Showing 27 of total 27 results (show query)
constantamateur
SoupX:Single Cell mRNA Soup eXterminator
Quantify, profile and remove ambient mRNA contamination (the "soup") from droplet based single cell RNA-seq experiments. Implements the method described in Young et al. (2018) <doi:10.1101/303727>.
Maintained by Matthew Daniel Young. Last updated 2 years ago.
12.6 match 264 stars 10.09 score 594 scripts 1 dependents
immunogenomics
harmony:Fast, Sensitive, and Accurate Integration of Single Cell Data
Implementation of the Harmony algorithm for single cell integration, described in Korsunsky et al <doi:10.1038/s41592-019-0619-0>. Package includes a standalone Harmony function and interfaces to external frameworks.
Maintained by Ilya Korsunsky. Last updated 4 months ago.
algorithmdata-integrationscrna-seqopenblascpp
6.4 match 554 stars 13.74 score 5.5k scripts 8 dependents
welch-lab
rliger:Linked Inference of Genomic Experimental Relationships
Uses an extension of nonnegative matrix factorization to identify shared and dataset-specific factors. See Welch J, Kozareva V, et al (2019) <doi:10.1016/j.cell.2019.05.006>, and Liu J, Gao C, Sodicoff J, et al (2020) <doi:10.1038/s41596-020-0391-8> for more details.
Maintained by Yichen Wang. Last updated 2 months ago.
nonnegative-matrix-factorizationsingle-cellopenblascpp
6.8 match 402 stars 10.80 score 334 scripts 1 dependents
feiyoung
ProFAST:Probabilistic Factor Analysis for Spatially-Aware Dimension Reduction
Probabilistic factor analysis for spatially-aware dimension reduction across multi-section spatial transcriptomics data with millions of spatial locations. More details can be referred to Wei Liu, et al. (2023) <doi:10.1101/2023.07.11.548486>.
Maintained by Wei Liu. Last updated 1 months ago.
9.6 match 2 stars 5.86 score 12 scripts 1 dependents
eonurk
cinaRgenesets:Ready-to-Use Curated Gene Sets for 'cinaR'
Immune related gene sets provided along with the 'cinaR' package.
Maintained by Onur Karakaslar. Last updated 4 years ago.
diceenrichmentenrichment-analysisgene-setsgenesetspbmcwikipathways
15.0 match 3 stars 3.35 score 15 scripts
bioc
scviR:experimental inferface from R to scvi-tools
This package defines interfaces from R to scvi-tools. A vignette works through the totalVI tutorial for analyzing CITE-seq data. Another vignette compares outputs of Chapter 12 of the OSCA book with analogous outputs based on totalVI quantifications. Future work will address other components of scvi-tools, with a focus on building understanding of probabilistic methods based on variational autoencoders.
Maintained by Vincent Carey. Last updated 5 months ago.
infrastructuresinglecelldataimportbioconductorcite-seqscverse
7.9 match 6 stars 5.60 score 11 scripts
satijalab
SeuratObject:Data Structures for Single Cell Data
Defines S4 classes for single-cell genomic data and associated information, such as dimensionality reduction embeddings, nearest-neighbor graphs, and spatially-resolved coordinates. Provides data access methods and R-native hooks to ensure the Seurat object is familiar to other R users. See Satija R, Farrell J, Gennert D, et al (2015) <doi:10.1038/nbt.3192>, Macosko E, Basu A, Satija R, et al (2015) <doi:10.1016/j.cell.2015.05.002>, and Stuart T, Butler A, et al (2019) <doi:10.1016/j.cell.2019.05.031> for more details.
Maintained by Paul Hoffman. Last updated 1 years ago.
3.6 match 25 stars 11.69 score 1.2k scripts 88 dependents
cran
VAM:Variance-Adjusted Mahalanobis
Contains logic for cell-specific gene set scoring of single cell RNA sequencing data.
Maintained by H. Robert Frost. Last updated 1 years ago.
8.7 match 4.78 score 4 dependents
stemangiola
tidyseurat:Brings Seurat to the Tidyverse
It creates an invisible layer that allow to see the 'Seurat' object as tibble and interact seamlessly with the tidyverse.
