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spatial transcriptomics deconvolution by topic modeling (stride) method  (Spatial Transcriptomics Inc)

 
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    Spatial Transcriptomics Inc spatial transcriptomics deconvolution by topic modeling (stride) method
    Spatial Transcriptomics Deconvolution By Topic Modeling (Stride) Method, supplied by Spatial Transcriptomics Inc, 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/deconvolution+model/spatial+transcriptomics+deconvolution+by+topic+modeling++stride++method/pm37919720-115-26-20
    Average 90 stars, based on 1 article reviews
    spatial transcriptomics deconvolution by topic modeling (stride) method - by Bioz Stars, 2026-09
    90/100 stars

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    Related Articles

    other:

    Article Title: Deciphering the molecular and cellular atlas of immune cells in septic patients with different bacterial infections.
    Article Snippet: For analysis of single-cell and ST data from mouse kidney tissue, the two types of data were integrated using the Spatial Transcriptomics Deconvolution by Topic Modeling (STRIDE) method [33].

    Article Title: Deciphering the molecular and cellular atlas of immune cells in septic patients with different bacterial infections
    Article Snippet: For analysis of single-cell and ST data from mouse kidney tissue, the two types of data were integrated using the Spatial Transcriptomics Deconvolution by Topic Modeling (STRIDE) method [ ].

    Sequencing:

    Article Title: Integration tools for scRNA-seq data and spatial transcriptomics sequencing data.
    Article Snippet: Numerous methods have been developed to integrate spatial transcriptomics sequencing data with single-cell RNA sequencing (scRNAseq) data.. Continuous development and improvement of these methods offer multiple options for integrating and analyzing scRNA-seq and spatial transcriptomics data based on diverse research inquiries.. However, each method has its own advantages, limitations and scope of application.



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    Image Search Results


    A flow chart illustrating the complete noise correction process for blood flow (BF) measurements, including evaluation using digital perfusion phantom (DPP) (starting from the first block) and evaluation using a clinical dataset (starting from the second block). For the DPP analysis, each GTBF value is simulated using two independent sets of 576 noise-impacted TACs, resulting in BF1 and BF2 estimates for random error calculation. This process is repeated for 28 GTBF values, totaling 16,128 TACs. For the clinical dataset, patient BF values calculated using the deconvolution model from Mayer’s study were used as input for the noise-impacted BF maps. BFD represents the noise-impacted BF measurements, which need to be corrected. IRF is the impulse response function, AIF is the arterial input function, TAC represents the tissue attenuation curve, and GTBF is the ground-truth blood flow. BFD corr (i) represents the noise-corrected BF measurement for the i th iteration. The random error and model error calculations are also shown in the flow chart. This iterative process for DPP continues until BFD corr aligns with GTBF or until the error between GTBF and corrected measurements is minimized to an acceptable threshold.

    Journal: Scientific Reports

    Article Title: Model based noise correction enhances the accuracy of pancreatic CT perfusion blood flow measurements

    doi: 10.1038/s41598-025-24482-x

    Figure Lengend Snippet: A flow chart illustrating the complete noise correction process for blood flow (BF) measurements, including evaluation using digital perfusion phantom (DPP) (starting from the first block) and evaluation using a clinical dataset (starting from the second block). For the DPP analysis, each GTBF value is simulated using two independent sets of 576 noise-impacted TACs, resulting in BF1 and BF2 estimates for random error calculation. This process is repeated for 28 GTBF values, totaling 16,128 TACs. For the clinical dataset, patient BF values calculated using the deconvolution model from Mayer’s study were used as input for the noise-impacted BF maps. BFD represents the noise-impacted BF measurements, which need to be corrected. IRF is the impulse response function, AIF is the arterial input function, TAC represents the tissue attenuation curve, and GTBF is the ground-truth blood flow. BFD corr (i) represents the noise-corrected BF measurement for the i th iteration. The random error and model error calculations are also shown in the flow chart. This iterative process for DPP continues until BFD corr aligns with GTBF or until the error between GTBF and corrected measurements is minimized to an acceptable threshold.

    Article Snippet: All evaluations in this study were performed using a commercial deconvolution model (syngo.via, Siemens Healthineers) with fixed reconstruction parameters such as slice thickness, reconstruction kernel, and matrix size selected to reflect standard clinical CTp practice.

    Techniques: Blocking Assay