Usecase ea: Difference between revisions
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GWAS-LD enrichment analysis examines the number of TSSs overlapping with LD blocks on the genome or a '''given TSS ID list''' provided by the user ('''e.g., significant TSS sets from differential analysis'''). Here we introduce the workflow of this method. | GWAS-LD enrichment analysis examines the number of TSSs overlapping with LD blocks on the genome or a '''given TSS ID list''' provided by the user ('''e.g., significant TSS sets from differential analysis'''). Here we introduce the workflow of this method. | ||
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<p style="text-align: center;"><img src="https://raw.githubusercontent.com/suimye/test_github/main/uPic/%E3%82%B9%E3%82%AF%E3%83%AA%E3%83%BC%E3%83%B3%E3%82%B7%E3%83%A7%E3%83%83%E3%83%88%202023-06-05%2023.36.36.png" style="zoom:60%;"></p> | |||
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===== 1. Differential analysis is carried out with refTSS as the reference ===== | ===== 1. Differential analysis is carried out with refTSS as the reference ===== | ||
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# TSS tag counts are calculated against the refTSSv4 TSS reference using TSS sequencing data (e.g., CAGE, RAMPAGE, etc.). For instance, the `featureCounts` function in the Subread package can be useful for this process. | # TSS tag counts are calculated against the refTSSv4 TSS reference using TSS sequencing data (e.g., CAGE, RAMPAGE, etc.). For instance, the `featureCounts` function in the Subread package can be useful for this process. | ||
# Normalization and statistical testing were performed by standard packages such as DESeq2 and edgeR. For example, edgeR is used to calculate statistical significance between treated and control samples. | # Normalization and statistical testing were performed by standard packages such as DESeq2 and edgeR. For example, edgeR is used to calculate statistical significance between treated and control samples. | ||
# The IDs of TSSs showing significant expression changes are extracted from the testing results. These TSS IDs are input into the refTSS website for GWAS-LD enrichment analysis. Here is an example file of TSS ID lists from CAGE analysis of neural crest cell differentiation experiments with iPS cells ([[https://reftss.riken.jp/reftss/userfiles/gwas_ld_example_ids.230607.txt|day0_vs_day18]]). | # The IDs of TSSs showing significant expression changes are extracted from the testing results. These TSS IDs are input into the refTSS website for GWAS-LD enrichment analysis. Here is an example file of TSS ID lists from CAGE analysis of neural crest cell differentiation experiments with iPS cells ([[Source::https://reftss.riken.jp/reftss/userfiles/gwas_ld_example_ids.230607.txt| day0_vs_day18]]). | ||
===== 2. GWAS-LD enrichment analysis ===== | ===== 2. GWAS-LD enrichment analysis ===== | ||
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From the menu, select refTSS-LD and paste the TSS IDs into the box. | From the menu, select refTSS-LD and paste the TSS IDs into the box. | ||
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===== 3. Interpreting the results of the enrichment analysis ===== | ===== 3. Interpreting the results of the enrichment analysis ===== | ||
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The table shows the results of the GWAS-LD analysis conducted using the example data. Results are sorted by order of FDR. The enriched TSS IDs are displayed in the "hits" column, allowing for inspection of which TSSs are concentrated in LD blocks grouped by traits. | The table shows the results of the GWAS-LD analysis conducted using the example data. Results are sorted by order of FDR. The enriched TSS IDs are displayed in the "hits" column, allowing for inspection of which TSSs are concentrated in LD blocks grouped by traits. | ||
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GO and KEGG pathway annotation enrichment analysis examines the over-representation of genes with biological annotations such as GO and KEGG pathways. Here we introduce the workflow of general annotation enrichment analysis using refTSS and external enrichment analysis tools. | GO and KEGG pathway annotation enrichment analysis examines the over-representation of genes with biological annotations such as GO and KEGG pathways. Here we introduce the workflow of general annotation enrichment analysis using refTSS and external enrichment analysis tools. | ||
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<p style="text-align: center;"><img src="https://github.com/suimye/test_github/assets/296176/bca5220d-ebd4-48a2-a923-079112ce2a64" style="zoom:60%;"></p> | |||
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===== 1. General annotation enrichment analysis is carried out with refTSS as the reference ===== | ===== 1. General annotation enrichment analysis is carried out with refTSS as the reference ===== | ||
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# The IDs of TSSs showing significant expression changes are extracted from the differential expression analysis results. | # The IDs of TSSs showing significant expression changes are extracted from the differential expression analysis results. | ||
# The TSS IDs are input into the ID conversion tool on the refTSS website. | # The TSS IDs are input into the ID conversion tool on the refTSS website. | ||
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# The extracted Gene Symbols are input into external enrichment analysis tools such as '''DAVID, MetaScape, and ClusterProfiler'''. | # The extracted Gene Symbols are input into external enrichment analysis tools such as '''DAVID, MetaScape, and ClusterProfiler'''. | ||
Revision as of 10:27, 3 October 2024
3. Workflow of GWAS-LD enrichment analysis
GWAS-LD enrichment analysis examines the number of TSSs overlapping with LD blocks on the genome or a given TSS ID list provided by the user (e.g., significant TSS sets from differential analysis). Here we introduce the workflow of this method.
1. Differential analysis is carried out with refTSS as the reference
- TSS tag counts are calculated against the refTSSv4 TSS reference using TSS sequencing data (e.g., CAGE, RAMPAGE, etc.). For instance, the `featureCounts` function in the Subread package can be useful for this process.
- Normalization and statistical testing were performed by standard packages such as DESeq2 and edgeR. For example, edgeR is used to calculate statistical significance between treated and control samples.
- The IDs of TSSs showing significant expression changes are extracted from the testing results. These TSS IDs are input into the refTSS website for GWAS-LD enrichment analysis. Here is an example file of TSS ID lists from CAGE analysis of neural crest cell differentiation experiments with iPS cells (day0_vs_day18).
2. GWAS-LD enrichment analysis
From the menu, select refTSS-LD and paste the TSS IDs into the box.
3. Interpreting the results of the enrichment analysis
The table shows the results of the GWAS-LD analysis conducted using the example data. Results are sorted by order of FDR. The enriched TSS IDs are displayed in the "hits" column, allowing for inspection of which TSSs are concentrated in LD blocks grouped by traits.
4. Workflow of general annotation enrichment analysis
GO and KEGG pathway annotation enrichment analysis examines the over-representation of genes with biological annotations such as GO and KEGG pathways. Here we introduce the workflow of general annotation enrichment analysis using refTSS and external enrichment analysis tools.
1. General annotation enrichment analysis is carried out with refTSS as the reference
- TSS tag counts are calculated against the refTSSv4 TSS reference using TSS sequencing data (e.g., CAGE, RAMPAGE, etc.). The `featureCounts` function in the Subread package can be useful for this process.
- Normalization and statistical testing were performed by standard packages such as DESeq2 and edgeR. For example, edgeR is used for statistical significance calculations between treated and control samples.
- The IDs of TSSs showing significant expression changes are extracted from the differential expression analysis results.
- The TSS IDs are input into the ID conversion tool on the refTSS website.
- The extracted Gene Symbols are input into external enrichment analysis tools such as DAVID, MetaScape, and ClusterProfiler.