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Lab information [ Lab website | CIRM grants ]

Experimental design

Flowchart

Summary

Recently high-throughput image-based transcriptomic methods were developed and enabled researchers to spatially resolve gene expression variation at the molecular level for the first time. In this work, we develop a general analysis tool to quantitatively study the spatial correlations of gene expression in fixed tissue sections. As an illustration, we analyze the spatial distribution of single mRNA molecules measured by in situ sequencing on human fetal pancreas at three developmental time points 80, 87 and 117 days post-fertilization. We develop a density profile-based method to capture the spatial relationship between gene expression and other morphological features of the tissue sample such as position of nuclei and endocrine cells of the pancreas. In addition, we build a statistical model to characterize correlations in the spatial distribution of the expression level among different genes. This model enables us to infer the inhibitory and clustering effects throughout different time points. Our analysis framework is applicable to a wide variety of spatially-resolved transcriptomic data to derive biological insights.

Publications

Modeling Spatial Correlation of Transcripts With Application to Developing Pancreas

Primary files

Lab analysis

Biomarkers, protocols, clustering or other supplementary files supplied by the lab

Secondary analysis

Expression Matrix (lab-generated) | Expression matrix (UCSC) | QC Metrics

CESCG Center Standard Analysis

FastQC | Picard | RSEM | STAR | bigWig

Tertiary analysis

Cell Browser

Sample Psychic

SCIMITAR

RIGGLE

SurfacePlots

JCVI BioMarkers


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