This app serves as an archive version of UCSCXenaShiny, where we only focus on maintaining its normal running and NOT ensuring bug fixes. For updated features, please refer to v2 (https://github.com/openbiox/UCSCXenaShiny).

Data Portal Summary

UCSCXenaShinyV1 v1.1.10 based on UCSCXenaTools v1.5.0
Data source: UCSC Xena or Hiplot mirror

DATA HUBS

11

COHORTS

160

DATASETS

1677

SAMPLES

~2,000,000

PRIMARY SITES

~37

DATA SUBTYPES

Select a genomic profile:

mRNA Expression

Transcript Expression

DNA Methylation

Protein Expression

miRNA Expression

Copy Number Variation

Copy Number Variation (thresholded)

Input a gene or formula (as signature)

Select Gender for plot

Height

Width

Choose plot format

pdf

png

Genomic profile data source

NOTEs:

1. GISTIC2 thresholded copy number -2,-1,0,1,2, representing homozygous deletion,single copy deletion,diploid normal copy,low-level copy number amplification,or high-level copy number amplification

2. Genomic profile data source

Save as csv

Select a genomic profile:

mRNA Expression

Transcript Expression

DNA Methylation

Protein Expression

miRNA Expression

Copy Number Variation

Input a gene or formula (as signature)

Select Measure for plot

Select Threshold for plot

First color

Second color

Third color

Height

Width

Choose plot format

pdf

png

NOTEs:

1. We define gene in certain cancer type as risky (log(Hazard Ratio) > 0) or protective (log(Hazard Ratio) < 0) or NS (No statistical significance, P value > 0.05)

2. We divide patients into different groups for comparison according to gene expression, you could choose the threshold for grouping (0.5 by default)

3. Genomic profile data source

Save as csv

Select a genomic profile:

mRNA Expression

Transcript Expression

DNA Methylation

Protein Expression

miRNA Expression

Copy Number Variation

Input a gene or formula (as signature)

Select the immune signature source

Select Correlation method

Height

Width

Choose plot format

pdf

png

NOTEs:

Genomic profile data source
Immune signature data source

Save as csv

Select a genomic profile:

mRNA Expression

Transcript Expression

DNA Methylation

Protein Expression

miRNA Expression

Copy Number Variation

Input a gene or formula (as signature)

Cell types :

Select Correlation method

Height

Width

Choose plot format

pdf

png

NOTEs:

1. TIL data source

2. Genomic profile data source

Save as csv

Select a genomic profile:

mRNA Expression

Transcript Expression

DNA Methylation

Protein Expression

miRNA Expression

Copy Number Variation

Input a gene or formula (as signature)

Select a feature

Select Correlation method

Height

Width

Choose plot format

pdf

png

NOTEs:

1. The data query may take some time based on your network. Wait until a plot shows

2. If a void plot shows, please check your input

3. Genomic profile data source

4. Check description of this link for TMB/Stemness/MSI data source

Save as csv

Select a genomic profile:

mRNA Expression

Protein Expression

Copy Number Variation

Height

Width

Choose plot format

pdf

png

Genomic profile data source

Save as csv

Input a gene or list (as signature)

Plot output form

X axis type

Height

Width

Choose plot format (only support ggplot2)

pdf

png

Method:

Analyze partial correlation of gene-drug association after controlling for tissue average expression.

When there are multiple genes, geometrical mean of expression of these genes are used as a signature.

NOTEs: CCLE gene expression data was quantile normalized among all different cell lines for partial correlation, and then Z-score normalization was applied in each tissue to calculate the expression difference between High-Low (use median as cutoff) IC50 groups. The X axis indicates mean/median expression difference across tissues.

Save as csv

Input a gene or list (as signature)

Show P value

High group color

Low group color

Filter Tissue

Choose a transparent value

Height

Width

Choose plot format

pdf

png

Method:

Analyze difference of drug response (IC50 value (uM)) between gene (or signature) high and low expression.

When there are multiple genes, geometrical mean of expression of these genes are used as a signature.

NOTEs: You can select multiple tissues, even ALL for all tissues. In this case 'number_of_cell_lines' indicates sample size for each gene-drug group instead of gene-drug-tissue group.

