Spatial Transcriptomics:Article Title: Identification of Hypoxia-ALCAM high Macrophage- Exhausted T Cell Axis in Tumor Microenvironment Remodeling for Immunotherapy Resistance.
Article Snippet: .. Spatial Transcriptomics: The prostate spatial transcriptomic data, including count matrices and images, were provided by Andrew Erickson et al. from the Mendeley database[24] Spatial transcriptomics of CRC were obtained from Genome Sequence Archive with accessible ID HRA000979[39] BRCA and OC spatial transcriptomic data were obtained from 10X genomics official website (https://support.10xgenomics. com spatial-gene-expression/datasets), ccRCC (GSE175540)[41] and SCC (GSE144240)[61] Spatial transcriptomics of 67 tumor tissues with covering 14 cancer types and with clear tumor boundaries were collected from the web available portal SpatialTME (https://www.spatialtme.yelab.site/)[62] Single-Cell Transcriptomics: Thirty-five single-cell transcriptomics datasets with metadata, 38 CD8+ T cells datasets and 34 CD4+ T cells datasets were obtained from Tumor Immune Single-cell Hub (TISCH)[45] To analyze T cell trajectory, we obtained human single-cell gene expression matrices from European Genome-phenome Archive (EGA) under study no. EGAS00001004809 and no. EGAD00001006608[63] For mouse T cells, we used single-cell expression matrices collected by Massimo Andreatta et al.[46] (https://github.com/carmonalab/ProjecTILs_CaseStudies). ..
Sequencing:Article Title: Identification of Hypoxia-ALCAM high Macrophage- Exhausted T Cell Axis in Tumor Microenvironment Remodeling for Immunotherapy Resistance.
Article Snippet: .. Spatial Transcriptomics: The prostate spatial transcriptomic data, including count matrices and images, were provided by Andrew Erickson et al. from the Mendeley database[24] Spatial transcriptomics of CRC were obtained from Genome Sequence Archive with accessible ID HRA000979[39] BRCA and OC spatial transcriptomic data were obtained from 10X genomics official website (https://support.10xgenomics. com spatial-gene-expression/datasets), ccRCC (GSE175540)[41] and SCC (GSE144240)[61] Spatial transcriptomics of 67 tumor tissues with covering 14 cancer types and with clear tumor boundaries were collected from the web available portal SpatialTME (https://www.spatialtme.yelab.site/)[62] Single-Cell Transcriptomics: Thirty-five single-cell transcriptomics datasets with metadata, 38 CD8+ T cells datasets and 34 CD4+ T cells datasets were obtained from Tumor Immune Single-cell Hub (TISCH)[45] To analyze T cell trajectory, we obtained human single-cell gene expression matrices from European Genome-phenome Archive (EGA) under study no. EGAS00001004809 and no. EGAD00001006608[63] For mouse T cells, we used single-cell expression matrices collected by Massimo Andreatta et al.[46] (https://github.com/carmonalab/ProjecTILs_CaseStudies). ..
Single Cell:Article Title: Identification of Hypoxia-ALCAM high Macrophage- Exhausted T Cell Axis in Tumor Microenvironment Remodeling for Immunotherapy Resistance.
Article Snippet: .. Spatial Transcriptomics: The prostate spatial transcriptomic data, including count matrices and images, were provided by Andrew Erickson et al. from the Mendeley database[24] Spatial transcriptomics of CRC were obtained from Genome Sequence Archive with accessible ID HRA000979[39] BRCA and OC spatial transcriptomic data were obtained from 10X genomics official website (https://support.10xgenomics. com spatial-gene-expression/datasets), ccRCC (GSE175540)[41] and SCC (GSE144240)[61] Spatial transcriptomics of 67 tumor tissues with covering 14 cancer types and with clear tumor boundaries were collected from the web available portal SpatialTME (https://www.spatialtme.yelab.site/)[62] Single-Cell Transcriptomics: Thirty-five single-cell transcriptomics datasets with metadata, 38 CD8+ T cells datasets and 34 CD4+ T cells datasets were obtained from Tumor Immune Single-cell Hub (TISCH)[45] To analyze T cell trajectory, we obtained human single-cell gene expression matrices from European Genome-phenome Archive (EGA) under study no. EGAS00001004809 and no. EGAD00001006608[63] For mouse T cells, we used single-cell expression matrices collected by Massimo Andreatta et al.[46] (https://github.com/carmonalab/ProjecTILs_CaseStudies). ..
