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scSimEval - Unified Benchmarking and Accuracy Evaluation for Single-Cell Multiomics Simulation Methods

A simple and unified evaluation toolkit for benchmarking single-cell and multiomics data simulation methods. Evaluates how realistic simulated data are compared to real biological datasets across three multiomics experimental designs: (i) paired multiomics (simultaneous co-assay from identical cells, e.g. 10x Chromium Multiome, SHARE-seq, SNARE-seq), which unlocks the full Category 7 cross-modal coupling suite including FOSCTTM alignment scores and peak-to-gene regulatory linkage; (ii) unpaired multiomics (independent scRNA-seq and scATAC-seq profiled from separate cells of the same tissue), supported through per-modality unimodal evaluation (Categories 1-6, 8) and population-level cross-modal metrics such as co-expression module fidelity and network Jaccard similarity; and (iii) mosaic multiomics (partial co-measurement where only a subset of cells has both modalities, e.g. DOGMA-seq or mosaic pooling designs), handled by subsetting the paired cell fraction for coupling metrics and evaluating each modality independently otherwise. The toolkit quantifies simulation realism using 62 metrics organized into 8 easy-to-understand categories: (1) Distribution Differences: Statistical distances between real and simulated values (such as KS, Wasserstein, MAD, and RMSE), (2) Summary Properties: Gene correlations, zero counts, and dropout patterns, (3) Cell Structure and Clustering: How well distinct cell types and clusters are preserved (such as Silhouette width, ARI, and NMI), (4) Batch and Confounder Mixing: How well technical batch variations are captured or mixed, (5) Biological Signals and Marker Genes: Preservation of differentially expressed genes (DEGs) across Simpipe, SimBench, and Shaky Foundations frameworks, (6) Cell Lineage and Trajectories: Preservation of continuous developmental pathways (inferred directly from scRNA-seq counts), (7) Multiomics Relationships: Connections between different layers (such as gene expression and chromatin accessibility) — fully active for paired data; population-level metrics available for unpaired and mosaic data, (8) Computational Resources: Speed (runtime in seconds) and memory usage (peak RAM in MiB). Includes non-linear low-dimensional cell embedding projections (UMAP, t-SNE, PCA) with quantitative cluster separability (Silhouette) and fidelity (ARI) metrics, automated score normalization, leaderboards, 600 DPI bubble plots, and an interactive Shiny app studio.

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singlecelltranscriptomicsatacseqqualitycontroldimensionreductiondifferentialexpressiongeneexpressionnormalizationsoftware

4.08 score

lfproQC - Quality Control for Label-Free Proteomics Expression Data

Label-free bottom-up proteomics expression data is often affected by data heterogeneity and missing values. Normalization and missing value imputation are commonly used techniques to address these issues and make the dataset suitable for further downstream analysis. This package provides an optimal combination of normalization and imputation methods for the dataset. The package utilizes three normalization methods and three imputation methods.The statistical evaluation measures named pooled co-efficient of variance, pooled estimate of variance and pooled median absolute deviation are used for selecting the best combination of normalization and imputation method for the given dataset. The user can also visualize the results by using various plots available in this package. The user can also perform the differential expression analysis between two sample groups with the function included in this package. The chosen three normalization methods, three imputation methods and three evaluation measures were chosen for this study based on the research papers published by Välikangas et al. (2016) <doi:10.1093/bib/bbw095>, Jin et al. (2021) <doi:10.1038/s41598-021-81279-4> and Srivastava et al. (2023) <doi:10.2174/1574893618666230223150253>.This work has published by Sakthivel et al. (2025) <doi:10.1021/acs.jproteome.4c00552>.

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4.00 score 1 stars 4 scripts 339 downloads

TxDb.Gmax.JGI.Gmv4 - Transcripts Annotation Database for Soybean

TxDb.Gmax.JGI.Gmv4 provides transcript, exon, and CDS annotations for soybean (Glycine max Wm82.a4.v1). It is a Bioconductor TxDb object built from the official JGI/Phytozome GFF3 annotation and is available via AnnotationHub (retrievable with AnnotationHub()); after loading you can use GenomicFeatures accessors (genes(), transcripts(), exonsBy(), cdsBy()) to extract gene models identified by JGI/Phytozome locus IDs.

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annotationdatatxdbglycine_maxsequenceannotationannotationhub

3.18 score

TxDb.Athaliana.Ensembl.TAIR10 - Transcripts Annotation Database for Arabidopsis thaliana

TxDb.Athaliana.Ensembl.TAIR10 provides transcript, exon, CDS, and gene annotations for Arabidopsis thaliana based on the TAIR10 reference genome and Ensembl Plants release 63. The TxDb object is built from the Ensembl Plants GFF3 annotation and can be queried using GenomicFeatures accessors such as genes(), transcripts(), exonsBy(), and cdsBy().

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annotationdatatxdbarabidopsis_thalianasequenceannotationannotationhub

3.18 score

PlantTxDbHub - Transcript Database Hub for Plant Genomes

Provides TxDb (transcript database) annotations for plant species. The SQLite files are hosted on Zenodo (or other permanent URLs) and are downloaded on demand via the `downloadPlantTxDbs()` function. The list of available species and their download URLs is maintained in a curated CSV file (inst/extdata/metadata.csv), allowing users and contributors to easily add new databases without modifying R code.

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annotationdatatxdbarabidopsis_thalianaoryza_sativaglycine_maxsequenceannotation

3.18 score