Quick start¶
Step 0: Define the task¶
DriverPower aims to identify cancer driver candidates from somatic variants of a tumour cohort. The minimal number of samples required in the PCAWG study is 15. However, more samples will have more power to detect rare driver events. Both whole-genome sequencing and panel sequencing variants can be used.
The basic test unit accepted by DriverPower is called a genomic element, which is a set of disjoint (or continuous) genomic regions (see the figure below). In our manuscript, we also removed all blacklist regions to reduce the effect of variant artifacts. In the real world, a genomic element can be all exons of a gene, or all TF binding sites among a promoter etc.
Typically 1K to 100K genomic elements are tested together such as ~20K genes in the human genome. For each element, given its functional impact, mutational burden as well as thousands of features, DriverPower will report a p-value and q-value associated with that element.
Step 1: Prepare the data¶
DriverPower runs on processed training and test data as described in data format.
Step 2: Build the background mutation rate model¶
The background mutation rate (BMR) model is used to estimate the expected number of somatic mutations for each genomic element,
given its features. DriverPower sub-command model is used to train BMR models. To build the BMR model, training features
(X) and responses (y) are required. DriverPower supports two algorithms for the BMR model, generalized linear models (GLM)
and gradient boosting machines (GBM).
See the model sub-command for all parameters and notes. Example code snippets are as follows:
$ driverpower model \
--feature PATH_TO_X \
--response PATH_TO_y \
--method GLM
$ driverpower model \
--feature PATH_TO_X \
--response PATH_TO_y \
--method GBM