Maintained by Stefano Mangiola. Last updated 8 months ago.
assaydomaininfrastructurernaseqdifferentialexpressiongeneexpressionnormalizationclusteringqualitycontrolsequencingtranscriptiontranscriptomicsdplyrggplot2pcapurrrsctseuratsingle-cellsingle-cell-rna-seqtibbletidyrtidyversetranscriptstsneumap
3.5 match 158 stars 9.66 score 398 scripts 1 dependentsbioc
tidySingleCellExperiment:Brings SingleCellExperiment to the Tidyverse
'tidySingleCellExperiment' is an adapter that abstracts the 'SingleCellExperiment' container in the form of a 'tibble'. This allows *tidy* data manipulation, nesting, and plotting. For example, a 'tidySingleCellExperiment' is directly compatible with functions from 'tidyverse' packages `dplyr` and `tidyr`, as well as plotting with `ggplot2` and `plotly`. In addition, the package provides various utility functions specific to single-cell omics data analysis (e.g., aggregation of cell-level data to pseudobulks).
Maintained by Stefano Mangiola. Last updated 5 months ago.
assaydomaininfrastructurernaseqdifferentialexpressionsinglecellgeneexpressionnormalizationclusteringqualitycontrolsequencingbioconductordplyrggplot2plotlysingle-cell-rna-seqsingle-cell-sequencingsinglecellexperimenttibbletidyrtidyverse
3.5 match 36 stars 8.86 score 125 scripts 2 dependents
stephenslab
fastTopics:Fast Algorithms for Fitting Topic Models and Non-Negative Matrix Factorizations to Count Data
Implements fast, scalable optimization algorithms for fitting topic models ("grade of membership" models) and non-negative matrix factorizations to count data. The methods exploit the special relationship between the multinomial topic model (also, "probabilistic latent semantic indexing") and Poisson non-negative matrix factorization. The package provides tools to compare, annotate and visualize model fits, including functions to efficiently create "structure plots" and identify key features in topics. The 'fastTopics' package is a successor to the 'CountClust' package. For more information, see <doi:10.48550/arXiv.2105.13440> and <doi:10.1186/s13059-023-03067-9>. Please also see the GitHub repository for additional vignettes not included in the package on CRAN.
Maintained by Peter Carbonetto. Last updated 16 days ago.
3.3 match 79 stars 8.38 score 678 scripts 1 dependentsbioc
dominoSignal:Cell Communication Analysis for Single Cell RNA Sequencing
dominoSignal is a package developed to analyze cell signaling through ligand - receptor - transcription factor networks in scRNAseq data. It takes as input information transcriptomic data, requiring counts, z-scored counts, and cluster labels, as well as information on transcription factor activation (such as from SCENIC) and a database of ligand and receptor pairings (such as from CellPhoneDB). This package creates an object storing ligand - receptor - transcription factor linkages by cluster and provides several methods for exploring, summarizing, and visualizing the analysis.
Maintained by Jacob T Mitchell. Last updated 5 months ago.
systemsbiologysinglecelltranscriptomicsnetwork
3.8 match 5 stars 6.50 score 5 scriptsbioc
seahtrue:Seahtrue revives XF data for structured data analysis
Seahtrue organizes oxygen consumption and extracellular acidification analysis data from experiments performed on an XF analyzer into structured nested tibbles.This allows for detailed processing of raw data and advanced data visualization and statistics. Seahtrue introduces an open and reproducible way to analyze these XF experiments. It uses file paths to .xlsx files. These .xlsx files are supplied by the userand are generated by the user in the Wave software from Agilent from the assay result files (.asyr). The .xlsx file contains different sheets of important data for the experiment; 1. Assay Information - Details about how the experiment was set up. 2. Rate Data - Information about the OCR and ECAR rates. 3. Raw Data - The original raw data collected during the experiment. 4. Calibration Data - Data related to calibrating the instrument. Seahtrue focuses on getting the specific data needed for analysis. Once this data is extracted, it is prepared for calculations through preprocessing. To make sure everything is accurate, both the initial data and the preprocessed data go through thorough checks.
