Welcome to UCSCXenaShiny v2!
Thank you for using UCSCXenaShiny v2.2.0 based on UCSCXenaTools v1.4.8. Our web tool aims to povide a user-friendly platform to explore UCSC Xena datasets for both general and personalized cancer molecular research. If you have any questions during use, please do not hesitate to contact us via Github issue. If the tool has faciliated your research, welcome to cite our work. :)
Daily Gene
Pan-Cancer Query
2.Run two explorations:
※ Shiny Page Gallery
※ Example TCGA Modules: Quick analysis with easy steps
Module: Tumor and Normal Comparison
Compare one multi-omics molecular value between tumor and normal (including GTEx) samples.
Go >>>
Module: Molecule-Molecule Correlation
Calculate the correlation of two multi-omics molecules acccording to their values in tumor samples.
Go >>>
Module: Kaplan-Meier Survival Analysis
Perform the log-rank test analysis of one multi-omics molecule in tumor samples of one cancer type.
Go >>>
※ Example TCGA pipelines: In-depth analysis with personalized steps
Pipelines: Comparison Analysis
Compare one of integrated identifiers (e.g. omis, non-omics, user-defined) based on two customizable groups of samples.
Go >>>
Pipelines: Correlation Analysis
Calculate the correlation between any two of integrated identifiers (e.g. omis, non-omics, user-defined) among custom samples
Go >>>
Pipelines: Survival Analysis
Perform log-rank test analysis for one of integrated identifiers based on two customizable groups of samples.
Go >>>
※ Latest significant release notes
- 2024-06-25: Design gene and pathway cross-omics analysis
- 2024-02-14: Incorporate the PharmacoGenomics modules
- 2024-01-21: Adjust homepge with slick gallery to show basic page help.
- 2024-01-16: Introduce MSigDB genesets for molecule batch analysis.
- 2023-12-20: Add download modules that support data requisition.
- See more update logs in our Github .
TPC modules
Here, we introduced multiple custom modules for quick TPC molecule exploration with few steps. In general, 10 panels for common analytical scenario are available, as follows:
1. Box Layout
- The dashboard-based box layouts are adopted for simple and intuitive representation. The left box is usually for data selection and analytical parameters adjustment. The right box is usually for display of result plot and one sidebar panel can be pulled to adjust visualization options and download results.
2. Molecular database
We designate one specific UCSC Xena dataset for each molecular type of TPC databases. You can check the detailed information through the links below.
2.1 TCGA
- mRNA Expression —— TcgaTargetGtex_rsem_gene_tpm
- Transcript Expression —— TcgaTargetGtex_rsem_isoform_tpm
- DNA Methylation —— GDC-PANCAN.methylation450.tsv
- miRNA Expression —— pancanMiRs_EBadjOnProtocolPlatformWithoutRepsWithUnCorrectMiRs_08_04_16.xena
- Protein Expression —— TCGA-RPPA-pancan-clean.xena
- Copy Number Variation —— Gistic2_CopyNumber_Gistic2_all_data_by_genes
- Mutation status —— mc3.v0.2.8.PUBLIC.nonsilentGene.xena
PCAWG
- mRNA Expression —— tophat_star_fpkm_uq.v2_aliquot_gl.sp.log
- miRNA Expression —— x3t2m1.mature.TMM.mirna.matrix.log
- Promoter Activity —— rawPromoterActivity.sp
- Gene Fusion —— pcawg3_fusions_PKU_EBI.gene_centric.sp.xena
- APOBEC mutagenesis —— MAF_Aug31_2016_sorted_A3A_A3B_comparePlus.sp
CCLE
- mRNA Expression —— ccle/CCLE_DepMap_18Q2_RNAseq_RPKM_20180502
- Protein Expression —— ccle/CCLE_RPPA_20180123
- Copy Number Variation —— ccle/CCLE_copynumber_byGene_2013-12-03
Tips: The menu of
TPC Pipelinssupports more general and personalized TPC molecular analysis, including alternative datasets, precise data preparation and versatile modes.
