We are analyzing https://link.springer.com/article/10.1186/s12859-016-1176-5.

Title:
FastProject: a tool for low-dimensional analysis of single-cell RNA-Seq data | BMC Bioinformatics
Description:
Background A key challenge in the emerging field of single-cell RNA-Seq is to characterize phenotypic diversity between cells and visualize this information in an informative manner. A common technique when dealing with high-dimensional data is to project the data to 2 or 3 dimensions for visualization. However, there are a variety of methods to achieve this result and once projected, it can be difficult to ascribe biological significance to the observed features. Additionally, when analyzing single-cell data, the relationship between cells can be obscured by technical confounders such as variable gene capture rates. Results To aid in the analysis and interpretation of single-cell RNA-Seq data, we have developed FastProject, a software tool which analyzes a gene expression matrix and produces a dynamic output report in which two-dimensional projections of the data can be explored. Annotated gene sets (referred to as gene ‘signatures’) are incorporated so that features in the projections can be understood in relation to the biological processes they might represent. FastProject provides a novel method of scoring each cell against a gene signature so as to minimize the effect of missed transcripts as well as a method to rank signature-projection pairings so that meaningful associations can be quickly identified. Additionally, FastProject is written with a modular architecture and designed to serve as a platform for incorporating and comparing new projection methods and gene selection algorithms. Conclusions Here we present FastProject, a software package for two-dimensional visualization of single cell data, which utilizes a plethora of projection methods and provides a way to systematically investigate the biological relevance of these low dimensional representations by incorporating domain knowledge.
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Keywords {🔍}

genes, signatures, data, cells, gene, signature, fastproject, cell, projection, expression, article, analysis, pubmed, google, scholar, scores, methods, cas, singlecell, projections, score, method, consistency, biological, set, central, number, matrix, report, signatureprojection, fig, dimensional, scrnaseq, probability, false, negatives, distribution, rnaseq, glioblastoma, software, additional, pca, nonlinear, procedure, results, output, provide, input, expressed, msigdb,

Topics {✒️}

^{\delta^{2}_{jk}/\alpha^{2}}}{ {\sum\nolimits}_{ w_{aj}w_{bj}}{ {\sum\nolimits}_{ single-cell rna-seq profiles single-cell rna-seq experiments single-cell rna-seq data d_{ik} \mid e_{ik} ^{\delta^{2}_{jk}/\alpha^{2}}} $$ }w_{aj}w_{bj}} $$ single-cell rna-seq d_{ij} \mid e_{ij} full-length mrna-seq scrna-seq research community article download pdf analyzing single-cell data = \frac{ {\sum\nolimits}_{ scrna-seq data exploration scrna-seq normalization tools $$ {\small{\begin{aligned} \delta_{ rna-seq methodologies strict pre-filtering step $$ {\small{\begin{aligned} \hat{ tab-delimited text files scrna-seq measurements tend article detomaso scrna-seq data based significant projection-signature pairs ppar γ agonists accepts pre-computed signatures quality control filter } {\sum\nolimits}_{ linear low-dimensional manifold $$ {\small{\begin{aligned} reduce high-dimensional data combines low dimensional-representations incorporating false-negative estimates user-friendly html report memory cd8 t-cells bmc bioinformatics 17 false-negative estimation methods reveals phenotypic heterogeneity methods false-negative estimates x_{aj} - \overline{ estimated false-negative weight privacy choices/manage cookies single cell data [1] single-cell data full access e_{ij} \mid lps-stimulated dendritic cells rank signature-projection pairings

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         description:A key challenge in the emerging field of single-cell RNA-Seq is to characterize phenotypic diversity between cells and visualize this information in an informative manner. A common technique when dealing with high-dimensional data is to project the data to 2 or 3 dimensions for visualization. However, there are a variety of methods to achieve this result and once projected, it can be difficult to ascribe biological significance to the observed features. Additionally, when analyzing single-cell data, the relationship between cells can be obscured by technical confounders such as variable gene capture rates. To aid in the analysis and interpretation of single-cell RNA-Seq data, we have developed FastProject, a software tool which analyzes a gene expression matrix and produces a dynamic output report in which two-dimensional projections of the data can be explored. Annotated gene sets (referred to as gene ‘signatures’) are incorporated so that features in the projections can be understood in relation to the biological processes they might represent. FastProject provides a novel method of scoring each cell against a gene signature so as to minimize the effect of missed transcripts as well as a method to rank signature-projection pairings so that meaningful associations can be quickly identified. Additionally, FastProject is written with a modular architecture and designed to serve as a platform for incorporating and comparing new projection methods and gene selection algorithms. Here we present FastProject, a software package for two-dimensional visualization of single cell data, which utilizes a plethora of projection methods and provides a way to systematically investigate the biological relevance of these low dimensional representations by incorporating domain knowledge.
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         RNA-Seq
         Dimensionality reduction
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         Microarrays
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         Computer Appl. in Life Sciences
         Algorithms
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