Maintained by Vincent de Boer. Last updated 5 months ago.
cellbasedassaysfunctionalpredictiondatarepresentationdataimportcellbiologycheminformaticsmetabolomicsmicrotitreplateassayvisualizationqualitycontrolbatcheffectexperimentaldesignpreprocessinggo
4.0 match 5.04 score 2 scriptsstephenslab
fastglmpca:Fast Algorithms for Generalized Principal Component Analysis
Implements fast, scalable optimization algorithms for fitting generalized principal components analysis (GLM-PCA) models, as described in "A Generalization of Principal Components Analysis to the Exponential Family" Collins M, Dasgupta S, Schapire RE (2002, ISBN:9780262271738), and subsequently "Feature Selection and Dimension Reduction for Single-Cell RNA-Seq Based on a Multinomial Model" Townes FW, Hicks SC, Aryee MJ, Irizarry RA (2019) <doi:10.1186/s13059-019-1861-6>.
Maintained by Eric Weine. Last updated 4 days ago.
3.3 match 11 stars 5.72 score 16 scriptsbioc
speckle:Statistical methods for analysing single cell RNA-seq data
The speckle package contains functions for the analysis of single cell RNA-seq data. The speckle package currently contains functions to analyse differences in cell type proportions. There are also functions to estimate the parameters of the Beta distribution based on a given counts matrix, and a function to normalise a counts matrix to the median library size. There are plotting functions to visualise cell type proportions and the mean-variance relationship in cell type proportions and counts. As our research into specialised analyses of single cell data continues we anticipate that the package will be updated with new functions.
Maintained by Belinda Phipson. Last updated 5 months ago.
singlecellrnaseqregressiongeneexpression
3.4 match 5.41 score 258 scripts
mojaveazure
pbmc3k:Raw and Processed Matrices of the PBMC 3k Dataset
What the package does (one paragraph).
Maintained by Paul Hoffman. Last updated 9 months ago.
17.9 match 1.00 scorebioc
CelliD:Unbiased Extraction of Single Cell gene signatures using Multiple Correspondence Analysis
CelliD is a clustering-free multivariate statistical method for the robust extraction of per-cell gene signatures from single-cell RNA-seq. CelliD allows unbiased cell identity recognition across different donors, tissues-of-origin, model organisms and single-cell omics protocols. The package can also be used to explore functional pathways enrichment in single cell data.
Maintained by Akira Cortal. Last updated 5 months ago.
rnaseqsinglecelldimensionreductionclusteringgenesetenrichmentgeneexpressionatacseqopenblascppopenmp
3.6 match 4.85 score 70 scriptsmojaveazure
pbmc3k.sce:PBMC 3k Dataset as a SingleCellExperiment
The PBMC 3k dataset provided as a SingleCellExperiment object. Also includes a processed version, pbmc3k.sce.final, using Bioc-equivalents of the Seurat standard workflow
Maintained by Paul Hoffman. Last updated 10 months ago.
12.2 match 1.30 score
orenbenkiki
chameleon:Automatic Colors for Multi-Dimensional Data
Assign distinct colors to arbitrary multi-dimensional data, considering its structure.
Maintained by Oren Ben-Kiki. Last updated 2 years ago.
5.1 match 3.00 score 20 scriptsbioc
EasyCellType:Annotate cell types for scRNA-seq data
We developed EasyCellType which can automatically examine the input marker lists obtained from existing software such as Seurat over the cell markerdatabases. Two quantification approaches to annotate cell types are provided: Gene set enrichment analysis (GSEA) and a modified versio of Fisher's exact test. The function presents annotation recommendations in graphical outcomes: bar plots for each cluster showing candidate cell types, as well as a dot plot summarizing the top 5 significant annotations for each cluster.
Maintained by Ruoxing Li. Last updated 5 months ago.
singlecellsoftwaregeneexpressiongenesetenrichment
3.5 match 4.30 score 6 scriptsbioc
ontoProc:processing of ontologies of anatomy, cell lines, and so on
Support harvesting of diverse bioinformatic ontologies, making particular use of the ontologyIndex package on CRAN. We provide snapshots of key ontologies for terms about cells, cell lines, chemical compounds, and anatomy, to help analyze genome-scale experiments, particularly cell x compound screens. Another purpose is to strengthen development of compelling use cases for richer interfaces to emerging ontologies.