Quick TCGA Analysis: Compare between tumor and normal
1. Select omics type
2. Select omics molecule
3. Select analysis mode
4. Include GTEx normal samples
Analytical results:
-
1. Visualization parameters
(1) Geometry type:
(2) Label type:
(3) Colors:
(4) ggplot theme:
2. Download options
(1) Figure:
(2) Data table:
Quick TCGA Analysis: Compare between tumor and normal
1. Select omics type
2. Select omics molecule
Analytical results:
-
1. Visualization parameters
(1) Gender:
2. Download options
(1) Figure:
(2) Data table:
Quick TCGA Analysis: Compare between mutation and wild tumor samples
1. Select omics type
2. Select omics molecule
3. Group by gene mutation
4. Select analysis mode
Analytical results:
-
1. Visualization parameters
(1) Geometry type:
(2) Label type:
(3) Colors:
2. Download options
(1) Figure:
(2) Data table:
Quick TCGA Analysis: Correlation in tumor samples
X-axis:
1. Select omics type
2. Select omics molecule
Y-axis:
1. Select omics type
2. Select omics molecule
3. Select TCGA cancer(s)
4. Select correlation method
5. Adjust tumor purity
Analytical results:
-
1. Visualization parameters
(1) Regression line:
(2) Point transparent:
(3) Point color:
(4) ggplot theme:
2. Download options
(1) Figure:
(2) Data table:
Quick TCGA Analysis: Correlation in tumor samples
1. Select omics type
2. Select omics molecule
3. Select TIL cell types
4. Select correlation method
Analytical results:
-
1. Visualization parameters
(1) Colors:
2. Download options
(1) Figure:
(2) Data table:
Quick TCGA Analysis: Correlation in tumor samples
1. Select omics type
2. Select omics molecule
3. Select TIL cell types
4. Select correlation method
Analytical results:
-
1. Visualization parameters
(1) Colors:
2. Download options
(1) Figure:
(2) Data table:
Quick TCGA Analysis: Correlation in tumor samples
1. Select omics type
2. Select omics molecule
3. Select fearture type
4. Select correlation method
Analytical results:
-
1. Visualization parameters
(1) Line color:
2. Download options
(1) Figure:
(2) Data table:
Quick TCGA Analysis: Correlation in tumor samples
1. Select omics type
2. Select omics molecule
3. Select one pathway
3. Select TCGA cancer(s)
4. Select correlation method
Analytical results:
-
1. Visualization parameters
(1) Regression line:
(2) Point transparent:
(3) Point color:
(4) ggplot theme:
2. Download options
(1) Figure:
(2) Data table:
Quick TCGA Analysis: Kaplan-Meier survival analysis(Log-rank)
1. Select omics type
2. Select omics molecule
3. Select TCGA cancer type and endpoint type
4. Filter by clinical features
5. Grouping by
Analytical results:
-
1. Visualization parameters
(1) Color palette:
2. Download options
(1) Figure:
(2) Data table:
Quick TCGA Analysis: Univariate Cox regression survival analysis
1. Select omics type
2. Select omics molecule
3. Select TCGA endpoint type
Analytical results:
-
1. Visualization parameters
(1) Color palette:
2. Download options
(1) Figure:
(2) Data table:
Analytical results:
-
1. Visualization parameters
(1) Marginal plot:
(2) Color palette:
2. Download options
(1) Figure:
(2) Data table:
Quick PCAWG Analysis: Compare between tumor and normal
1. Select omics type
2. Select omics molecule
Analytical results:
-
1. Visualization parameters
(1) Geometry type:
(2) Label type:
(3) Colors:
(4) ggplot theme:
2. Download options
(1) Figure:
(2) Data table:
Quick PCAWG Analysis: Correlation in tumor samples
X-axis:
1. Select omics type
2. Select omics molecule
Y-axis:
1. Select omics type
2. Select omics molecule
3. Select PCAWG project(s)
4. Select correlation method
5. Adjust tumor purity
Analytical results:
-
1. Visualization parameters
(1) Regression line:
(2) Point transparent:
(3) Point color:
(4) ggplot theme:
2. Download options
(1) Figure:
(2) Data table:
Quick PCAWG Analysis: Kaplan-Meier survival analysis(Log-rank)
1. Select omics type
2. Select omics molecule
3. Select PCAWG project (Only OS event)
4. Filter by clinical features
5. Grouping by
Analytical results:
-
1. Visualization parameters
(1) Color palette:
2. Download options
(1) Figure:
(2) Data table:
