Plugins

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SeqGeq™ now supports the use of plugins (SeqGeq 1.1+). These tools allow for the direct use of third party algorithms in the form of R scripts, or for very quick implementation of features once a requirement has been identified. These featurettes are distributed via JavaArchive files, or “JARs” which can be placed together in a common “SeqGeq… Read more »

1.1.0 Research Preview Release Notes

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Highlights This release is NOT certified by FlowJo, LLC Quality Assurance Department. This release is not for production use. Major improvements & bug fixes from SeqGeq v1.0.1, including Plugin API feature. Initial plugins are available on The FlowJo Exchange. Improvements Enhanced performance Support for HDF5 (.h5) formatted data files Color mapping key for plots in… Read more »

Sample Import

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Thank you for your interest in SeqGeq™, one of the latest software suites from the team at FlowJo, LLC! SeqGeq is designed to make analysis of single cell RNA-Sequencing (scRNA-Seq) data as intuitive and enjoyable as it is for flow data in FlowJo®. The first step in analyzing your scRNA-Seq experiment in this platform is… Read more »

Linear Discriminant Analysis

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linear discriminant analysis (LDA) is dimensionality reduction method that explicitly attempts to model the difference between the classes of data rather than similarities. LDA is a generalization of Fisher’s linear discriminant that characterizes or separates two or more classes of objects or events. The resulting combination can be used in SeqGeq as a linear classifier… Read more »

t-Distributed Stochastic Neighbor Embedding

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t-distributed stochastic neighbor embedding (t-SNE) is a machine learning dimensionality reduction algorithm useful for visualizing high dimensional data sets. t-SNE is particularly well-suited for embedding high-dimensional data into a biaxial plot which can be visualized in a graph window. The dimensionality is reduced in such a way that similar cells are modeled nearby and dissimilar… Read more »

Principal Component Analysis

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Principal component analysis (PCA) is a dimensionality reduction methods which creates a reduced dimensionality projection to provides the best view of the differences in the data. PCA is widely used to visualize high dimensionality data (aka data with many parameters). In SeqGeq you can directly explore the data after projecting into on a biaxial plot…. Read more »

Dimensionality Reduction

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In SeqGeq the dimensionality reduction platform helps to perform certain complex algorithms in just a few clicks. The goal in dimensionality reduction is to reduce the number of variables under consideration (i.e., gene reads) and to obtaining a set of principal variables (i.e., analytical parameters). This is particularly useful as working with too many dimensions… Read more »

Heatmaps

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HeatMaps help visualize genes expression profiles across cell populations in order to determine patterns and/or gene sets. HeatMaps are widely used to organize and visualize data. These plots can be particularly useful for identifying patterns, such as cell cluster to gene set correlation. Clear organization of the data allows the human eye to inspect outputs… Read more »

1.0.1 Release Notes

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Highlights Speed of the Dimensionality Reduction Platform has been significantly improved. Many issues found in SeqGeq 1.0 have been fixed. Improvements Dimensionality Reduction Platform Dimensionality Reduction Platform (DRP) runs significantly faster than previously. PCA now operates normally when the sample contains non-numerical metadata. Running a dimensional reduction algorithm on genes whose names do not start with… Read more »

1.0 Release Notes

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Features Flexible sample import Sample-aware concatenation lets you merge multiple NGS runs prior to analysis, and the intelligent import tool takes into account the format and metadata of your files, regardless of the platform. SeqGeq can communicate with an Illumina BaseSpace account to import scRNA-seq data, while local files can be simply dragged directly into the workspace…. Read more »