Save as csv

Global setting:

Usage

Home Page
Repository Page
General Analysis Page
- Overview
- Video
Quick PanCan Analysis Page
- Overview
- Notes
- Videos
Global Setting Page
Feature: Genomic Signature
- Overview
- Video
Use UCSCXenaShinyV1 R package

Home Page

Overview

Home page gives you a quick overview of this tool, which includes

basic information and statistics about supported data hubs.
current version of this tool and its key dependent package “UCSCXenaTools”.
where the datasets come from.

If you use this tool locally, sometimes the version number can help you check if there is a update of this tool and track any bug when you report to the developers.

Quick search

The web GUI is mainly designed and developed for cancer researchers without programming experience or want to quickly explore the flash of inspiration. We provide a search bar on the top of this page, you can type the gene symbol (e.g. TP53) and a window should pop up to show the pan-cancer expression distribution of this gene.

If you want to keep this data, your either right click to save this image or please go to module “TCGA: Molecular Profile Distribution Across Cancer Types (Tumor VS Normal)” under “Quick PanCan Analysis” page. The analysis module is more powerful and provides many features to custom this figure for academic use.

NOTE: Although we label the module with TCGA, the data come from toil data hub, it contains samples from TCGA, TARGET and GTEx databases.

Video

Click watch operations on bilibili

Repository Page

Overview

This page provides many features related to datasets of UCSC Xena and the entry to “General Analysis”. This section we will only focus on the former part, for “General Analysis”, please refer to the next section.

Repository page shows multiple filters on the left panels, a dataset table on the main panel and 3 buttons on the bottom. These widgets are combined to create a full-feature page for dataset filtering, checking and downloading.

Dataset filter

At default, when you open this page, you will find a table showing hundreds of datasets. To quickly locate what you are looking for, you can use filters from different aspects:

Data hub (when you don’t select any data hub, all datasets will be shown in the right data table).
Cohort name. A cohort contains multiple datasets for same samples. So it is the common target for a analysis.
Data type.
Data subtype.

Besides, you can use the “Filter with keyword” search bar on the top-right of the dataset table to filter the datasets with any keyword.

Dataset selection

The filtered dataset table are not selected, you have to use your mouse to click one or more data rows to select corresponding datasets. After your selection, the tool will repond you selection to query the extra information (e.g., download link, the corresponding UCSC Xena page) of the datasets from UCSC Xena server.

If you just want to download datasets, a simple way is to click the download link one by one.

After dataset selection, you can use the 3 buttons below the table to

Check the metadata of the datasets.
Request (download) datasets by multiple ways.
Analyze datasets (when you click this button, it will automatically jump to “General Analysis” page).

Dataset check

After you click the “Show Metadata” button, a windown will pop up to show the detail metadata of your selected datasets.

Dataset download

After you click the “Request Data” button, a windown will pop up to show the key information of the datasets and 3 ways to download the data:

“Download data directly” will download datasets from UCSC Xena data hubs and zip the datasets to provides download by your browser.
“Batch download” will provide a .sh script to you, so you can run the download process in a Unix (Linux/MacOS) environment (wget is required in the environment). This is recommended if you selected many big datasets.
“Copy R download code” will show the R code to download the datasets, so you can integerate it with your following data analysis code and enhance data analysis reproducibility.

Video

Click watch operations on bilibili

General Analysis Page

Overview

The 3rd button below the dataset table in “Repository” page is “Analyze Data”. When you click the button, the tool will be automatically switched to this “General Analysis” page.

(To enhance the analysis, all phenotype datasets in the same cohort will be automatically queried and loaded.)

4 analysis modules are provided for solving common analysis tasks.

Scatter-Correlation: analyze and visualize correlation for two variables.
Matrix-Correlation: analyze and visualize correlation for two or more variables.
Group-Comparison: analyze and visualize numeric difference of a continuous variable between multiple groups of a variable.
Survival-Analysis: analyze and visualize survival curves between multiple groups of a variable.

On the right panel of each analysis module, we provide a sample filter. If you want to use a sample subset of the dataset for analysis, you should click the “Click to filter!” button and follow the hint step by step.

Video

Click watch operations on bilibili

Quick PanCan Analysis Page

Overview

This page provides many indepedent analysis modules for TCGA and CCLE related data. They are very simple, useful and easy to get started.

