RNA Seq Brainstorming

[kunalbhutani]

Quick brainstorm of RNA-Seq research plans.

Samples
001_002_RNA_Blood_Scripps-10-1_02-02-15
001_002_RNA_Blood_Scripps-1_09-25-14
001_002_RNA_Blood_Scripps-5_10-29-14
001_002_RNA_Blood_Scripps-7_11-25-14
001_002_RNA_Blood_Scripps-9-1_12-19-14
001_003_RNA_Blood_Scripps-2_09-25-14
001_003_RNA_Blood_Scripps-6_10-29-14
001_003_RNA_Blood_Scripps-8_11-25-14
001_004_RNA_Blood_Scripps-3_09-25-14
001_005_RNA_Blood_Scripps-4_09-25-14

We have 5 samples for the individual 002, 3 samples of individual 003, and then one for individuals 004 and 005.

Variation Across Individuals

• Comparisons across individuals to confirm that these samples are different people. Standard PCA plot.

  Variation Within Individuals

• Overall trends (similar to PCA); ideally more differences across individuals than within individuals
• Changes over time
  • Linear Trends
  • Non-linear trends (change point analysis; spikes (outliers))

    Comparisons with metabolomic data

• Difficulty level: over 9000
  • Studies by metablome showed that little to no correlation between blood expression and plasma metabolites, since plasma metabolites come from entire body.
• Metabolite - Gene relationships mined from http://consensuspathdb.org/
• Correlation across time
• Lag in response; although this is a month difference between timepoints, maybe metabolite level precedes increase in expression level. Although this will again be muddled.

Feel free to add or modify these ideas as you see fit.

[kunalbhutani]

Data can be found in data/family3/rna-seq/bcbio_03202015/853005_Tanner_RNASeq . Would recommend TPM values for comparisons, but should review literature.