Getting Started With OncoLand
From Array Suite Wiki
Getting Started With OncoLand
OncoLand is OmicSoft's database and interface to tens of thousands of carefully processed and curated oncology -Omic data. The following videos will give an overview of some of the many functions built into OncoLand to enable scientific discovery. If you have trouble viewing the videos, the full playlist may be found here. Written tutorials for OmicSoft products may be found here.
OncoLand Video Tutorials
A first look at OncoLand
OncoLand contains data from tens of thousands of curated samples related to cancer, from multiple large-scale studies of cancer -Omics. OncoLand is an on-going project, with quarterly updates. The user can first check number and distribution of samples/comparisons in the current OncoLand release.
- Datasets available in OncoLand [00:21]
- Viewing OncoLand Release Whitepaper [00:30]
- Sample distribution view [00:49]
TCGALand Introduction and Overview
TCGALand contains clinical -omic data from diverse cancer samples. OmicSoft has carefully curated and processed these data for exploration in OncoLand.
- TCGA Default View: Samples View [00:26]
- Changing Sample Grouping [01:16]
- Customizing Views in the View Controller [01:33]
- Selecting samples to view details [02:22]
- Filtering samples on metadata [03:02]
- Clinical Significance Group Association View[03:24]
- Survival Data View [04:13]
CCLELand Introduction and Overview
The Cancer Cell Line Encyclopedia (CCLE) provides -Omic and treatment response data for human cancer cell lines.
- Primary Site View [00:16]
- Filtering for samples with RNA-seq data [00:56]
Gene-Level OncoLand Views
- DNA Alteration Distribution View [00:26]
- DNAseq data Views [01:02]
- RNAseq expression Views [01:43]
- Viewing transcript expression differences between samples [02:33]
- Detecting Gene Fusions [04:28]
- Downloading Fusion Details [05:50]
- RNAseq mutation data[06:25]
- Somatic Mutation Site Distribution View [07:00]
- Microarray expression Views [07:28]
- Protein levels (RPPA) Views [07:45]
- Copy number variation (CNV) Views [08:25]
- DNA Methylation Views [09:04]
Multiple genes can be used in a single search, which enables additional Views designed for multi-gene analysis.
- Alteration Distribution By Gene View [00:41]
- Co-mutation Frequencies View [01:15]
- Alteration OmicPrint [01:45]
- Multi-Gene Variable View [02:32]
- Correlation of expression between genes [02:48]
- Fusions between specific genes[02:58]
- Fusion Genome Browser [03:50]
GeneSets provide a powerful way to group many genes together for multi-gene analysis.
- Creating the GeneSet source [00:16]
- Adding a GeneSet to OncoLand [00:28]
- Searching with a GeneSet [01:16]
- GeneSet (multi-gene) Views [01:42]
Using a gene of interest, you can discover correlations between your gene's expression and other genes with correlated expression, or changes in gene copy number.
- Correlating copy number to gene expression (CNV => RNA-seq Expression) [00:10]
- Identifying similarly-expressed genes (RNA-Seq Expression => RNA-Seq Expression) [01:06]
OncoLand allows efficient filtering of samples by extensive curated metadata. You can also create custom groupings of samples based on filters or selection.
- Create a SampleSet from Filter [00:24]
- Group a SampleSet from Selection [02:30]
- Use SampleSet metadata in Grouping [04:20]
- Managing SampleSets [04:41]
Viewing Sample RNA-seq coverage in Genome Browser
RNA-seq read coverage can be directly viewed for samples that you have selected in an OncoLand View, using the OmicSoft Genome Browser.
- Selecting samples to view (Browse Selected Samples)[01:45]
- Splitting RNA-seq tracks from Group to individual samples [02:59]
Custom queries allow users to identify samples that match user-defined criteria, which then is added as metadata. These metadata can be used for subsequent filtering, sorting, and grouping. Queries can be combined to further segment samples.
- Create an RNASeq_FPKM query [00:20]
- Filter and group samples by custom query [01:40]
- Create a directional RNASeq_Fusion query [02:37]
- Create categorical breakpoints for RNASeq_FPKM data [04:46]
- Group and color-code samples by query results [06:23]
- Combine queries [07:31]
- Custom query OmicPrint [08:50]
Report Top Genes
OncoLand samples can be queried, based on a SampleSet, to return the top genes in a category, including top expressed genes, top mutated genes, top fusion genes, etc.
- Creating a SampleSet to perform a query on [00:16]
- Creating Top Mutated Genes query [01:25]
- Retrieving Result Set from Server [02:47]
- Sorting Result Set to identify Top mutated genes [03:23]
Measurement data, such as compound responsiveness, can be imported into OncoLand to perform queries. Measurement data, similar to the example set in this video, can be found at http://www.cancerrxgene.org/downloads
- Measurement data file format [00:09]
- Importing measurement data [00:49]
- Managing measurement data [01:32]
- Adding metadata to measurements [01:51]
- Using measurement data in searches [02:28]
- Creating a Land data queries with measurement data [03:32]
- Using measurement data queries [04:57]
Download OncoLand data to Local Analysis
Data for genes and samples can easily be retrieved from Land, and saved as an Array Studio Analysis project. Here, the user can have even greater flexibility in creating Views and transforming data. Array Studio analysis modules can also be used on these data.
- Create new Local Analysis project [00:15]
- Options for downloading Land data [00:48]
- Explore local data objects [03:08]
Some example Views from local analysis of Land data
- Add a new View to local data [00:00]
- Color-code Heatmap View samples [00:24]
- Filter data displayed in a View [01:50]
- Change Heatmap values range [02:11]
- View expression of fusion genes in each sample [02:38]
VirtualLands allow you to combine datasets from different Lands into a single Land, so you can directly compare and analyze data from different sources.
- Create a new VirtualLand [00:30]
- Open a VirtualLand [01:20]
- Labeling View data by Source Land [01:52]