For most of modules provided here, multiple types of molecules including mRNA, transcript, mutation, etc. are supported. In most cases, you can just put gene symbol (e.g., TP53). When you choose to explore the transcipt and miRNA expression, you need iuput the Ensembl ID (e.g., ENST00000000233) and miRNA ID (e.g., hsa-miR-128-3p).

Notes

The DNA methylation data is gene level, it is obtained from the average of all probes mapping to a gene.
The copy number varation value is obtained from the GISTIC2 software, if the numeric value is used, 0 indicates the normal copy.
The definition and data of four major clinical outcome endpoints are from reference doi: 10.1016/j.cell.2018.02.052.
For TCGA survival analysis, when you choose Auto for cutoff mode, the function of surv_cutpoint {survminer package} will be called. The method is described as Determine the optimal cutpoint for one or multiple continuous variables at once, using the maximally selected rank statistics from the ‘maxstat’ R package. This is an outcome-oriented methods providing a value of a cutpoint that correspond to the most significant relation with outcome (here for survival data).

Video

Click watch operations on bilibili

Global Setting Page

This page is used to display widgets to control global settings of the Shiny tool.

Currently, only the mirror setting is supported.

Feature: Genomic Signature

Overview

Inspired by UCSC Xena browser, feature “genomic signature” is also supported in this tool. For most of analysis modules, this feature is deployed and work well.

To use this feature, you just type a formula instead of a molecule identifier.

For example, if you want to see TCGA mRNA expression distribution of a signature generated by TP53 + 1.5 * KRAS. Firstly, delete the default symbol in the input bar “Input a gene or formula (as signature)”, then input TP53 + 1.5 * KRAS, click the shown “Add TP53 + 1.5 * KRAS”, run analysis as usual. When you use this feature, you must input a space in the formula, so we can understand that you are inputting a signature instead of a normal symbol like gene name.

NOTE: the signature defined in two CCLE drug related analysis modules is different from others shown above. In the CCLE drug related analysis modules, you just select multiple genes, a signature is constructed from geometrical mean of mRNA expression of your input gene list.

Video

Click watch operations on bilibili

Use UCSCXenaShinyV1 R package

For most data query and analysis, we implemented them as R functions available in our R package {UCSCXenaShinyV1}. To use the data and functions provided by us, please check the reference list.

Abbreviations

TCGA cancer types

Study Abbreviation	Study Name
LAML	Acute Myeloid Leukemia
ACC	Adrenocortical carcinoma
BLCA	Bladder Urothelial Carcinoma
LGG	Brain Lower Grade Glioma
BRCA	Breast invasive carcinoma
CESC	Cervical squamous cell carcinoma and endocervical adenocarcinoma
CHOL	Cholangiocarcinoma
LCML	Chronic Myelogenous Leukemia
COAD	Colon adenocarcinoma
CNTL	Controls
ESCA	Esophageal carcinoma
FPPP	FFPE Pilot Phase II
GBM	Glioblastoma multiforme
HNSC	Head and Neck squamous cell carcinoma
KICH	Kidney Chromophobe
KIRC	Kidney renal clear cell carcinoma
KIRP	Kidney renal papillary cell carcinoma
LIHC	Liver hepatocellular carcinoma
LUAD	Lung adenocarcinoma
LUSC	Lung squamous cell carcinoma
DLBC	Lymphoid Neoplasm Diffuse Large B-cell Lymphoma
MESO	Mesothelioma
MISC	Miscellaneous
OV	Ovarian serous cystadenocarcinoma
PAAD	Pancreatic adenocarcinoma
PCPG	Pheochromocytoma and Paraganglioma
PRAD	Prostate adenocarcinoma
READ	Rectum adenocarcinoma
SARC	Sarcoma
SKCM	Skin Cutaneous Melanoma
STAD	Stomach adenocarcinoma
TGCT	Testicular Germ Cell Tumors
THYM	Thymoma
THCA	Thyroid carcinoma
UCS	Uterine Carcinosarcoma
UCEC	Uterine Corpus Endometrial Carcinoma
UVM	Uveal Melanoma