Quick PCAWG Analysis: Univariate Cox regression survival analysis
1. Select omics type
2. Select omics molecule
Analytical results:
-
1. Visualization parameters
(1) Color palette:
2. Download options
(1) Figure:
(2) Data table:
Quick CCLE Analysis: Compare across primary sites
1. Select omics type
2. Select omics molecule
Analytical results:
-
1. Visualization parameters
2. Download options
(1) Figure:
(2) Data table:
Quick CCLE Analysis: Correlation in cancer cell lines
X-axis:
1. Select omics type
2. Select omics molecule
Y-axis:
1. Select omics type
2. Select omics molecule
3. Select primary site(s)
4. Select correlation method
Analytical results:
-
1. Visualization parameters
(1) Regression line:
(2) Point transparent:
(3) Point color:
(4) ggplot theme:
2. Download options
(1) Figure:
(2) Data table:
Quick Analysis: Compare between tumor and normal
1. Select omics type
2. Select one gene list
3. Select X-axis type
Analytical results:
-
1. Visualization parameters
2. Download options
(1) Figure:
(2) Data table:
Quick Analysis: Compare between tumor and normal
1. Select omics type
2. Select one gene list
3. Select primary site(s)
Analytical results:
-
1. Visualization parameters
(1) Show P value:
(2) Colors:
(3) Point transparent:
2. Download options
(1) Figure:
(2) Data table:
TPC pipelines
Here, we introduced a series of personalized TPC pipelines for comprehensive and precise TPC molecule exploration with various operations. In general, 9 panels for common analytical scenario are available, as follows:
Generally, each analytical pipeline comprises of three main subpanels (S1, S2, S3).
- In S1 subpanel, you can prepare and select the expected data and sample ranges;
- In S2 subpanel, you can fetch the specific tumor data for downstream analysis;
- In S3 subpanel, you can execute the analysis, visualize and download results.
Tips: The menu of
TPC modulesenable specific and quick TPC molecular analysis, such as tumor and tomor comparison, molecule and molecule correlation.
TCGA Correlation Analysis
S1: Preset
S1.1 Modify datasets [opt]
No alternative datasets can be chosen.
No alternative datasets can be chosen.
No alternative datasets can be chosen.
No alternative datasets can be chosen.
S1.2 Choose cancer
S1.3 Filter samples [opt]
Quick filter:
Exact filter:
S1.4 Upload metadata [opt]
S1.5 Add signature [opt]
S2: Get data
S2.1 Get data for X-axis
S2.2 Get data for Y-axis
S3: Analyze & Visualize
S3.1 Set analysis parameters
S3.2 Set visualization parameters
S3.3 Download results
S1: Preset
S1.1 Modify datasets [opt]
No alternative datasets can be chosen.
No alternative datasets can be chosen.
No alternative datasets can be chosen.
No alternative datasets can be chosen.
S1.2 Choose cancers
S1.3 Filter samples [opt]
Quick filter:
Exact filter:
S1.4 Upload metadata [opt]
S1.5 Add signature [opt]
S2: Get data
S2.1 Get data for X-axis
S2.2 Get data for Y-axis
S3: Analyze & Visualize
S3.1 Set analysis parameters
S3.2 Set visualization parameters
S3.3 Download results
S1: Preset
S1.1 Modify datasets [opt]
No alternative datasets can be chosen.
No alternative datasets can be chosen.
No alternative datasets can be chosen.
No alternative datasets can be chosen.
S1.2 Choose cancer
S1.3 Filter samples [opt]
Quick filter:
Exact filter:
S1.4 Upload metadata [opt]
S1.5 Add signature [opt]
S3: Analyze
S3.1 Set analysis parameters
S3.2 Download results
TCGA Comparison Analysis
S1: Preset
S1.1 Modify datasets [opt]
No alternative datasets can be chosen.
No alternative datasets can be chosen.
No alternative datasets can be chosen.
No alternative datasets can be chosen.
S1.2 Choose cancer
S1.3 Filter samples [opt]
Quick filter:
Exact filter:
S1.4 Upload metadata [opt]
S1.5 Add signature [opt]
S2: Get data
S2.1 Divide 2 groups by one condition
S2.2 Get data for comparison
S3: Analyze & Visualize
S3.1 Set analysis parameters
S3.2 Set visualization parameters
S3.3 Download results
S1: Preset
S1.1 Modify datasets [opt]
No alternative datasets can be chosen.
No alternative datasets can be chosen.
No alternative datasets can be chosen.
No alternative datasets can be chosen.
