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Seurat
Allows studying of spatial patterning of gene expression at the single-cell level. Seurat is an R package that enables quality control (QC), analysis, and exploration of single cell RNA-seq data. The software includes three computational methods: (1) unsupervised clustering and discovery of cell types and states, (2) spatial reconstruction of single cell data, and (3) integrated analysis of single cell RNA-seq across conditions, technologies, and species. It can also localize rare subpopulations, and map both spatially restricted and scattered groups.
Linnorm
Provides a linear model and normality based transformation method. Linnorm is an R package for the analysis of RNA-seq, scRNA-seq, ChIPseq count data or any large-scale count data. It transforms such datasets for parametric tests. Some pipelines are implemented: (i) library size/batch effect normalization, (ii) cell sub-population analysis and visualization, (iii) differential expression analysis or differential peak detection, (iv) highly variable gene discovery and visualization, (v) gene correlation network analysis and visualization, (vi) stable gene selection for scRNA-seq data and (vii) data imputation.
DrImpute
Estimates dropout events in scRNA-seq data. DrImpute is a simple, fast hot deck imputation approach. It identifies similar cells based on clustering, and imputation is performed by averaging the expression values from similar cells. This method can serve as a very useful addition to the currently existing statistical tools for single cell RNA-seq analysis. DrImpute greatly improved many existing statistical tools that do not address the dropout events in three popular research areas in scRNA-seq—cell clustering, visualization, and lineage reconstruction.
SAVER / Single-cell Analysis Via Expression Recovery
Recovers the true expression level of each gene in each cell. SAVER is based on adaptive shrinkage to a multi-gene prediction model. It removes technical variation while retaining biological variation. The tool forms a prediction model for each gene and then uses a weighted average of the observed count and the prediction to estimate the true expression of the gene. It was performed on the Drop-seq dataset for the 16 genes that overlapped between Drop-seq and fluorescence in situ hybridization (FISH).
CIDR / Clustering through Imputation and Dimensionality Reduction
An ultrafast algorithm which uses a novel yet very simple "implicit imputation" approach to alleviate the impact of dropouts in single cell RNA-seq (scRNA-seq) data in a principled manner. Using a range of simulated and real data, we have shown that CIDR outperforms the state-of-the-art methods, namely t-SNE, ZIFA and RaceID, by at least 50% in terms of clustering accuracy, and typically completes within seconds for processing a dataset of hundreds of cells. We believe that single-cell mRNA sequencing in combination with the RaceID algorithm is a powerful tool to unravel heterogeneity of rare cell types in both healthy and diseased organs.
URSM / Unified RNA-Sequencing Model
Analyzes two types of RNA-seq: single cell data and bulk data. URSM adjusts dropout events in single cell data and achieves simultaneously deconvolution in bulk data. This software doesn’t need to calculate on the same subjects the single cell and bulk data. It can (1) obtain reliable estimation of cell type specific gene expression profiles; (2) infer the dropout entries in single cell data; and (3) infer the mixing proportions of different cell types in bulk samples.
LSImpute / Locality Sensitive Imputation
Infers missing values. LSImpute works with several steps: (i) it selects pairs of cells with similarity level, (ii) it clusters the cells previously selected, (iii) for each of identified clusters, it replaces zero values for each gene with values imputed based on the expression levels of gene in all the cells within the cluster, and (iv) the selected cells have imputed values and the clusters they form are collapsed into their respective centroids.
SAUCIE / Sparse Autoencoder for Unsupervised Clustering, Imputation, and Embedding
Offers a method for handling and extracting structure from single-cell RNA-sequencing and CyTOF data. SAUCIE is a standalone software that provides a deep learning approach developed for the analysis of single-cell data from a cohort of patients. The application is based on different layers able to performs several tasks such as data imputation, clustering, batch correction or visualization. The approach is based on the autoencoder neural network framework for unsupervised learning.
MAGIC / Markov Affinity-based Graph Imputation of Cells
Provides a method for imputing missing values, and restoring the structure of the data. After the use of MAGIC, two- and three-dimensional gene interactions are restored. MAGIC is able to impute complex and non-linear shapes of interactions. MAGIC also retains cluster structure, enhances cluster-specific gene interactions and restores trajectories, as demonstrated in mouse retinal bipolar cells, hematopoiesis, and a generated epithelial-to-mesenchymal transition dataset.
BISCUIT / Bayesian Inference for Single-cell ClUstering and ImpuTing
Provides a method for the iterative normalization and cluster of single-cell gene RNA-seq expression data. BISCUIT eases clustering of cells based on similar gene expression after correcting technical variation. The software uses a Bayesian model and employs a model driven by covariance structures for the normalization and the input of data. These functionalities are appropriate to work on tumor heterogeneity and other primary tissue to understand novel cell types.
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