TCGA sample type codes

Code	Definition	Short Letter Code
01	Primary Solid Tumor	TP
02	Recurrent Solid Tumor	TR
03	Primary Blood Derived Cancer - Peripheral Blood	TB
04	Recurrent Blood Derived Cancer - Bone Marrow	TRBM
05	Additional - New Primary	TAP
06	Metastatic	TM
07	Additional Metastatic	TAM
08	Human Tumor Original Cells	THOC
09	Primary Blood Derived Cancer - Bone Marrow	TBM
10	Blood Derived Normal	NB
11	Solid Tissue Normal	NT
12	Buccal Cell Normal	NBC
13	EBV Immortalized Normal	NEBV
14	Bone Marrow Normal	NBM
15	sample type 15	15SH
16	sample type 16	16SH
20	Control Analyte	CELLC
40	Recurrent Blood Derived Cancer - Peripheral Blood	TRB
50	Cell Lines	CELL
60	Primary Xenograft Tissue	XP
61	Cell Line Derived Xenograft Tissue	XCL
99	sample type 99	99SH

TCGA platform codes

Platform Code	Platform Alias	Platform Name
HT_HG-U133A	HT_HG-U133A	Affymetrix HT Human Genome U133 Array Plate Set
HuEx-1_0-st-v2	HuEx-1_0-st-v2	Affymetrix Human Exon 1.0 ST Array
Genome_Wide_SNP_6	Genome_Wide_SNP_6	Affymetrix Genome-Wide Human SNP Array 6.0
HG-CGH-415K_G4124A	HG-CGH-415K_G4124A	Agilent Human Genome CGH Custom Microarray 2x415K
WHG-CGH_4x44B	WHG-CGH_4x44B	Agilent Human Genome CGH Microarray 44K
HG-CGH-244A	HG-CGH-244A	Agilent Human Genome CGH Microarray 244A
WHG-1x44K_G4112A	1 x 44K	Agilent Whole Human Genome
WHG-4x44K_G4112F	4 x 44K	Agilent Whole Human Genome Microarray Kit
AgilentG4502A_07_1	AgilentG4502A_07	Agilent 244K Custom Gene Expression G4502A-07-1
H-miRNA_G4470A	H-miRNA_G4470A	Agilent Human miRNA Microarray
AgilentG4502A_07_2	AgilentG4502A_07	Agilent 244K Custom Gene Expression G4502A-07-2
H-miRNA_8x15Kv2	H-miRNA_8x15K	Agilent Human miRNA Microarray Rel12.0
AgilentG4502A_07_3	AgilentG4502A_07	Agilent 244K Custom Gene Expression G4502A-07-3
H-miRNA_8x15K	H-miRNA_8x15K	Agilent 8 x 15K Human miRNA-specific microarray
CGH-1x1M_G4447A	CGH-1x1M_G4447A	Agilent SurePrint G3 Human CGH Microarray Kit 1x1M
H-miRNA_EarlyAccess	H-miRNA_EarlyAccess	Agilent Human miRNA Early Access Array
IlluminaGG	IlluminaGG	Illumina GoldenGate
HumanMethylation27	HumanMethylation27	Illumina Infinium Human DNA Methylation 27
IlluminaDNAMethylation_OMA003_CPI	IlluminaDNAMethylation	Illumina DNA Methylation OMA003 Cancer Panel I
Human1MDuo	Human1MDuo	Illumina Human1M-Duo BeadChip
IlluminaDNAMethylation_OMA002_CPI	IlluminaDNAMethylation	Illumina DNA Methylation OMA002 Cancer Panel I
HumanHap550	HumanHap550	Illumina 550K Infinium HumanHap550 SNP Chip
AgilentG4502A_07	AgilentG4502A_07	Agilent 244K Custom Gene Expression G4502A-07
bio	bio	Biospecimen Metadata - Complete Set
biotab	biotab	Biospecimen Metadata - Complete Set - All Samples - Tab-delimited
minbio	minbio	Biospecimen Metadata - Minimal Set
minbiotab	minbiotab	Biospecimen Metadata - Minimal Set - All Samples - Tab-delimited
ABI	ABI	Applied Biosystems Sequence data
IlluminaHiSeq_DNASeq	Mutation Calling	Illumina HiSeq 2000 DNA Sequencing
SOLiD_DNASeq	Mutation Calling	ABI SOLiD DNA Sequencing
IlluminaGA_DNASeq	Mutation Calling	Illumina Genome Analyzer DNA Sequencing
IlluminaGA_mRNA_DGE	IlluminaGA_mRNA_DGE	Illumina Genome Analyzer mRNA Digital Gene Expression
454	454	454 Life Sciences Genome Sequence data
HG-U133A_2	HG-U133A_2	Affymetrix Human Genome U133A 2.0 Array
HG-U133_Plus_2	HG-U133_Plus_2	Affymetrix Human Genome U133 Plus 2.0 Array
Mapping250K_Nsp	Mapping250K_Nsp	Affymetrix Human Mapping 250K Nsp Array
Mapping250K_Sty	Mapping250K_Sty	Affymetrix Human Mapping 250K Sty Array
GenomeWideSNP_5	GenomeWideSNP_5	Affymetrix Genome-Wide Human SNP Array 5.0