S1.2 Choose cancers
S1.3 Filter samples [opt]
Quick filter:
Exact filter:
S1.4 Upload metadata [opt]
S1.5 Add signature [opt]
S2: Get data
S2.1 Divide 2 groups by one condition
S2.2 Get data for comparison
S3: Analyze & Visualize
S3.1 Set analysis parameters
S3.2 Set visualization parameters
S3.3 Download results
S1: Preset
S1.1 Modify datasets [opt]
No alternative datasets can be chosen.
No alternative datasets can be chosen.
No alternative datasets can be chosen.
No alternative datasets can be chosen.
S1.2 Choose cancer
S1.3 Filter samples [opt]
Quick filter:
Exact filter:
S1.4 Upload metadata [opt]
S1.5 Add signature [opt]
S3: Analyze
S3.1 Set analysis parameters
S3.2 Download results
TCGA Survival Analysis
S1: Preset
S1.1 Modify datasets [opt]
No alternative datasets can be chosen.
No alternative datasets can be chosen.
No alternative datasets can be chosen.
No alternative datasets can be chosen.
S1.2 Choose cancer
S1.3 Filter samples [opt]
Quick filter:
Exact filter:
S1.4 Upload metadata [opt]
S1.5 Add signature [opt]
S2: Get data
S2.1 Select survival endpoint
S2.2 Divide 2 groups by one condition
S3: Analyze & Visualize
S3.1 Set analysis parameters
S3.2 Set visualization parameters
S3.3 Download results
S1: Preset
S1.1 Modify datasets [opt]
No alternative datasets can be chosen.
No alternative datasets can be chosen.
No alternative datasets can be chosen.
No alternative datasets can be chosen.
S1.2 Choose cancers
S1.3 Filter samples [opt]
Quick filter:
Exact filter:
S1.4 Upload metadata [opt]
S1.5 Add signature [opt]
S2: Get data
S2.1 Select survival endpoint
S2.2 Divide 2 groups by one condition
S3: Analyze & Visualize
S3.1 Set analysis parameters
S3.2 Set visualization parameters
S3.3 Download results
S1: Preset
S1.1 Modify datasets [opt]
No alternative datasets can be chosen.
No alternative datasets can be chosen.
No alternative datasets can be chosen.
No alternative datasets can be chosen.
S1.2 Choose cancer
S1.3 Filter samples [opt]
Quick filter:
Exact filter:
S1.4 Upload metadata [opt]
S1.5 Add signature [opt]
S3: Analyze
S3.1 Set analysis parameters
S3.2 Download results
TCGA Cross-Omics Analysis
S1: Preset
S1.1 Modify datasets [Only for S1.3]
No alternative datasets can be chosen.
No alternative datasets can be chosen.
No alternative datasets can be chosen.
No alternative datasets can be chosen.
S1.2 Choose cancers
S1.3 Filter samples [opt]
Quick filter:
Exact filter:
S2: Get data
S2.1 Select one gene
S2.2 Load mRNA/Mutation/CNV data
S2.3 Load transcript data
S2.4 Load methylation (450K) data
S1: Preset
S1.1 Modify datasets [Only for S1.3]
No alternative datasets can be chosen.
No alternative datasets can be chosen.
No alternative datasets can be chosen.
No alternative datasets can be chosen.
S1.2 Choose cancers
S1.3 Filter samples [opt]
Quick filter:
Exact filter:
S2: Get data
S2.1 Select one pathway
PCAWG Correlation Analysis
S1: Preset
S1.1 Modify datasets [opt]
No alternative datasets can be chosen.
No alternative datasets can be chosen.
No alternative datasets can be chosen.
No alternative datasets can be chosen.
S1.2 Choose project
S1.3 Filter samples [opt]
Quick filter:
Exact filter:
S1.4 Upload metadata [opt]
S1.5 Add signature [opt]
S2: Get data
S2.1 Get data for X-axis
S2.2 Get data for Y-axis
S3: Analyze & Visualize
S3.1 Set analysis parameters
S3.2 Set visualization parameters
S3.3 Download results
S1: Preset
S1.1 Modify datasets [opt]
No alternative datasets can be chosen.
No alternative datasets can be chosen.
No alternative datasets can be chosen.
No alternative datasets can be chosen.
