“… After preparing the data, run the following script to identify the pcRegions, pcVariants within and corresponding pcGenes: > bash predict_region_variant_egene.sh CRITICAL: GRPath also allows users to adjust parameters according to their own needs. The following parameters can be adjusted (As for more detailed meaning of the parameters, users may refer to the STAR Methods section in Xi’s work 1 ): …”
Section: Step-by-step Methods Detailsmentioning
confidence: 99%
“… CRITICAL: GRPath also allows users to adjust parameters according to their own needs. The following parameters can be adjusted (As for more detailed meaning of the parameters, users may refer to the STAR Methods section in Xi’s work 1 ):…”
Section: Step-by-step Methods Detailsmentioning
confidence: 99%
“… single_cell_label.txt : disease state (0: control / 1: disease) and cell type of each cell in the gene-by-cell matrix ( Figure 4 B). After preparing the data, run the following script to link the previously identified pcGenes with noteworthy cell types, and obtain the complete pcPaths: > bash predict_regulation_path.sh CRITICAL: Since we used down-sampling technique in this step to avoid the bias caused by unbalanced sample sizes in different classes, 1 , 4 we need to repeat the classification process for multiple rounds and take the average. Here, GRPath also allows users to decide the number of rounds they prefer by parameter r : …”
Section: Step-by-step Methods Detailsmentioning
confidence: 99%
“… CRITICAL: Since we used down-sampling technique in this step to avoid the bias caused by unbalanced sample sizes in different classes, 1 , 4 we need to repeat the classification process for multiple rounds and take the average. Here, GRPath also allows users to decide the number of rounds they prefer by parameter r :…”
Section: Step-by-step Methods Detailsmentioning
confidence: 99%
“… 1 https://github.com/xixi-cathy/GRPath/tree/main/demo_data Software and algorithms GRPath Xi et al. 1 https://github.com/xixi-cathy/GRPath/tree/main/code OpenCausal Li et al. 2 https://github.com/liwenran/OpenCausal Seurat Stuart et al.…”
“… After preparing the data, run the following script to identify the pcRegions, pcVariants within and corresponding pcGenes: > bash predict_region_variant_egene.sh CRITICAL: GRPath also allows users to adjust parameters according to their own needs. The following parameters can be adjusted (As for more detailed meaning of the parameters, users may refer to the STAR Methods section in Xi’s work 1 ): …”
Section: Step-by-step Methods Detailsmentioning
confidence: 99%
“… CRITICAL: GRPath also allows users to adjust parameters according to their own needs. The following parameters can be adjusted (As for more detailed meaning of the parameters, users may refer to the STAR Methods section in Xi’s work 1 ):…”
Section: Step-by-step Methods Detailsmentioning
confidence: 99%
“… single_cell_label.txt : disease state (0: control / 1: disease) and cell type of each cell in the gene-by-cell matrix ( Figure 4 B). After preparing the data, run the following script to link the previously identified pcGenes with noteworthy cell types, and obtain the complete pcPaths: > bash predict_regulation_path.sh CRITICAL: Since we used down-sampling technique in this step to avoid the bias caused by unbalanced sample sizes in different classes, 1 , 4 we need to repeat the classification process for multiple rounds and take the average. Here, GRPath also allows users to decide the number of rounds they prefer by parameter r : …”
Section: Step-by-step Methods Detailsmentioning
confidence: 99%
“… CRITICAL: Since we used down-sampling technique in this step to avoid the bias caused by unbalanced sample sizes in different classes, 1 , 4 we need to repeat the classification process for multiple rounds and take the average. Here, GRPath also allows users to decide the number of rounds they prefer by parameter r :…”
Section: Step-by-step Methods Detailsmentioning
confidence: 99%
“… 1 https://github.com/xixi-cathy/GRPath/tree/main/demo_data Software and algorithms GRPath Xi et al. 1 https://github.com/xixi-cathy/GRPath/tree/main/code OpenCausal Li et al. 2 https://github.com/liwenran/OpenCausal Seurat Stuart et al.…”
Investigating mutations, including single nucleotide variations (SNVs), gene fusions, alternative splicing and copy number variations (CNVs), is fundamental to cancer study. Recent computational methods and biological research have demonstrated the reliability and biological significance of detecting mutations from single-cell transcriptomic data. However, there is a lack of a single-cell-level database containing comprehensive mutation information in all types of cancer. Establishing a single-cell mutation landscape from the huge emerging single-cell transcriptomic data can provide a critical resource for elucidating the mechanisms of tumorigenesis and evolution. Here, we developed scTML (http://sctml.xglab.tech/), the first database offering a pan-cancer single-cell landscape of multiple mutation types. It includes SNVs, insertions/deletions, gene fusions, alternative splicing and CNVs, along with gene expression, cell states and other phenotype information. The data are from 74 datasets with 2 582 633 cells, including 35 full-length (Smart-seq2) transcriptomic single-cell datasets (all publicly available data with raw sequencing files), 23 datasets from 10X technology and 16 spatial transcriptomic datasets. scTML enables users to interactively explore multiple mutation landscapes across tumors or cell types, analyze single-cell-level mutation-phenotype associations and detect cell subclusters of interest. scTML is an important resource that will significantly advance deciphering intra-tumor and inter-tumor heterogeneity, and how mutations shape cell phenotypes.
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