Maintained by Vincent Carey. Last updated 3 days ago.
infrastructuregobioinformaticsgenomicsontology
1.7 match 3 stars 6.37 score 75 scripts 2 dependentsbioc
cytoMEM:Marker Enrichment Modeling (MEM)
MEM, Marker Enrichment Modeling, automatically generates and displays quantitative labels for cell populations that have been identified from single-cell data. The input for MEM is a dataset that has pre-clustered or pre-gated populations with cells in rows and features in columns. Labels convey a list of measured features and the features' levels of relative enrichment on each population. MEM can be applied to a wide variety of data types and can compare between MEM labels from flow cytometry, mass cytometry, single cell RNA-seq, and spectral flow cytometry using RMSD.
Maintained by Jonathan Irish. Last updated 5 months ago.
proteomicssystemsbiologyclassificationflowcytometrydatarepresentationdataimportcellbiologysinglecellclustering
2.3 match 4.18 score 15 scriptsbioc
adverSCarial:adverSCarial, generate and analyze the vulnerability of scRNA-seq classifier to adversarial attacks
adverSCarial is an R Package designed for generating and analyzing the vulnerability of scRNA-seq classifiers to adversarial attacks. The package is versatile and provides a format for integrating any type of classifier. It offers functions for studying and generating two types of attacks, single gene attack and max change attack. The single-gene attack involves making a small modification to the input to alter the classification. The max-change attack involves making a large modification to the input without changing its classification. The package provides a comprehensive solution for evaluating the robustness of scRNA-seq classifiers against adversarial attacks.
Maintained by Ghislain FIEVET. Last updated 5 months ago.
softwaresinglecelltranscriptomicsclassification
1.7 match 5.42 score 19 scripts
sistm
cytometree:Automated Cytometry Gating and Annotation
Given the hypothesis of a bi-modal distribution of cells for each marker, the algorithm constructs a binary tree, the nodes of which are subpopulations of cells. At each node, observed cells and markers are modeled by both a family of normal distributions and a family of bi-modal normal mixture distributions. Splitting is done according to a normalized difference of AIC between the two families. Method is detailed in: Commenges, Alkhassim, Gottardo, Hejblum & Thiebaut (2018) <doi: 10.1002/cyto.a.23601>.
Maintained by Boris P Hejblum. Last updated 2 years ago.
1.2 match 9 stars 5.91 score 15 scripts 1 dependentscran
RESET:Reconstruction Set Test
Contains logic for sample-level variable set scoring using randomized reduced rank reconstruction error. Frost, H. Robert (2023) "Reconstruction Set Test (RESET): a computationally efficient method for single sample gene set testing based on randomized reduced rank reconstruction error" <doi:10.1101/2023.04.03.535366>.
Maintained by H. Robert Frost. Last updated 1 years ago.
2.8 match 2.30 scorebioc
LRcell:Differential cell type change analysis using Logistic/linear Regression
The goal of LRcell is to identify specific sub-cell types that drives the changes observed in a bulk RNA-seq differential gene expression experiment. To achieve this, LRcell utilizes sets of cell marker genes acquired from single-cell RNA-sequencing (scRNA-seq) as indicators for various cell types in the tissue of interest. Next, for each cell type, using its marker genes as indicators, we apply Logistic Regression on the complete set of genes with differential expression p-values to calculate a cell-type significance p-value. Finally, these p-values are compared to predict which one(s) are likely to be responsible for the differential gene expression pattern observed in the bulk RNA-seq experiments. LRcell is inspired by the LRpath[@sartor2009lrpath] algorithm developed by Sartor et al., originally designed for pathway/gene set enrichment analysis. LRcell contains three major components: LRcell analysis, plot generation and marker gene selection. All modules in this package are written in R. This package also provides marker genes in the Prefrontal Cortex (pFC) human brain region, human PBMC and nine mouse brain regions (Frontal Cortex, Cerebellum, Globus Pallidus, Hippocampus, Entopeduncular, Posterior Cortex, Striatum, Substantia Nigra and Thalamus).
Maintained by Wenjing Ma. Last updated 5 months ago.
singlecellgenesetenrichmentsequencingregressiongeneexpressiondifferentialexpressionenrichmentmarker-genes
0.5 match 3 stars 4.48 score 5 scripts