S1.2 Choose projects
S1.3 Filter samples [opt]
Quick filter:
Exact filter:
S1.4 Upload metadata [opt]
S1.5 Add signature [opt]
S2: Get data
S2.1 Get data for X-axis
S2.2 Get data for Y-axis
S3: Analyze & Visualize
S3.1 Set analysis parameters
S3.2 Set visualization parameters
S3.3 Download results
S1: Preset
S1.1 Modify datasets [opt]
No alternative datasets can be chosen.
No alternative datasets can be chosen.
No alternative datasets can be chosen.
No alternative datasets can be chosen.
S1.2 Choose project
S1.3 Filter samples [opt]
Quick filter:
Exact filter:
S1.4 Upload metadata [opt]
S1.5 Add signature [opt]
S3: Analyze
S3.1 Set analysis parameters
S3.2 Download results
PCAWG Comparison Analysis
S1: Preset
S1.1 Modify datasets [opt]
No alternative datasets can be chosen.
No alternative datasets can be chosen.
No alternative datasets can be chosen.
No alternative datasets can be chosen.
S1.2 Choose project
S1.3 Filter samples [opt]
Quick filter:
Exact filter:
S1.4 Upload metadata [opt]
S1.5 Add signature [opt]
S2: Get data
S2.1 Divide 2 groups by one condition
S2.2 Get data for comparison
S3: Analyze & Visualize
S3.1 Set analysis parameters
S3.2 Set visualization parameters
S3.3 Download results
S1: Preset
S1.1 Modify datasets [opt]
No alternative datasets can be chosen.
No alternative datasets can be chosen.
No alternative datasets can be chosen.
No alternative datasets can be chosen.
S1.2 Choose projects
S1.3 Filter samples [opt]
Quick filter:
Exact filter:
S1.4 Upload metadata [opt]
S1.5 Add signature [opt]
S2: Get data
S2.1 Divide 2 groups by one condition
S2.2 Get data for comparison
S3: Analyze & Visualize
S3.1 Set analysis parameters
S3.2 Set visualization parameters
S3.3 Download results
S1: Preset
S1.1 Modify datasets [opt]
No alternative datasets can be chosen.
No alternative datasets can be chosen.
No alternative datasets can be chosen.
No alternative datasets can be chosen.
S1.2 Choose project
S1.3 Filter samples [opt]
Quick filter:
Exact filter:
S1.4 Upload metadata [opt]
S1.5 Add signature [opt]
S3: Analyze
S3.1 Set analysis parameters
S3.2 Download results
PCAWG Survival Analysis(OS)
S1: Preset
S1.1 Modify datasets [opt]
No alternative datasets can be chosen.
No alternative datasets can be chosen.
No alternative datasets can be chosen.
No alternative datasets can be chosen.
S1.2 Choose project
S1.3 Filter samples [opt]
Quick filter:
Exact filter:
S1.4 Upload metadata [opt]
S1.5 Add signature [opt]
S2: Get data
S2.1 Select survival endpoint
Only OS (Overall Survial) is supported.
S2.2 Divide 2 groups by one condition
S3: Analyze & Visualize
S3.1 Set analysis parameters
S3.2 Set visualization parameters
S3.3 Download results
S1: Preset
S1.1 Modify datasets [opt]
No alternative datasets can be chosen.
No alternative datasets can be chosen.
No alternative datasets can be chosen.
No alternative datasets can be chosen.
S1.2 Choose projects
S1.3 Filter samples [opt]
Quick filter:
Exact filter:
S1.4 Upload metadata [opt]
S1.5 Add signature [opt]
S2: Get data
S2.1 Select survival endpoint
Only OS (Overall Survial) is supported.
S2.2 Divide 2 groups by one condition
S3: Analyze & Visualize
S3.1 Set analysis parameters
S3.2 Set visualization parameters
S3.3 Download results
S1: Preset
S1.1 Modify datasets [opt]
No alternative datasets can be chosen.
No alternative datasets can be chosen.
No alternative datasets can be chosen.
No alternative datasets can be chosen.
S1.2 Choose project
S1.3 Filter samples [opt]
Quick filter:
Exact filter:
S1.4 Upload metadata [opt]
S1.5 Add signature [opt]
S2: Get data
S2.1 Select survival endpoint
Only OS (Overall Survial) is supported.
