SingleCellExperiment - S4 Classes for Single Cell Data
Defines a S4 class for storing data from single-cell experiments. This includes specialized methods to store and retrieve spike-in information, dimensionality reduction coordinates and size factors for each cell, along with the usual metadata for genes and libraries.
Last updated 4 months ago
immunooncologydatarepresentationdataimportinfrastructuresinglecell
8.74 score 28 dependencies 264 dependentsRUVSeq - Remove Unwanted Variation from RNA-Seq Data
This package implements the remove unwanted variation (RUV) methods of Risso et al. (2014) for the normalization of RNA-Seq read counts between samples.
Last updated 4 months ago
immunooncologydifferentialexpressionpreprocessingrnaseqsoftware
12 stars 6.20 score 113 dependencies 5 dependentsEDASeq - Exploratory Data Analysis and Normalization for RNA-Seq
Numerical and graphical summaries of RNA-Seq read data. Within-lane normalization procedures to adjust for GC-content effect (or other gene-level effects) on read counts: loess robust local regression, global-scaling, and full-quantile normalization (Risso et al., 2011). Between-lane normalization procedures to adjust for distributional differences between lanes (e.g., sequencing depth): global-scaling and full-quantile normalization (Bullard et al., 2010).
Last updated 4 months ago
immunooncologysequencingrnaseqpreprocessingqualitycontroldifferentialexpression
4 stars 5.53 score 108 dependencies 9 dependentsscone - Single Cell Overview of Normalized Expression data
SCONE is an R package for comparing and ranking the performance of different normalization schemes for single-cell RNA-seq and other high-throughput analyses.
Last updated 4 months ago
immunooncologynormalizationpreprocessingqualitycontrolgeneexpressionrnaseqsoftwaretranscriptomicssequencingsinglecellcoverage
53 stars 3.70 score 176 dependencieszinbwave - Zero-Inflated Negative Binomial Model for RNA-Seq Data
Implements a general and flexible zero-inflated negative binomial model that can be used to provide a low-dimensional representations of single-cell RNA-seq data. The model accounts for zero inflation (dropouts), over-dispersion, and the count nature of the data. The model also accounts for the difference in library sizes and optionally for batch effects and/or other covariates, avoiding the need for pre-normalize the data.
Last updated 4 months ago
immunooncologydimensionreductiongeneexpressionrnaseqsoftwaretranscriptomicssequencingsinglecell
3.38 score 68 dependencies 7 dependentsmbkmeans - Mini-batch K-means Clustering for Single-Cell RNA-seq
Implements the mini-batch k-means algorithm for large datasets, including support for on-disk data representation.
Last updated 4 months ago
clusteringgeneexpressionrnaseqsoftwaretranscriptomicssequencingsinglecell
9 stars 2.09 score 79 dependencies 2 dependentsawst - Asymmetric Within-Sample Transformation
We propose an Asymmetric Within-Sample Transformation (AWST) to regularize RNA-seq read counts and reduce the effect of noise on the classification of samples. AWST comprises two main steps: standardization and smoothing. These steps transform gene expression data to reduce the noise of the lowly expressed features, which suffer from background effects and low signal-to-noise ratio, and the influence of the highly expressed features, which may be the result of amplification bias and other experimental artifacts.
Last updated 4 months ago
normalizationgeneexpressionrnaseqsoftwaretranscriptomicssequencingsinglecell
3 stars 0.91 score 28 dependencies