Transcriptomics:Article Title: Identification of Hypoxia-ALCAM high Macrophage- Exhausted T Cell Axis in Tumor Microenvironment Remodeling for Immunotherapy Resistance.
Article Snippet: .. Spatial Transcriptomics: The prostate spatial transcriptomic data, including count matrices and images, were provided by Andrew Erickson et al. from the Mendeley database[24] Spatial transcriptomics of CRC were obtained from Genome Sequence Archive with accessible ID HRA000979[39] BRCA and OC spatial transcriptomic data were obtained from 10X genomics official website (https://support.10xgenomics. com spatial-gene-expression/datasets), ccRCC (GSE175540)[41] and SCC (GSE144240)[61] Spatial transcriptomics of 67 tumor tissues with covering 14 cancer types and with clear tumor boundaries were collected from the web available portal SpatialTME (https://www.spatialtme.yelab.site/)[62] Single-Cell Transcriptomics: Thirty-five single-cell transcriptomics datasets with metadata, 38 CD8+ T cells datasets and 34 CD4+ T cells datasets were obtained from Tumor Immune Single-cell Hub (TISCH)[45] To analyze T cell trajectory, we obtained human single-cell gene expression matrices from European Genome-phenome Archive (EGA) under study no. EGAS00001004809 and no. EGAD00001006608[63] For mouse T cells, we used single-cell expression matrices collected by Massimo Andreatta et al.[46] (https://github.com/carmonalab/ProjecTILs_CaseStudies). ..
Gene Expression:Article Title: Identification of Hypoxia-ALCAM high Macrophage- Exhausted T Cell Axis in Tumor Microenvironment Remodeling for Immunotherapy Resistance.
Article Snippet: .. Spatial Transcriptomics: The prostate spatial transcriptomic data, including count matrices and images, were provided by Andrew Erickson et al. from the Mendeley database[24] Spatial transcriptomics of CRC were obtained from Genome Sequence Archive with accessible ID HRA000979[39] BRCA and OC spatial transcriptomic data were obtained from 10X genomics official website (https://support.10xgenomics. com spatial-gene-expression/datasets), ccRCC (GSE175540)[41] and SCC (GSE144240)[61] Spatial transcriptomics of 67 tumor tissues with covering 14 cancer types and with clear tumor boundaries were collected from the web available portal SpatialTME (https://www.spatialtme.yelab.site/)[62] Single-Cell Transcriptomics: Thirty-five single-cell transcriptomics datasets with metadata, 38 CD8+ T cells datasets and 34 CD4+ T cells datasets were obtained from Tumor Immune Single-cell Hub (TISCH)[45] To analyze T cell trajectory, we obtained human single-cell gene expression matrices from European Genome-phenome Archive (EGA) under study no. EGAS00001004809 and no. EGAD00001006608[63] For mouse T cells, we used single-cell expression matrices collected by Massimo Andreatta et al.[46] (https://github.com/carmonalab/ProjecTILs_CaseStudies). ..
Expressing:Article Title: Identification of Hypoxia-ALCAM high Macrophage- Exhausted T Cell Axis in Tumor Microenvironment Remodeling for Immunotherapy Resistance.
Article Snippet: .. Spatial Transcriptomics: The prostate spatial transcriptomic data, including count matrices and images, were provided by Andrew Erickson et al. from the Mendeley database[24] Spatial transcriptomics of CRC were obtained from Genome Sequence Archive with accessible ID HRA000979[39] BRCA and OC spatial transcriptomic data were obtained from 10X genomics official website (https://support.10xgenomics. com spatial-gene-expression/datasets), ccRCC (GSE175540)[41] and SCC (GSE144240)[61] Spatial transcriptomics of 67 tumor tissues with covering 14 cancer types and with clear tumor boundaries were collected from the web available portal SpatialTME (https://www.spatialtme.yelab.site/)[62] Single-Cell Transcriptomics: Thirty-five single-cell transcriptomics datasets with metadata, 38 CD8+ T cells datasets and 34 CD4+ T cells datasets were obtained from Tumor Immune Single-cell Hub (TISCH)[45] To analyze T cell trajectory, we obtained human single-cell gene expression matrices from European Genome-phenome Archive (EGA) under study no. EGAS00001004809 and no. EGAD00001006608[63] For mouse T cells, we used single-cell expression matrices collected by Massimo Andreatta et al.[46] (https://github.com/carmonalab/ProjecTILs_CaseStudies). ..
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