Background reduction software tools | Single-cell RNA sequencing data analysis
Profiling the transcriptomes of individual cells via single-cell RNA-sequencing (scRNA-seq) allows the functional role of heterogeneity in gene expression levels between cells to be investigated in early development, in cancer and during tissue differentiation. Current scRNA-seq protocols require amplification of the minute amount of mRNA present in an individual cell. PCR or in vitro transcription is used to amplify cDNA molecules. In combination, these steps contribute to substantial increases in the level of technical noise relative to bulk-level RNA-seq. Several strategies have been proposed to reduce or eliminate technical noise in scRNA-seq protocols.
Assists in detecting of otherwise undetectable subpopulations of cells. scLVM is a program that can be used for estimating the proportion of variance in expression across cells that is explained by technical noise, biological variability and cell cycle. It also can be applied for creating a ‘corrected’ gene expression data set, in which the effect of the identified factor is removed.
A statistical method and software to identify a sorted list of ordering effect (OE) genes. OEFinder is available as an R package along with user-friendly graphical interface implementations that allows users to check for potential artifacts in scRNA-seq data generated by the Fluidigm C1 platform.
Consists of an auto-encoder designed for denoising scRNA-seq data. DCA represents the high dimensional ambient data space in significantly lower dimensions. It allows non-linear embedding of cells. This tool employs scRNA-seq data specific loss function based on negative binomial count distributions to work. It permits users to discover the optimal set of parameters for denoising to avoid poor generalization due to overfitting.
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.
Enhances detection of clusters of cells and co-expressed genes. kNN-smoothing determines neighbors after projecting partially smoothed data onto the first few principal components. It can smooth the number of neighbors and the cell itself. This software avoids or minimizes the artificial clumping of cells in the smoothed data with a dither feature.
Provides an effective algorithm for smoothing single-cell RNA-Seq data. This method combines a previously proposed normalization method with a standard variance-stabilizing transformation (VST) for Poisson-distributed data. It relies on the basic notion of smoothing scRNA-Seq expression profiles by aggregating them with similar cells. Simple aggregation or averaging of scRNA-Seq expression profiles.
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.
Assists in implementing and assessing the performance of a range of normalization workflows. SCONE evaluates the performance of each workflow and ranks them by aggregating over a set of performance metrics. It is applicable to different single-cell RNASeq (scRNAseq) protocols including microfluidic, plate, and droplet, methods. It allows researchers to compare a set of default normalizations as well as to include user-defined normalization methods.
Moderates noisy experimental data in RNA sequencing experiments. netSmooth employs prior knowledge and was applied to a variety of single cell experiments. It uses networks encoding co-expression patterns to smooth scRNA-seq data. This tool can be useful to preprocess data in scRNA-seq experiments. It is able to amplify the biological/technical variability ratio.
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