human dlpfc dataset  (10X Genomics)

Journal: Briefings in Bioinformatics

Article Title: SpaGIC: graph-informed clustering in spatial transcriptomics via self-supervised contrastive learning

doi: 10.1093/bib/bbae578

Figure Lengend Snippet: Spatial domains identification and data denoising on the DLPFC dataset. ( A ) Manual annotation of the DLPFC 151673 slice. ( B ) ARI boxplots of eight methods on 12 DLPFC slices. In the boxplot, the center line denotes the median, box limits denote the upper and lower quartiles, and whiskers denote the 1.5 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\times $\end{document} interquartile range. ( C ) The spatial domains identified by Scanpy, SpaGCN, DeepST, SEDR, STAGATE, Spatial-MGCN, GraphST, and SpaGIC on the DLPFC 151673 slice. ( D ) UMAP visualization and PAGA graph generated based on the embedding by these methods on the 151673 slice. ( E ) Visualization of the raw expression of layer marker genes in the 151673 slice, both before and after denoising by SpaGIC.

Article Snippet: Specifically, (i) the LIBD human dorsolateral prefrontal cortex (DLPFC) dataset: http://spatial.libd.org/spatialLIBD/ ; (ii) the 10x Visium human breast cancer dataset: https://www.10xgenomics.com/datasets/human-breast-cancer-block-a-section-1-1-standard-1-1-0 ; (iii) the anterior section of the 10x Visium mouse brain: https://www.10xgenomics.com/resources/datasets/mouse-brain-serial-section-1-sagittal-anterior-1-standard-1-1-0 ; (iv) the Stereo-seq mouse olfactory bulb dataset: https://github.com/STOmics/SAW/tree/main/Test_Data ; (v) the Slide-seqV2 mouse olfactory bulb dataset: https://singlecell.broadinstitute.org/single_cell/study/SCP815/highly-sensitive-spatial-transcriptomics-at-near-cellular-resolution-with-slide-seqv2#study-summary ; (vi) the STARmap mouse visual cortex dataset: https://drive.google.com/drive/folders/1I1nxheWlc2RXSdiv24dex3YRaEh780my?usp=sharing ; (vii) the osmFISH mouse somatosensory cortex dataset: https://linnarssonlab.org/osmFISH/ .

Techniques: Generated, Expressing, Marker

Journal: Briefings in Bioinformatics

Article Title: SpaGIC: graph-informed clustering in spatial transcriptomics via self-supervised contrastive learning

doi: 10.1093/bib/bbae578

Figure Lengend Snippet: Joint analysis on the DLPFC dataset. ( A ) Aligned spatial domain identified by Harmony, STAGATE, SEDR, and SpaGIC via joint analysis of four slices of sample 3 (151673-151676). ( B ) UMAP visualization of embeddings colored by slices (top), ground truth (middle), and identified domains (bottom).

Article Snippet: Specifically, (i) the LIBD human dorsolateral prefrontal cortex (DLPFC) dataset: http://spatial.libd.org/spatialLIBD/ ; (ii) the 10x Visium human breast cancer dataset: https://www.10xgenomics.com/datasets/human-breast-cancer-block-a-section-1-1-standard-1-1-0 ; (iii) the anterior section of the 10x Visium mouse brain: https://www.10xgenomics.com/resources/datasets/mouse-brain-serial-section-1-sagittal-anterior-1-standard-1-1-0 ; (iv) the Stereo-seq mouse olfactory bulb dataset: https://github.com/STOmics/SAW/tree/main/Test_Data ; (v) the Slide-seqV2 mouse olfactory bulb dataset: https://singlecell.broadinstitute.org/single_cell/study/SCP815/highly-sensitive-spatial-transcriptomics-at-near-cellular-resolution-with-slide-seqv2#study-summary ; (vi) the STARmap mouse visual cortex dataset: https://drive.google.com/drive/folders/1I1nxheWlc2RXSdiv24dex3YRaEh780my?usp=sharing ; (vii) the osmFISH mouse somatosensory cortex dataset: https://linnarssonlab.org/osmFISH/ .

Techniques:

Journal: Briefings in Bioinformatics

Article Title: SpaGIC: graph-informed clustering in spatial transcriptomics via self-supervised contrastive learning

doi: 10.1093/bib/bbae578

Figure Lengend Snippet: The ARI boxplots of SpaGIC and its variants on the DLPFC dataset.

Article Snippet: Specifically, (i) the LIBD human dorsolateral prefrontal cortex (DLPFC) dataset: http://spatial.libd.org/spatialLIBD/ ; (ii) the 10x Visium human breast cancer dataset: https://www.10xgenomics.com/datasets/human-breast-cancer-block-a-section-1-1-standard-1-1-0 ; (iii) the anterior section of the 10x Visium mouse brain: https://www.10xgenomics.com/resources/datasets/mouse-brain-serial-section-1-sagittal-anterior-1-standard-1-1-0 ; (iv) the Stereo-seq mouse olfactory bulb dataset: https://github.com/STOmics/SAW/tree/main/Test_Data ; (v) the Slide-seqV2 mouse olfactory bulb dataset: https://singlecell.broadinstitute.org/single_cell/study/SCP815/highly-sensitive-spatial-transcriptomics-at-near-cellular-resolution-with-slide-seqv2#study-summary ; (vi) the STARmap mouse visual cortex dataset: https://drive.google.com/drive/folders/1I1nxheWlc2RXSdiv24dex3YRaEh780my?usp=sharing ; (vii) the osmFISH mouse somatosensory cortex dataset: https://linnarssonlab.org/osmFISH/ .

