Meta-analysis of Nanostring nCounter datasets

The purpose of the meta-analysis is to provide posterior estimates to plug in to prior distributions of model parameters in the MetaNorm procedure. The analysis is based on a complex Bayesian hierarchical model, similar to the ones used in MetaNorm and RCRnorm. The model is designed specifically for these datasets, and while not mean to be reproduced with other data, can certainly be a guide for similar analyses.

In the meta_analysis_data.RData, we provide positive probe data for the 13 collected datasets. To curate the data and generate empirical estimated coefficients based on this data simply run the following.

library(MetaNorm)
data("meta_analysis_data")
ds = curate_data(dataset=ds)
results = find_regression_coefs(df=ds)

The minimal requirement is that the data contains 5 columns which must be named

  1. DataSet: An unique ID given to each study

  2. RNA: The designated mRNA measurement of the positive probes. They must be 128, 32, 8, 2, 0.5, and 0.125.

  3. SampleID: An unique ID given to each patient in each study

  4. Count: The actual measurements

  5. UID: An unique ID given to each combination of patient and study

The data curation involves two steps: creating indices that are consecutive and performing linear regression to get empirical estimates of intercepts, slopes, and residuals.

Once these are done, you are ready to perform meta analysis

Draws = meta_analysis(ds=results$df,
                  coeffs2=results$coeffs2,
                  M=12000,
                  n_keep=5000)