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.
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).
Address the dropout events prevalent in scRNAseq data. scImpute is an imputation method that determines which values are affected by dropout events in data and performs imputation only on dropout entries. This method learns each gene’s dropout probability in each cell by fitting a mixture model. Next, it imputes the dropout values in a cell by borrowing information of the same gene in other similar cells.
Serves for single-cell data analysis. Granatum is a program that provides biologists with access to single-cell bioinformatics methods, and software developers with the opportunity to promote and combine their tools with various others in customizable pipelines. Its architecture simplifies the incorporation of cutting-edge tools and enables handling of large datasets. Moreover, it can eliminate inter-module incompatibilities by isolating the dependencies of each module.
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.
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.
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.
Imputes dropout values in single-cell RNA sequencing (scRNA-seq) data. DeepImpute is a package exploiting deep neural networks and models based on sub-networks derived from subsets generated from split genes. This program proposes a method enabling the use of a model which can be trained with a subset of data to increase the rapidity of its analysis. It was tested on four different datasets.
Enables single-cell RNA sequencing (scRNA-seq) data imputation. PBLR is a cell sub-population based bounded low-rank method that can (1) recover transcriptomic level and dynamics masked by dropouts, (2) improve low-dimensional representation, and (3) restore the gene-gene co-expression relationship. The software also automatically detects cell subpopulations. It has few parameters, making it generally applicable to data from diverse labs or techniques.
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.
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.
Retrieves informative features, such as low-dimensional representations of gene expression profiles, per cell from massive single-cell data. scScope employs a deep learning method and a self-correcting layer. It can conduct imputations on zero-valued entries of input scRNA-seq data. This tool enables the exploitation of massive and noisy single-cell expression data. It is useful for unsupervised single-cell data modeling.
Models gene expression as a low rank matrix. mcImpute is an imputation algorithm for scRNA-seq data that can be used for clustering accuracy, cell type separability, differential gene prediction, cell visualization or gene distribution. It sprouts in values in place of dropouts in the process of recovering the full gene expression data from sparse single cell data. It can also recover artificially planted missing values in a single cell expression matrix of mouse neurons.
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.
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.
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.
Retrieves true scRNA-seq expression levels. ALRA imputes technical zeros in a selective manner using the non-negativity and correlation structure of expression matrices. It performs a rank-k approximation of the expression matrix with randomized singular vector decomposition (SVD). This tool is useful to separate cell types in both t-distributed stochastic neighbor embedding (t-SNE) and the original high-dimensional space.
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