scholarly journals Splotch: Robust estimation of aligned spatial temporal gene expression data

2019 ◽  
Author(s):  
Tarmo Äijö ◽  
Silas Maniatis ◽  
Sanja Vickovic ◽  
Kristy Kang ◽  
Miguel Cuevas ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Data ◽  
Rna Seq ◽  
Computational Framework ◽  
Temporal Gene Expression ◽  
Tissue Sections ◽  
Spatially Resolved ◽  
Transcriptomics Data ◽  
Approaches To Study ◽  
And Function

AbstractSpatial genomics technologies enable new approaches to study how cells interact and function in intact multicellular environments but present a host of technical and computational challenges. Here we describe Splotch, a novel computational framework for the analysis of spatially resolved transcriptomics data. Splotch aligns transcriptomics data from multiple tissue sections and timepoints to generate improved posterior estimates of gene expression. We demonstrate alignment of a large corpus of single-cell RNA-seq data into an automatically generated spatial-temporal coordinate and study optimal design for spatial transcriptomics experiments.

scholarly journals Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution

Science ◽  
2019 ◽  
Vol 363 (6434) ◽  
pp. 1463-1467 ◽  
Author(s):  
Samuel G. Rodriques ◽  
Robert R. Stickels ◽  
Aleksandrina Goeva ◽  
Carly A. Martin ◽  
Evan Murray ◽  
...  
Keyword(s):  
Gene Expression ◽  
High Spatial Resolution ◽  
Expression Patterns ◽  
Cell Types ◽  
Expression Data ◽  
Tissue Sections ◽  
Spatially Resolved ◽  
Genome Wide ◽  
Cell Type Specific ◽  
Genome Wide Expression

Spatial positions of cells in tissues strongly influence function, yet a high-throughput, genome-wide readout of gene expression with cellular resolution is lacking. We developed Slide-seq, a method for transferring RNA from tissue sections onto a surface covered in DNA-barcoded beads with known positions, allowing the locations of the RNA to be inferred by sequencing. Using Slide-seq, we localized cell types identified by single-cell RNA sequencing datasets within the cerebellum and hippocampus, characterized spatial gene expression patterns in the Purkinje layer of mouse cerebellum, and defined the temporal evolution of cell type–specific responses in a mouse model of traumatic brain injury. These studies highlight how Slide-seq provides a scalable method for obtaining spatially resolved gene expression data at resolutions comparable to the sizes of individual cells.

scholarly journals VistoSeg: a MATLAB pipeline to process, analyze and visualize high resolution histology images for Visium spatial transcriptomics data

2021 ◽  
Author(s):  
Madhavi Tippani ◽  
Heena Rajesh Divecha ◽  
Joseph L. Catallini ◽  
Lukas M Weber ◽  
Abby Spangler ◽  
...  
Keyword(s):  
Gene Expression ◽  
High Resolution ◽  
Programming Language ◽  
Gene Expression Data ◽  
Expression Data ◽  
Intact Tissue ◽  
Spatially Resolved ◽  
Transcriptomics Data ◽  
High Resolution Images ◽  
Molecular Information

Motivation: Recent advances in spatially-resolved transcriptomics technologies such as the 10x Genomics Visium platform have enabled the generation of transcriptome-wide spatial expression maps within intact tissue. However, steps for processing the high-resolution histology images, extracting relevant features from the images, and integrating them with the gene expression data remain unresolved. Results: We describe VistoSeg, a MATLAB pipeline to process, analyze, and interactively visualize the high-resolution images from the 10x Genomics Visium platform. The output from VistoSeg can then be integrated with the spatial-molecular information in downstream analyses using any programming language, such as R or Python. Availability: VistoSeg is available at https://github.com/LieberInstitute/VistoSeg with a tutorial at http://research.libd.org/VistoSeg

scholarly journals TriGen: A genetic algorithm to mine triclusters in temporal gene expression data

2014 ◽  
Vol 132 ◽  
pp. 42-53 ◽  
Author(s):  
D. Gutiérrez-Avilés ◽  
C. Rubio-Escudero ◽  
F. Martínez-Álvarez ◽  
J.C. Riquelme
Keyword(s):  
Gene Expression ◽  
Genetic Algorithm ◽  
Gene Expression Data ◽  
Expression Data ◽  
Temporal Gene Expression

scholarly journals IRIS-EDA: An integrated RNA-Seq interpretation system for gene expression data analysis

2019 ◽  
Vol 15 (2) ◽  
pp. e1006792 ◽  
Author(s):  
Brandon Monier ◽  
Adam McDermaid ◽  
Cankun Wang ◽  
Jing Zhao ◽  
Allison Miller ◽  
...  
Keyword(s):  
Gene Expression ◽  
Data Analysis ◽  
Gene Expression Data ◽  
Expression Data ◽  
Rna Seq ◽  
Gene Expression Data Analysis ◽  
Interpretation System

Abstract P3-04-10: Comparison between RNA-Seq and Affymetrix gene expression data

2012 ◽  
Author(s):  
D Fumagalli ◽  
B Haibe-Kains ◽  
S Michiels ◽  
DN Brown ◽  
D Gacquer ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Expression Data ◽  
Rna Seq ◽  
Affymetrix Gene Expression
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