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: 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: