Visualization Gallery in sciNOME
Introduction
The sciNOME package provides an extensive suite of
academic-grade plotting functions for Single-Cell RNA, Epigenetic, and
Multi-Omics integrated data.
In this vignette, we demonstrate the usage of these functions using minimal mock datasets to ensure rapid compilation without relying on external large data files.
1. Single-Cell RNA Visualization
We first create a tiny mock RNA object.
set.seed(42)
# Mock metadata
mock_meta <- data.frame(
orig.ident = rep(c("Tumor", "Normal"), each = 20),
nFeature_RNA = runif(40, 200, 2500),
nCount_RNA = runif(40, 500, 8000),
percent_mt = runif(40, 0, 10),
Auto_Cluster = rep(c("C1", "C2", "C3", "C4"), each = 10)
)
rownames(mock_meta) <- paste0("Cell_", 1:40)
# Mock UMAP coordinates
mock_umap <- matrix(rnorm(80), ncol = 2)
rownames(mock_umap) <- rownames(mock_meta)
colnames(mock_umap) <- c("UMAP_1", "UMAP_2")
# Mock Pseudotime
mock_pt <- list(
pseudotime = runif(40, 0, 1),
dr_method = "umap",
group_col = "Auto_Cluster",
algorithm = "cluster",
start_clus = "C1",
curve_coords = data.frame(Dim1 = rnorm(40), Dim2 = rnorm(40))
)
# Assemble RNA object
mock_rna_obj <- list(
meta.data = mock_meta,
filter_meta.data = mock_meta,
reductions = list(umap = mock_umap, pseudotime = mock_pt)
)
class(mock_rna_obj) <- "RNA"
1.2 Dimensionality Reduction Plot
Set show_cluster = TRUE to plot custom groups alongside
unsupervised clusters.
if (requireNamespace("patchwork", quietly = TRUE)) {
PlotDimRed_RNA(mock_rna_obj, reduction = "umap", group_col = "orig.ident", show_cluster = TRUE)
}
1.3 Volcano Plot for DEA
mock_dea <- data.frame(
gene = paste0("Gene_", 1:50),
avg_log2FC = rnorm(50, 0, 1.5),
p_val_adj = runif(50, 0, 0.1)
)
mock_dea$avg_log2FC[1:5] <- runif(5, 2, 4)
mock_dea$p_val_adj[1:5] <- runif(5, 1e-6, 1e-3)
if (requireNamespace("ggrepel", quietly = TRUE)) {
PlotVolcano_RNA(mock_dea, fc_cut = 1, p_cut = 0.05, top_n = 5)
}
2. Epigenetic Visualization
We simulate a small methylation .level matrix and its
corresponding metadata.
set.seed(123)
mock_epi_mat <- matrix(runif(200, min = 0, max = 1), nrow = 10, ncol = 20)
colnames(mock_epi_mat) <- paste0("Sample_", 1:20)
rownames(mock_epi_mat) <- paste0("Region_", 1:10)
mock_epi_meta <- data.frame(
SampleID = paste0("Sample_", 1:20),
Condition = rep(c("Tumor", "Normal"), each = 10)
)2.1 Epigenetic Landscape (Ridge Plot)
if (requireNamespace("ggridges", quietly = TRUE)) {
PlotLandscape_Epi(
mat = mock_epi_mat,
meta = mock_epi_meta,
sample_col = "SampleID",
group_col = "Condition"
)
}
2.2 Dimensionality Reduction for Epigenetics
mock_epi_dr <- data.frame(
Dim1 = rnorm(20),
Dim2 = rnorm(20),
Condition = rep(c("Tumor", "Normal"), each = 10)
)
PlotDimRed_Epi(mock_epi_dr, group_col = "Condition", method = "PCA")
2.3 Epigenetic Volcano Plot
mock_epi_dea <- data.frame(
chrdata = paste0("chr1:", 1001:1050),
Diff = rnorm(50, mean = 0, sd = 1),
P.Value = runif(50, min = 0, max = 0.5)
)
mock_epi_dea$Diff[1:3] <- c(1.5, 2.0, -1.8)
mock_epi_dea$P.Value[1:3] <- c(0.001, 0.0005, 0.002)
if (requireNamespace("ggrepel", quietly = TRUE)) {
PlotVolcano_Epi(mock_epi_dea, metric_col = "Diff", th_effect = 1.0, top_n = 3)
}
3. Multi-Omics Integration Plots
Finally, we simulate an integrated dataframe containing RNA Expression, CpG Methylation, and GpC Accessibility.
set.seed(42)
mock_multi <- data.frame(
RNA_Exp = rnorm(40, mean = 10, sd = 2),
CpG_level = runif(40, 0, 1),
GpC_level = runif(40, 0, 1)
)
# Add some artificial correlation
mock_multi$RNA_Exp <- mock_multi$RNA_Exp - (mock_multi$CpG_level * 3)
Session Information
sessionInfo()
#> R version 4.6.0 (2026-04-24)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.4 LTS
#>
#> Matrix products: default
#> BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
#> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so; LAPACK version 3.12.0
#>
#> locale:
#> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
#> [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
#> [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
#> [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
#> [9] LC_ADDRESS=C LC_TELEPHONE=C
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
#>
#> time zone: Etc/UTC
#> tzcode source: system (glibc)
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] ggplot2_4.0.3 dplyr_1.2.1 data.table_1.18.4 sciNOME_0.99.0
#> [5] BiocStyle_2.41.0
#>
#> loaded via a namespace (and not attached):
#> [1] gtable_0.3.6 xfun_0.58 bslib_0.11.0
#> [4] ggrepel_0.9.8 rstatix_0.7.3 lattice_0.22-9
#> [7] vctrs_0.7.3 tools_4.6.0 generics_0.1.4
#> [10] stats4_4.6.0 parallel_4.6.0 tibble_3.3.1
#> [13] pkgconfig_2.0.3 Matrix_1.7-5 RColorBrewer_1.1-3
#> [16] S7_0.2.2 S4Vectors_0.51.3 ggridges_0.5.7
#> [19] lifecycle_1.0.5 compiler_4.6.0 farver_2.1.2
#> [22] Seqinfo_1.3.0 carData_3.0-6 htmltools_0.5.9
#> [25] sys_3.4.3 buildtools_1.0.0 sass_0.4.10
#> [28] yaml_2.3.12 Formula_1.2-5 pillar_1.11.1
#> [31] car_3.1-5 ggpubr_0.6.3 jquerylib_0.1.4
#> [34] tidyr_1.3.2 MASS_7.3-65 cachem_1.1.0
#> [37] abind_1.4-8 nlme_3.1-169 tidyselect_1.2.1
#> [40] digest_0.6.39 purrr_1.2.2 splines_4.6.0
#> [43] maketools_1.3.2 labeling_0.4.3 fastmap_1.2.0
#> [46] grid_4.6.0 cli_3.6.6 magrittr_2.0.5
#> [49] patchwork_1.3.2 broom_1.0.13 withr_3.0.2
#> [52] scales_1.4.0 backports_1.5.1 rmarkdown_2.31
#> [55] igraph_2.3.2 otel_0.2.0 ggsignif_0.6.4
#> [58] pbapply_1.7-4 evaluate_1.0.5 knitr_1.51
#> [61] GenomicRanges_1.65.0 IRanges_2.47.2 viridisLite_0.4.3
#> [64] mgcv_1.9-4 rlang_1.2.0 Rcpp_1.1.1-1.1
#> [67] glue_1.8.1 BiocManager_1.30.27 BiocGenerics_0.59.7
#> [70] jsonlite_2.0.0 R6_2.6.1