem_1.1.0                magick_2.9.1               
#> [37] abind_1.4-8                 tidyselect_1.2.1           
#> [39] digest_0.6.39               stringi_1.8.9              
#> [41] dplyr_1.2.1                 reshape2_1.4.5             
#> [43] bookdown_0.48               labeling_0.4.3             
#> [45] fastmap_1.2.0               grid_4.6.1                 
#> [47] cli_3.6.6                   SparseArray_1.13.2         
#> [49] magrittr_2.0.5              patchwork_1.3.2            
#> [51] S4Arrays_1.13.0             dichromat_2.0-1            
#> [53] withr_3.0.3                 scales_1.4.0               
#> [55] rmarkdown_2.32              XVector_0.53.0             
#> [57] matrixStats_1.5.0           igraph_2.3.3               
#> [59] otel_0.2.0                  evaluate_1.0.5             
#> [61] knitr_1.52                  GenomicRanges_1.65.4       
#> [63] IRanges_2.47.5              rlang_1.3.0                
#> [65] isoband_0.3.0               Rcpp_1.1.2                 
#> [67] glue_1.8.1                  BiocManager_1.30.27        
#> [69] xml2_1.6.0                  BiocGenerics_0.59.12       
#> [71] jsonlite_2.0.0              R6_2.6.1                   
#> [73] plyr_1.8.9                  MatrixGenerics_1.25.0

16.1 Signal contracts and reproducibility

Signal interpretation: ratio preserves Test/(Test + Control), with no min-max rescaling; equal nonzero inputs give 0.5. expressionLog = TRUE back-transforms log2 inputs only in this mode. A single ratio column means abundance/(abundance + 1), not a comparison with a control. logfc accepts two log-scale columns or one already computed logFC, mapping zero to 0.5. zscore centres on the mean logFC of measured network support points, not on biological absence of change. Missing measurements are assigned 0.5 and listed in result$metadata. Gaussian smoothing mixes neighbouring signals, so these baseline statements apply to the input signals and to uniformly neutral networks.

Permutation inference is conditional on the fixed network and layout. Measured gene values are shuffled as pairs and edge midpoints are recalculated; missing positions stay fixed. In the default regional mode the maximum regional mass over both directions is the reference statistic, which controls the search across regions without a further adjustment. In cell mode both tails over occupied cells form one multiple-testing family per comparison, adjusted with p_adjust_method (“BY” by default); result$raw_pvalues retains the unadjusted values. perm_side selects the displayed side without changing either family. This is not a test of differential expression between biological replicates; increasing n_perm alone does not validate inferential use. Calibration across networks, layouts and missingness patterns still requires simulation studies.

Call set.seed() before permutation runs. result$metadata records the RNG state, network, coordinates, signal mode, grid settings and software versions. leviDiff() rejects incompatible metadata or grid coordinates. For DESeq2, edgeR and limma adapters, select the logFC column against itself with signal_mode = "logfc"; abundance annotations are not control measurements. For edgeR, select the contrast in glmLRT() or glmQLFTest() before calling leviFromEdgeR() on the resulting test object. KEGG uses the supplied universe, converting both selected genes and background to ENTREZID with the same OrgDb.

References

10x Genomics. 2016. “3k PBMCs from a Healthy Donor: Single Cell Gene Expression Dataset by Cell Ranger 1.1.0.” https://www.10xgenomics.com/datasets.

Chiaretti, Sabina, Xiaochun Li, Robert Gentleman, Antonella Vitale, Marco Vignetti, Franco Mandelli, Jerome Ritz, and Robin Foa. 2004. “Gene Expression Profile of Adult T-cell Acute Lymphocytic Leukemia Identifies Distinct Subsets of Patients with Different Response to Therapy and Survival.” Blood 103 (7): 2771–78. https://doi.org/10.1182/blood-2003-09-3243.

Csárdi, Gábor, and Tamás Nepusz. 2006. “The Igraph Software Package for Complex Network Research.” InterJournal Complex Systems: 1695.

Fruchterman, Thomas M J, and Edward M Reingold. 1991. “Graph Drawing by Force-Directed Placement.” Software: Practice and Experience 21 (11): 1129–64. https://doi.org/10.1002/spe.4380211102.

Hao, Yuhan, Stephanie Hao, Erica Andersen-Nissen, William M Mauck, Shiwei Zheng, Andrew Butler, Maddie J Lee, et al. 2021. “Integrated Analysis of Multimodal Single-Cell Data.” Cell 184 (13): 3573–87. https://doi.org/10.1016/j.cell.2021.04.048.

Himes, Blanca E, Xiaofeng Jiang, Peter Wagner, Ruoxi Hu, Qiyu Wang, Barbara Klanderman, Reid M Whitaker, et al. 2014. “RNA-Seq Transcriptome Profiling Identifies CRISPLD2 as a Glucocorticoid Responsive Gene That Modulates Cytokine Function in Airway Smooth Muscle Cells.” PLoS ONE 9 (6): e99625. https://doi.org/10.1371/journal.pone.0099625.

Kamada, Tomihisa, and Satoru Kawai. 1989. “An Algorithm for Drawing General Undirected Graphs.” Information Processing Letters 31 (1): 7–15. https://doi.org/10.1016/0020-0190(89)90102-6.

Love, Michael I, Wolfgang Huber, and Simon Anders. 2014. “Moderated Estimation of Fold Change and Dispersion for RNA-seq Data with DESeq2.” Genome Biology 15 (12): 550. https://doi.org/10.1186/s13059-014-0550-8.

Ritchie, Matthew E, Belinda Phipson, Di Wu, Yifang Hu, Charity W Law, Wei Shi, and Gordon K Smyth. 2015. “Limma Powers Differential Expression Analyses for RNA-sequencing and Microarray Studies.” Nucleic Acids Research 43 (7): e47. https://doi.org/10.1093/nar/gkv007.

Robinson, Mark D, Davis J McCarthy, and Gordon K Smyth. 2010. “edgeR: A Bioconductor Package for Differential Expression Analysis of Digital Gene Expression Data.” Bioinformatics 26 (1): 139–40. https://doi.org/10.1093/bioinformatics/btp616.

Szklarczyk, Damian, Annika L Gable, Katerina C Nastou, David Lyon, Rebecca Kirsch, Sampo Pyysalo, Nadezhda T Doncheva, et al. 2021. “The STRING Database in 2021: Customizable Protein-Protein Networks, and Functional Characterization of User-Uploaded Gene/Measurement Sets.” Nucleic Acids Research 49 (D1): D605–12. https://doi.org/10.1093/nar/gkaa1074.

levi: A Complete User Guide

2026-09-20