SCAN_Space

scRNA-seq technology cannot analyze the gene expression profile in spatial location, so spatial transcriptomics (ST) technology came into being, which can reveal the spatial structure of complex transcription networks in brain. The SCAN_Space module provides the ST data sets of various neural tissues from eight species, which can help users to clarify the internal compositions and spatial distribution of the nervous system under different conditions, and further having a deeper understanding of the development of the nervous system and disease occurrence. Users can directly select species and tissue types on this page, and then the page will automatically filter out the available data sets and display brief information about the data sets (including species classification, species Latin name, common name of species, tissue type, normal / disease status, ST technology, and data sources). In the data list, nine icons were provided as the entries of the data visualization interactive interfaces and the detailed information page, respectively.

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Select tissue


Select data set {{getName(Cancer)}}

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SVG {{SCAN_ST_ID}}

g: the gene name; FSV: fraction of variance explained by spatial variation; M: a parameter representing the complexity of the model; l: a parameter indicating the genes distance scale a gene changes expression over; max_delta: a parameter value estimated in the maximum likelihood estimation process; max_ll: the maximum value of Log-Likelihood; max_mu_hat: the maximum mean value of the estimation obtained in the calculation process; max_s2_t_hat: the maximum variance obtained in the calculation process; model: model used; n: sample size; s2_FSV: variance of Structured Noise in gene expression data; s2_logdelta: the variance of the estimate of the log_delta parameter; time: time taken for calculation; BIC: Bayesian Information Criterion; max_ll_null: the best fitting degree of observation data under the empty model; LLR: Log-Likelihood Ratio; pval: the p-value for spatial differential expression; qval: the corrected p-value after correcting for multiple testing;

For analysis of SVGs, Scanpy was used to read the h5ad data of the spatial transcriptome, and the “pd.DataFrame” function was used to convert the gene expression matrices and coordinates of the spots into dataframe format. These parameters were transferred into the “SpatialDE.run” function in the SpatialDE package for SVGs calculation. The resultant gene lists were sorted in ascending order based on q values.(PMID: 29553579)

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Enrichment {{SCAN_ST_ID}} Download
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    Merge

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    Clustering of spots


    marker of cluster


    expression pattern of marker gene


    Top 20 highly expressed genes


    Protein protein interactions of spatially variable genes



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