The term batch effects, often used in the biological community, describes a situation where subsets (batches) of the measurements significantly differ in distribution, due to irrelevant instrument-related factors. Batch effects introduce systematic error, which may cause statistical analysis to produce spurious results and/or obfuscate the signal of interest. Typically, the systematic effect of varying instrument conditions on the measurements depends on many unknown factors, whose impact on the difference between the observed and underlying true signal cannot be modeled.
Assists in probabilistic representation and analysis of gene expression in single cells. scVI is a probabilistic approach to normalization and downstream analysis of scRNA-seq data. This method is based on a hierarchical Bayesian model with conditional distributions specified by deep neural networks. It permits to rely instead on stochastic optimization, sampling a fixed number of cells at each iteration.
An easy-to-use application for microarray, RNA-Seq and metabolomics analysis. For splicing sensitive platforms (RNA-Seq or Affymetrix Exon, Gene and Junction arrays), AltAnalyze will assess alternative exon (known and novel) expression along protein isoforms, domain composition and microRNA targeting. In addition to splicing-sensitive platforms, AltAnalyze provides comprehensive methods for the analysis of other data (RMA summarization, batch-effect removal, QC, statistics, annotation, clustering, network creation, lineage characterization, alternative exon visualization, gene-set enrichment and more).
Removes systematic batch effects. Batch Effect Removal is a method based on a residual neural network, trained to minimize the Maximum Mean Discrepancy (MMD) between the multivariate distributions of two replicates, measured in different batches. The software gives access to the datasets and models used to produce the results in the manuscript. The approach is general and can be applied to various data types.
Offers a general workflow for scRNA-Seq processing. SCTK is a toolkit encompassing independent modules for interactive browsing and analysis. The package offers a wide range of functionalities enabling data summary, batch correction, differential expression, or a method to estimate tradeoff between sample size or sequencing depths. It also includes a pipeline starting from filtering step to pathway activity analysis.
Allows creation of workflow for the analysis of Single cell RNA sequencing (scRNA-seq) experiments. ascend can handle data generated from any single cell library preparation platform. It includes functions to leverage multiple CPUs, allowing most analyses to be performed on a standard desktop or laptop. In summary, this tool implements a state-of-the-art unsupervised clustering method and integrates established analysis techniques for normalization and differential gene expression.
Incorporates and perform a batch-correction for heterogeneous single-cell RNA sequencing (scRNA-seq) datasets. Scanorama is an approach able to distinguish only the scRNA-seq datasets with overlapping cell types which are necessary to its analysis. It can be used to highlight: (i) causes of discrepancies between experiments, (ii) genes which contribute to the alignment of two datasets. This program supplies a modular structure that is suited for integration in scRNA-seq workflows.
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.
Manages cell free mRNA contamination within droplet based single cell RNA sequencing. SoupX is an R package able to evaluate, profile and subtract background contamination from a measured expression profile. The application computes which part of unique molecular identifiers (UMIs) for each cell can be related to the detected contamination to subsequently fit cells’ expression and lastly remove it.
Quantifies batch effects in single-cell RNA-sequencing (scRNA-seq) data. kBET allows users to study high-dimensional data without prior assumptions regarding statistical properties. It can be applied to any type of next-generation sequencing (NGS) data given a reasonable sample size per batch. The software was evaluated on simulated data with three different degrees of batch effects.