tissue_images	tissue_images	Tissue Images
IlluminaGA_RNASeq	IlluminaGA_RNASeq	Illumina Genome Analyzer RNA Sequencing
IlluminaGA_miRNASeq	IlluminaGA_miRNASeq	Illumina Genome Analyzer miRNA Sequencing
diagnostic_images	diagnostic_images	Diagnostic Images
pathology_reports	pathology_reports	Pathology Reports
MDA_RPPA_Core	MDA_RPPA_Core	M.D. Anderson Reverse Phase Protein Array Core
microsat_i	microsat_i	Microsatellite Instability Analysis
HumanMethylation450	HumanMethylation450	Illumina Infinium Human DNA Methylation 450
IlluminaHiSeq_mRNA_DGE	IlluminaHiSeq_mRNA_DGE	Illumina HiSeq 2000 mRNA Digital Gene Expression
IlluminaHiSeq_miRNASeq	IlluminaHiSeq_miRNASeq	Illumina HiSeq 2000 miRNA Sequencing
IlluminaHiSeq_RNASeq	IlluminaHiSeq_RNASeq	Illumina HiSeq 2000 RNA Sequencing
IlluminaHiSeq_DNASeqC	IlluminaHiSeq_DNASeqC	Illumina HiSeq for Copy Number Variation
fh_analyses	fh_analyses	Firehose Analyses
fh_stddata	fh_stddata	Firehose Standardized Data
fh_reports	fh_reports	Firehose Reports
IlluminaGA_RNASeqV2	IlluminaGA_RNASeqV2	Illumina Genome Analyzer RNA Sequencing Version 2 analysis
IlluminaHiSeq_RNASeqV2	IlluminaHiSeq_RNASeqV2	Illumina HiSeq 2000 RNA Sequencing Version 2 analysis
IlluminaHiSeq_DNASeq_Cont	Mutation Calling	Illumina HiSeq 2000 DNA Sequencing - Controlled
IlluminaGA_DNASeq_Cont	Mutation Calling	Illumina Genome Analyzer DNA Sequencing - Controlled
IlluminaHiSeq_TotalRNASeqV2	IlluminaHiSeq_TotalRNASeqV2	Illumina HiSeq 2000 Total RNA Sequencing Version 2 analysis
SOLiD_DNASeq_Cont	Mutation Calling	ABI SOLiD DNA Sequencing - Controlled
Mixed_DNASeq	Mutation Calling	Mixed DNA Sequencing
Mixed_DNASeq_Cont	Mutation Calling	Mixed DNA Sequencing - Controlled
IlluminaHiSeq_WGBS	IlluminaHiSeq_WGBS	Illumina HiSeq 2000 Bisulfite-converted DNA Sequencing
IlluminaHiSeq_DNASeq_automated	Automated Mutation Calling	IlluminaHiSeq automated DNA sequencing
IlluminaHiSeq_DNASeq_curated	Curated Mutation Calling	IlluminaHiSeq curated DNA sequencing
IlluminaGA_DNASeq_automated	Automated Mutation Calling	IlluminaGA automated DNA sequencing
IlluminaGA_DNASeq_curated	Curated Mutation Calling	IlluminaGA curated DNA sequencing
SOLiD_DNASeq_automated	Automated Mutation Calling	SOLiD automated DNA sequencing
SOLiD_DNASeq_curated	Curated Mutation Calling	SOLiD curated DNA sequencing
Mixed_DNASeq_automated	Automated Mutation Calling	Mixed automated DNA sequencing
Mixed_DNASeq_curated	Curated Mutation Calling	Mixed curated DNA sequencing
IlluminaHiSeq_DNASeq_Cont_automated	Automated Mutation Calling	IlluminaHiSeq automated DNA sequencing - controlled
IlluminaHiSeq_DNASeq_Cont_curated	Curated Mutation Calling	IlluminaHiSeq curated DNA sequencing - controlled
IlluminaGA_DNASeq_Cont_automated	Automated Mutation Calling	IlluminaGA automated DNA sequencing - controlled
IlluminaGA_DNASeq_Cont_curated	Curated Mutation Calling	IlluminaGA curated DNA sequencing - controlled
SOLiD_DNASeq_Cont_automated	Automated Mutation Calling	SOLiD automated DNA sequencing - controlled
SOLiD_DNASeq_Cont_curated	Curated Mutation Calling	SOLiD curated DNA sequencing - controlled
Mixed_DNASeq_Cont_automated	Automated Mutation Calling	Mixed automated DNA sequencing - controlled
Mixed_DNASeq_Cont_curated	Curated Mutation Calling	Mixed curated DNA sequencing - controlled
supplemental_clinical	supplemental_clinical	supplemental_clinical
Multicenter_mutation_calling_MC3	Multicenter Mutation Calling (MC3)	Multi-Center Mutations
Multicenter_mutation_calling_MC3_Cont	Multicenter Mutation Calling (MC3)	Multi-Center Mutations - Controlled

