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merfish probe counts  (Allen Institute for Brain Science)

 
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    Allen Institute for Brain Science merfish probe counts
    Overall training and architectural scheme for CellTransformer. ( a. ) During training, a single cell is drawn (we denote this the reference cell, boxed in red). We extract the reference cell’s spatial neighbors and partition the group into a masked reference cell and its observed spatial neighbors. ( b. ) Initially, the model encoder receives information about each cell and projects those features to d- dimensional latent variable space. Features interact across cells (tokens) through the self-attention mechanism, which is repeated n times. These per-cell representations and an extra token are then aggregated into a single vector representation, which we refer to as the neighborhood representation. This representation is concatenated to a mask token which is cell type specific and chosen to represent the type of the reference cell. A shallow transformer decoder (dotted lines) further refines these representations and then a linear projection is used to output parameters of a negative binomial distribution modeling of the <t>MERFISH</t> probe counts for the reference cell.
    Merfish Probe Counts, supplied by Allen Institute for Brain Science, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/merfish+probe+counts/bio_rxiv__2024__05__05__592608-165-4-24?v=Allen+Institute+for+Brain+Science
    Average 90 stars, based on 1 article reviews
    merfish probe counts - by Bioz Stars, 2026-07
    90/100 stars

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    1) Product Images from "Data-driven fine-grained region discovery in the mouse brain with transformers"

    Article Title: Data-driven fine-grained region discovery in the mouse brain with transformers

    Journal: bioRxiv

    doi: 10.1101/2024.05.05.592608

    Overall training and architectural scheme for CellTransformer. ( a. ) During training, a single cell is drawn (we denote this the reference cell, boxed in red). We extract the reference cell’s spatial neighbors and partition the group into a masked reference cell and its observed spatial neighbors. ( b. ) Initially, the model encoder receives information about each cell and projects those features to d- dimensional latent variable space. Features interact across cells (tokens) through the self-attention mechanism, which is repeated n times. These per-cell representations and an extra token are then aggregated into a single vector representation, which we refer to as the neighborhood representation. This representation is concatenated to a mask token which is cell type specific and chosen to represent the type of the reference cell. A shallow transformer decoder (dotted lines) further refines these representations and then a linear projection is used to output parameters of a negative binomial distribution modeling of the MERFISH probe counts for the reference cell.
    Figure Legend Snippet: Overall training and architectural scheme for CellTransformer. ( a. ) During training, a single cell is drawn (we denote this the reference cell, boxed in red). We extract the reference cell’s spatial neighbors and partition the group into a masked reference cell and its observed spatial neighbors. ( b. ) Initially, the model encoder receives information about each cell and projects those features to d- dimensional latent variable space. Features interact across cells (tokens) through the self-attention mechanism, which is repeated n times. These per-cell representations and an extra token are then aggregated into a single vector representation, which we refer to as the neighborhood representation. This representation is concatenated to a mask token which is cell type specific and chosen to represent the type of the reference cell. A shallow transformer decoder (dotted lines) further refines these representations and then a linear projection is used to output parameters of a negative binomial distribution modeling of the MERFISH probe counts for the reference cell.

    Techniques Used: Plasmid Preparation



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    merfish probe counts  (Allen Institute for Brain Science)
    90
    Allen Institute for Brain Science merfish probe counts
    Overall training and architectural scheme for CellTransformer. ( a. ) During training, a single cell is drawn (we denote this the reference cell, boxed in red). We extract the reference cell’s spatial neighbors and partition the group into a masked reference cell and its observed spatial neighbors. ( b. ) Initially, the model encoder receives information about each cell and projects those features to d- dimensional latent variable space. Features interact across cells (tokens) through the self-attention mechanism, which is repeated n times. These per-cell representations and an extra token are then aggregated into a single vector representation, which we refer to as the neighborhood representation. This representation is concatenated to a mask token which is cell type specific and chosen to represent the type of the reference cell. A shallow transformer decoder (dotted lines) further refines these representations and then a linear projection is used to output parameters of a negative binomial distribution modeling of the <t>MERFISH</t> probe counts for the reference cell.
    Merfish Probe Counts, supplied by Allen Institute for Brain Science, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/merfish+probe+counts/bio_rxiv__2024__05__05__592608-165-4-24?v=Allen+Institute+for+Brain+Science
    Average 90 stars, based on 1 article reviews
    merfish probe counts - by Bioz Stars, 2026-07
    90/100 stars
    		  Buy from Supplier

    Image Search Results


    Overall training and architectural scheme for CellTransformer. ( a. ) During training, a single cell is drawn (we denote this the reference cell, boxed in red). We extract the reference cell’s spatial neighbors and partition the group into a masked reference cell and its observed spatial neighbors. ( b. ) Initially, the model encoder receives information about each cell and projects those features to d- dimensional latent variable space. Features interact across cells (tokens) through the self-attention mechanism, which is repeated n times. These per-cell representations and an extra token are then aggregated into a single vector representation, which we refer to as the neighborhood representation. This representation is concatenated to a mask token which is cell type specific and chosen to represent the type of the reference cell. A shallow transformer decoder (dotted lines) further refines these representations and then a linear projection is used to output parameters of a negative binomial distribution modeling of the MERFISH probe counts for the reference cell.

    Journal: bioRxiv

    Article Title: Data-driven fine-grained region discovery in the mouse brain with transformers

    doi: 10.1101/2024.05.05.592608

    Figure Lengend Snippet: Overall training and architectural scheme for CellTransformer. ( a. ) During training, a single cell is drawn (we denote this the reference cell, boxed in red). We extract the reference cell’s spatial neighbors and partition the group into a masked reference cell and its observed spatial neighbors. ( b. ) Initially, the model encoder receives information about each cell and projects those features to d- dimensional latent variable space. Features interact across cells (tokens) through the self-attention mechanism, which is repeated n times. These per-cell representations and an extra token are then aggregated into a single vector representation, which we refer to as the neighborhood representation. This representation is concatenated to a mask token which is cell type specific and chosen to represent the type of the reference cell. A shallow transformer decoder (dotted lines) further refines these representations and then a linear projection is used to output parameters of a negative binomial distribution modeling of the MERFISH probe counts for the reference cell.

    Article Snippet: We downloaded the log-transformed MERFISH probe counts and metadata from the Allen Institute public beta ( https://alleninstitute.github.io/abc_atlas_access/intro.html ) access for ABC-MWB for both the Allen Institute for Brain Science and Zhuang lab mice.

    Techniques: Plasmid Preparation