Techniques:

Journal: Computational and Structural Biotechnology Journal

Article Title: Spatial domains identification in spatial transcriptomics using modality-aware and subspace-enhanced graph contrastive learning

doi: 10.1016/j.csbj.2024.10.029

Figure Lengend Snippet: GRAS4T improved the accuracy of identifying layer structures within the DLPFC dataset compared to other methods. (a) Boxplot of ARI values across all sections of the DLPFC dataset for six methods. (b) The H&E image and manual annotation of slice 151672. (c) The spatial domains in six methods for slice 151672. (d) UMAP visualizations and PAGA graphs in six methods for slice 151672.

Article Snippet: The ST datasets supporting the findings of this study are all publicly available. (1) The DLPFC dataset is available at http://research.libd.org/spatialLIBD/ . (2) The HER2+ dataset generated by spatial transcriptomics platform is accessed at https://github.com/almaan/her2st . (3) The mouse visual cortex dataset generated by STARmap is available at https://www.dropbox.com/sh/f7ebheru1lbz91s/AADm6D54GSEFXB1feRy6OSASa/visual_1020/20180505_BY3_1kgenes?dl=0&subfolder_nav_tracking=1 . (4) The adult mouse brain dataset is accessed at https://www.10xgenomics.com/resources/datasets . (5) The Stereo-seq mouse olfactory bulb dataset is available at https://github.com/JinmiaoChenLab/SEDR_analyses/ . (6) The MERFISH dataset is accessed at https://datadryad.org/stash/dataset/doi:10.5061/dryad.8t8s248 . (7) The human breast cancer dataset is available at https://www.10xgenomics.com/resources/datasets . (8) The anterior and posterior sections of the mouse brain are accessed at https://www.10xgenomics.com/resources/datasets and the Allen Brain Atlas reference is available at https://mouse.brain-map.org/static/atlas .

Techniques:

Journal: Advanced Science

Article Title: Spatially Informed Graph Structure Learning Extracts Insights from Spatial Transcriptomics

doi: 10.1002/advs.202403572

Figure Lengend Snippet: STAGUE achieves the best overall performance in spatial clustering. Comparison of clustering methods across four dataset groups using ARI (left) and AMI (right): A) Real#1, C) Simulated#1, D) Simulated#2, and E) Real#2. Each point represents the result of the corresponding method on one dataset. The black center line indicates the mean value across all datasets. Boxplot: center line, the median; upper and lower edges, the interquartile range; whiskers, 1.5 × interquartile range. B) ARI performance of selected representative methods under different dropout rates, ranging from 0.1 to 0.7 with a step of 0.05. See Figure (Supporting Information) for the corresponding AMI performance. Datasets include V1 from mouse primary visual cortex, BZ5 from mouse medial prefrontal cortex, and slices #151507 and #151672 from human dorsolateral prefrontal cortex (DLPFC) (see Experimental Section). SpatialPCA failed at higher dropout rates for V1 and BZ5.

Article Snippet: Specifically, we use slice #151672 of the DLPFC dataset [ ] and the BRCA Block A Section sample from the 10x Genomics database as representative examples.

Techniques: Comparison

Journal: Advanced Science

Article Title: Spatially Informed Graph Structure Learning Extracts Insights from Spatial Transcriptomics

doi: 10.1002/advs.202403572

Figure Lengend Snippet: STAGUE better demarcates the laminar structure of mouse brain tissue and dissects finer‐grained structures for human brain and breast cancer tissues. A) Clustering results of different methods on the STARmap mouse V1 dataset and true annotations. The result from a single run of each method is selected for demonstration. See Figure (Supporting Information) for the demonstration of other methods. B) Expression pattern of DEGs in different clusters detected by STAGUE. C) UMAP plots with ground‐truth labels overlaid with PAGA trajectory inference results using latent embeddings from Scanpy, SpaGCN, and STAGUE. D) Top panel: Clustering results of different methods on the Stereo‐seq mouse olfactory bulb dataset and true annotations. See Figure (Supporting Information) for the results of other methods. Bottom panel: Visualization of the clusters identified by STAGUE, with cluster colors adjusted to match true annotations. E) Clustering results of different methods on slice #151672 of the human DLPFC dataset and true annotations. See Figure (Supporting Information) for the results of other methods. F) Dotplot of the representative DEGs of cluster 0 in comparison to other clusters. G) Clustering results of STAGUE on the 10x Visium BRCA dataset with different cluster numbers n c . H) Pathway enrichment analysis of the H1 sub‐region. I) Expression pattern of the most significantly upregulated and downregulated genes in H1.

Article Snippet: Specifically, we use slice #151672 of the DLPFC dataset [ ] and the BRCA Block A Section sample from the 10x Genomics database as representative examples.

Techniques: Expressing, Comparison

Journal: Briefings in Bioinformatics

Article Title: CHAI: consensus clustering through similarity matrix integration for cell-type identification

doi: 10.1093/bib/bbae411

Figure Lengend Snippet: CHAI-ST benchmarking on human DLPFC 10X visium datasets and Savas breast cancer dataset.

Article Snippet: Since the benchmarking results in show that integrating the spatial transcriptomic results into CHAI-ST-SNF at the second level yielded the best results, we chose to use this method for our evaluation, in addition to CHAI-AvgSim-ST. Sicnce STGNNks relies on 10X Genomics Visium datasets as input, we compared both CHAI-ST methods to the baseline methods on three human DLPFC 10X Visium datasets [ , ].

Techniques: