AMP PD Transcriptomics Differential Expression All Time Points
Craig Lab, Department of Translational Genomics, Keck School of Medicine of USC
The Craig Lab at USC has been heavily involved in AMP-PD, beginning with the PPMI project and continues to be a valuable contributing member of the community. Their expertise is in RNAseq analysis and visualization as exemplified in the AMP-PD RNA-Seq Explorer, found here on Terra.
Mission Statement: We are deeply committed to excellence in translational genomics research, bringing to bear vast experience and expertise in molecular genetics, genome science, biomedical informatics, translational science, and molecular medicine. Our ultimate goal is to serve the Keck community by bridging basic and clinical research through discovery and validation of novel diagnostics and therapeutics for earlier diagnosis and smarter treatments.
Analysis Methodology
Note: The analysis that produced this target list is available to all registered AMP PD users in Terra and can be reused in your work.
Introduction
The AMP-PD Target Explorer uses transcriptomic data described here.
3 Studies were included in this analysis:
| Number of Samples Successfully Processed | |
|---|---|
| BioFIND | 165 |
| PDBP | 3296 |
| PPMI | 4622 |
Methods:
Outliers and covariates
Outlier samples
- Principal components were correlated to metadata and QC metrics. RIN and PCT USABLE BASES (Picard RnaSeqMetrics) were used to set cut offs for outlier detection.
- Tools:
- R
Covariates
- To help identify covariates, principal components were correlated to metadata and QC metrics. variancePartition was used to validate potential covariates. Finally a neutrophil score was developed to fill the gap in CBC data.
- Tools:
- R
- variancePartition
- Input:
- fullTableRNAseqMeta_TNT.csv created by: Terra Notebook
- neutAndLymph.csv generated using: Lymphocyte and neutrophil enriched genes, from Protein Atlas Human Proteome
- Output:
- default covariates:
sex + plate + age + neutPerc + lymphPerc
- default covariates:
Expression
Differential expression
- Cohorts for DE were created by filtering metadata for case/control status, visit month, and genetic mutation status. DE was performed with and without neutrophil score as a covariate.
- Tools:
- R
- edgeR
- limma
- Transcriptomics Differential Expression Terra Workspace
- Input:
matrix.featureCounts.tsv- raw count data (output from Subread featureCounts)fullTableRNAseqMeta_TNT.csv- table of relevant sample metadata
- Output:
_limmaResults.tsv- differential expression table including fold change, p-values, and average expression- Plots (volcano and mean difference)
Underlying Analyses
Differential Expression
Gene, Transcript
Results of PD case versus HC differential expression analysis. Differential expression was assessed using linear modeling and empirical Bayes moderation (employing default settings) with limma+voom, using sex, age, plate, derived neutrophil and lymphocyte percentages in the design formula in addition to disease status. Genes with an adjusted p < 0.05 and a log2 fold change > 0.1 are considered differentially expressed. Multiple hypothesis testing was performed with the decideTests() function in limma using the default adjustment method (Benjamini-Hochberg).
Whole Blood
Transcriptomics
BioFIND, Parkinson's Disease Biomarkers Program (PDBP), Parkinson's Progression Markers Initiative (PPMI)
Data Dictionary
| Field Name | Field Name Expanded | Short Description (optional) |
|---|---|---|
| ensembl_gene_id | ENSEMBL Gene ID | |
| hgnc_symbol | HGNC symbol | |
| gene_type | Gene type from GENCODE | |
| logfc | Log2 Fold Change | |
| AveExpr | Average Expression | |
| t | T-test statistic | |
| p_val | p-Value | |
| adj_p_val | Adjusted p-value |