SCISSOR™: a single-cell inferred site-specific omics resource for tumor microenvironment association study

Tumor tissues are heterogeneous with different cell types in tumor microenvironment, which play an important role in tumorigenesis and tumor progression. Several computational algorithms and tools have been developed to infer the cell composition from bulk transcriptome profiles. However, they ignore the tissue specificity and thus a new resource for tissue-specific cell transcriptomic reference is needed for inferring cell composition in tumor microenvironment and exploring their association with clinical outcomes and tumor omics. In this study, we developed SCISSOR™ (https://thecailab.com/scissor/), an online open resource to fulfill that demand by integrating five orthogonal omics data of >6031 large-scale bulk samples, patient clinical outcomes and 451 917 high-granularity tissue-specific single-cell transcriptomic profiles of 16 cancer types. SCISSOR™ provides five major analysis modules that enable flexible modeling with adjustable parameters and dynamic visualization approaches. SCISSOR™ is valuable as a new resource for promoting tumor heterogeneity and tumor–tumor microenvironment cell interaction research, by delineating cells in the tissue-specific tumor microenvironment and characterizing their associations with tumor omics and clinical outcomes.
Graphical Abstract Graphical AbstractSCISSOR™ preprocessed and hosted The Cancer Genome Atlas (TCGA) bulk transcriptomic data and tissue-specific single-cell transcriptomic data. Using these data, SCISSOR™ infers the proportion of tissue-specific cell types in tumor microenvironment. With the omics data and survival data from TCGA, the relationship and interaction can be tested and visualized in five modules, including Overview, Survival, Gene–TME cell correlation with two submodules for gene expression and genetic aberration, Genome-wide TME–omics association and Deconvolution. The Deconvolution module will allow users to upload their bulk transcriptome data and perform deconvolution with tissue-specific single-cell transcriptome profile references and deconvolution methods specified by users. The result will be automatically sent by email.