S2.2 Divide 2 groups by batch conditions
S3: Analyze
S3.1 Set analysis parameters
S3.2 Download results
CCLE Correlation Analysis
S1: Preset
S1.1 Modify datasets [opt]
No alternative datasets can be chosen.
No alternative datasets can be chosen.
No alternative datasets can be chosen.
No alternative datasets can be chosen.
S1.2 Choose sites
S1.3 Filter samples [opt]
Exact filter:
S1.4 Upload metadata [opt]
S1.5 Add signature [opt]
S2: Get data
S2.1 Get data for X-axis
S2.2 Get data for Y-axis
S3: Analyze & Visualize
S3.1 Set analysis parameters
S3.2 Set visualization parameters
S3.3 Download results
S1: Preset
S1.1 Modify datasets [opt]
No alternative datasets can be chosen.
No alternative datasets can be chosen.
No alternative datasets can be chosen.
No alternative datasets can be chosen.
S1.2 Choose sites
S1.3 Filter samples [opt]
Exact filter:
S1.4 Upload metadata [opt]
S1.5 Add signature [opt]
S3: Analyze
S3.1 Set analysis parameters
S3.2 Download results
CCLE Comparison Analysis
S1: Preset
S1.1 Modify datasets [opt]
No alternative datasets can be chosen.
No alternative datasets can be chosen.
No alternative datasets can be chosen.
No alternative datasets can be chosen.
S1.2 Choose sites
S1.3 Filter samples [opt]
Exact filter:
S1.4 Upload metadata [opt]
S1.5 Add signature [opt]
S2: Get data
S2.1 Divide 2 groups by one condition
S2.2 Get data for comparison
S3: Analyze & Visualize
S3.1 Set analysis parameters
S3.2 Set visualization parameters
S3.3 Download results
S1: Preset
S1.1 Modify datasets [opt]
No alternative datasets can be chosen.
No alternative datasets can be chosen.
No alternative datasets can be chosen.
No alternative datasets can be chosen.
S1.2 Choose sites
S1.3 Filter samples [opt]
Exact filter:
S1.4 Upload metadata [opt]
S1.5 Add signature [opt]
S3: Analyze
S3.1 Set analysis parameters
S3.2 Download results
Analysis Controls
NOTE: The data table is not available when use ggstatsplot.
Sample Filters
Analysis Controls
Sample Filters
Analysis Controls
Sample Filters
Analysis Controls
Sample Filters
Analysis Controls
Sample Filters
Part1: Download molecular data
1. Select one database
No alternative datasets can be chosen.
No alternative datasets can be chosen.
No alternative datasets can be chosen.
No alternative datasets can be chosen.
No alternative datasets can be chosen.
No alternative datasets can be chosen.
No alternative datasets can be chosen.
No alternative datasets can be chosen.
No alternative datasets can be chosen.
No alternative datasets can be chosen.
No alternative datasets can be chosen.
No alternative datasets can be chosen.
2. Select samples
Quick filter:
Exact filter:
3. Select identifiers
4. Download results
NOTEs:
1. To get the whole dataset, please click 'Respository' page and download derictly from UCSC website.
2. Queried data in long format is for easy display and it is downloaded as the wide format.
Part2: Download other data
1. TCGA database
2. PCAWG database
3. CCLE database
1. Select one data hub
2. Select one dataset
(1) Format type
(2) Profile type
3. Select multiple ids
Loading...
3. Download results
NOTEs:
1. To get the whole dataset, please click 'Respository' page and download derictly from UCSC website.
2. Queried data in long format is for easy display and it is downloaded as the wide format.
.
NOTEs:
Given that there are overlapping cells in different drug and omics datasets, we have utilized the common data to assess correlations, thereby maximizing the utilization of existing information. For instance, the designation 'gdsc_ctrp1' indicates that the omics data is sourced from the GDSC project, while the drug sensitivity data is derived from the CTRP1 project.
The y axis is the drug sensitivity metric.
The x-axis represents the molecular state or numerical data, including the boolean state for gene mutations or mRNA expression.
.
NOTEs:
The y axis is the drug sensitivity metric or molecular data for boxplot.
.
.