All TCGA code tables are available at https://gdc.cancer.gov/resources-tcga-users/tcga-code-tables/

Citation

Please cite the following article when you used UCSCXenaShinyV1 in your study:

Shixiang Wang^#, Yi Xiong^#, Longfei Zhao^#, Kai Gu^#, Yin Li, Fei Zhao, Jianfeng Li, Mingjie Wang, Haitao Wang, Ziyu Tao, Tao Wu, Yichao Zheng, Xuejun Li, Xue-Song Liu, UCSCXenaShinyV1: An R/CRAN Package for Interactive Analysis of UCSC Xena Data, Bioinformatics, 2021;, btab561, https://doi.org/10.1093/bioinformatics/btab561.

Bibtex format:

@article{10.1093/bioinformatics/btab561,
    author = {Wang, Shixiang and Xiong, Yi and Zhao, Longfei and Gu, Kai and Li, Yin and Zhao, Fei and Li, Jianfeng and Wang, Mingjie and Wang, Haitao and Tao, Ziyu and Wu, Tao and Zheng, Yichao and Li, Xuejun and Liu, Xue-Song},
    title = "{UCSCXenaShinyV1: An R/CRAN Package for Interactive Analysis of UCSC Xena Data}",
    journal = {Bioinformatics},
    year = {2021},
    month = {07},
    abstract = "{UCSC Xena platform provides huge amounts of processed cancer omics data from large cancer research projects (e.g. TCGA, CCLE and PCAWG) or individual research groups and enables unprecedented research opportunities. However, a graphical user interface (GUI) based tool for interactively analyzing UCSC Xena data and generating elegant plots is still lacking, especially for cancer researchers and clinicians with limited programming experience. Here, we present UCSCXenaShinyV1, an R Shiny package for quickly searching, downloading, exploring, analyzing and visualizing data from UCSC Xena data hubs. This tool could effectively promote the practical use of public data, and can serve as an important complement to the current Xena genomics explorer.UCSCXenaShinyV1 is an open source R package under GPLv3 license and it is freely available at https://github.com/openbiox/UCSCXenaShinyV1 or https://cran.r-project.org/package=UCSCXenaShinyV1. The docker image is available at https://hub.docker.com/r/shixiangwang/UCSCXenaShinyV1.Supplementary data are available at Bioinformatics online.}",
    issn = {1367-4803},
    doi = {10.1093/bioinformatics/btab561},
    url = {https://doi.org/10.1093/bioinformatics/btab561},
    note = {btab561},
    eprint = {https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btab561/39456169/btab561.pdf},
}

When you use UCSC Xena data, you should also cite it with:

Goldman, M. J., Craft, B., Hastie, M., Repečka, K., McDade, F., Kamath, A., … & Haussler, D. (2020). Visualizing and interpreting cancer genomics data via the Xena platform. Nature biotechnology, 38(6), 675-678.

Bibtex format:

@article{goldman2020visualizing,
  title={Visualizing and interpreting cancer genomics data via the Xena platform},
  author={Goldman, Mary J and Craft, Brian and Hastie, Mim and Repe{\v{c}}ka, Kristupas and McDade, Fran and Kamath, Akhil and Banerjee, Ayan and Luo, Yunhai and Rogers, Dave and Brooks, Angela N and others},
  journal={Nature biotechnology},
  volume={38},
  number={6},
  pages={675--678},
  year={2020},
  publisher={Nature Publishing Group}
}

Contact

Bug report or feature request

If you find any bug or have any feature to request, please report it at GitHub https://github.com/openbiox/UCSCXenaShinyV1/issues or email to me mailto:[email protected].

If you love our work, please give use a star at https://github.com/openbiox/UCSCXenaShinyV1.

Discussion

To discuss ideas or suggestions, you can contact any of us via information shown in “Developers” page.

Extra Datasets

Extra datasets cleaned from UCSCXena hubs or references are deposited in Zenodo repo.

You can download them directly from the Zenodo repo or using get_data() function provided in UCSCXenaShinyV1 R package.

If you want to know the dataset source, you can check the “data_source” attribute.

e.g.,

> str(pancan_MSI)
tibble [11,139 x 3] (S3: tbl_df/tbl/data.frame)
 $ case_id     : chr [1:11139] "10.1038_ng.2273_B085" "10.1038_ng.2273_B099" "10.1038_ng.2273_R104" "10.1038_ng.2273_T26" ...
 $ cancer_type : chr [1:11139] "CHOL_10.1038_ng.2273" "CHOL_10.1038_ng.2273" "CHOL_10.1038_ng.2273" "CHOL_10.1038_ng.2273" ...
 $ MANTIS_Score: num [1:11139] 0.285 0.257 0.275 0.292 0.281 ...
 - attr(*, "data_source")= chr "DOI:https://doi.org/10.1200/PO.17.00073"

Shixiang Wang

Leader

See Profile

Fei Zhao

Counselor

See Profile

Yi Xiong

Key developer

See Profile

Longfei Zhao

Key developer

See Profile

Yin Li

Developer

See Profile

Kai Gu

Key developer

See Profile

Data Portal Summary

DATA HUBS

11

COHORTS

160

DATASETS

1677

SAMPLES

~2,000,000

PRIMARY SITES

~37

DATA SUBTYPES

~45

Selected Data:

Wait until a table shows...

Upload your data file

Note: Not both files are required. Prepare your upload files based on your analysis plan. Besides, make sure upload files have correct file extensions.

Analysis Controls

Sample Filters

Analysis Controls

Sample Filters

Analysis Controls

Sample Filters

Analysis Controls

Sample Filters

NOTEs:

NOTEs:

NOTEs:

NOTEs:

NOTEs:

NOTEs:

NOTEs:

NOTEs:

NOTEs:

1. Not all dataset have clinical/pathological stages, so, in this case, the stage option is disabled.

2. The default option <Auto> will return the best p value, if you do not want to do so please choose <Custom>.

Method:

Method:

NOTEs:

NOTEs:

NOTEs:

NOTEs:

The default option <Auto> will return the best p value, if you do not want to do so please choose <Custom>.

Global setting:

Usage

Home Page

Overview

Quick search

Video

Repository Page

Overview

Dataset filter

Dataset selection

Dataset check

Dataset download

Video

General Analysis Page

Overview

Video

Quick PanCan Analysis Page

Overview

Notes

Video

Global Setting Page

Feature: Genomic Signature

Overview

Video

Use UCSCXenaShinyV1 R package

Abbreviations

TCGA cancer types

TCGA sample type codes

TCGA platform codes

Citation

Contact

Bug report or feature request

Discussion

Extra Datasets

Shixiang Wang

Fei Zhao

Yi Xiong