NOTEs:
This analysis can examine connections between a target feature and all features within all datasets.
the determination of effect size and p-value varies based on the type of the features being compared:
1. For continuous features against continuous datasets , the Spearman correlation coefficient R, which ranges from 0 to 1, is utilized;
2. When comparing discrete features against continuous datasets , the log2 fold change (events/wildtype) serves as the measure of effect, with p-values obtained from the Wilcoxon test;
3. In the case of discrete features against discrete datasets , the effect size is quantified using the log2 odds ratio, with p-values calculated via the Chi-squared test;
The continuous features comprise Copy Number Data, DNA Methylation, mRNA Expression, Protein Expression, and drug sensitivity. On the other hand, the discrete features encompass Gene Fusion, Gene Mutation, and Gene Site Mutation.
The Frequency table contains two columns: the count column, which records the number of significant pairs, and the proportion column, representing the ratio of significant pairs to total pairs.
You can change the threshold to filter the effect size. The result table indicates a 'yes' in the Significance column when the p-value is below 0.05 and the effect size falls above the threshold defined by the user for the feature pair.
Feel free to talk with me if you find any bugs or have any suggestions. :)
Email: mugpeng@foxmail.com
github: https://github.com/mugpeng
You can visit https://github.com/mugpeng/OmicsPharDB to reach the toturial.
UCSCXenaShiny v2
-
Shensuo Li, Yuzhong Peng, Minjun Chen, Yankun Zhao, Yi Xiong, Jianfeng Li, Peng Luo, Haitao Wang, Fei Zhao, Qi Zhao, Yanru Cui, Sujun Chen, Jian-Guo Zhou, Shixiang Wang, Facilitating integrative and personalized oncology omics analysis with UCSCXenaShiny, Communications Biology, 1200 (2024), https://doi.org/10.1038/s42003-024-06891-2
-
PMID: 39341906
-
Bibtex format:
@ARTICLE{Li2024-yd,
title = "Facilitating integrative and personalized oncology omics analysis with UCSCXenaShiny",
author = "Li, Shensuo and Peng, Yuzhong and Chen, Minjun and Zhao, Yankun and Xiong, Yi and Li, Jianfeng and Luo, Peng and Wang, Haitao and Zhao, Fei and Zhao, Qi and Cui, Yanru and Chen, Sujun and Zhou, Jian-Guo and Wang, Shixiang",
abstract = "The continuous generation of multi-omics and phenotype data is propelling advancements in precision oncology. UCSCXenaShiny was developed as an interactive tool for exploring thousands of cancer datasets available on UCSC Xena. However, its capacity for comprehensive and personalized pan-cancer data analysis is being challenged by the growing demands. Here, we introduce UCSCXenaShiny v2, a milestone update through a variety of improvements. Firstly, by integrating multidimensional data and implementing adaptable sample settings, we create a suite of robust TPC (TCGA, PCAWG, CCLE) analysis pipelines. These pipelines empower users to conduct in-depth analyses of correlation, comparison, and survival in three modes: Individual, Pan-cancer and Batch screen. Additionally, the tool includes download interfaces that enable users to access diverse data and outcomes, several features also facilitate the joint analysis of drug sensitivity and multi-omics of cancer cell lines. UCSCXenaShiny v2 is an open-source R package and a web application, freely accessible at https://github.com/openbiox/UCSCXenaShiny.",
journal = "Commun. Biol.",
volume = 7,
number = 1,
pages = "1200",
month = sep,
year = 2024,
language = "en"
}
UCSCXenaShiny v1
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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, UCSCXenaShiny: An R/CRAN Package for Interactive Analysis of UCSC Xena Data, Bioinformatics, 2021;, btab561, https://doi.org/10.1093/bioinformatics/btab561.
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PubMed ID: 34323947
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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 = "{UCSCXenaShiny: 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 UCSCXenaShiny, 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.UCSCXenaShiny is an open source R package under GPLv3 license and it is freely available at https://github.com/openbiox/UCSCXenaShiny or https://cran.r-project.org/package=UCSCXenaShiny. The docker image is available at https://hub.docker.com/r/shixiangwang/ucscxenashiny.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},
}
UCSC Xena
When you use UCSC Xena data, you should also cite it with:
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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. https://doi:10.1038/s41587-020-0546-8
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PubMed ID: 32444850
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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}
}
Shixiang Wang
Leader
Shensuo Li
Developer of XenaShiny v2
Yuzhong Peng
Developer of XenaShiny v2 (PharmacoGenomics)
Fei Zhao
Counselor
Yi Xiong
Key developer
Longfei Zhao
Key developer
Yin Li
Developer
Kai Gu
Key developer