  Group testing is a technique which avoids individually testing $n$ samples
for a rare disease and instead tests $n < p$ pools, where a pool consists of a
mixture of small, equal portions of a subset of the $p$ samples. Group testing
saves testing time and resources in many applications, including RT-PCR, with
guarantees for the recovery of the status of the $p$ samples from results on
$n$ pools. The noise in quantitative RT- PCR is inherently known to follow a
multiplicative data-dependent model. In recent literature, the corresponding
linear systems for inferring the health status of $p$ samples from results on
$n$ pools have been solved using the Lasso estimator and its variants, which
have been typically used in additive Gaussian noise settings. There is no
existing literature which establishes performance bounds for Lasso for the
multiplicative noise model associated with RT-PCR. After noting that a recent
general technique, Hunt et al., works for Poisson inverse problems, we adapt it
to handle sparse signal reconstruction from compressive measurements with
multiplicative noise: we present high probability performance bounds and
data-dependent weights for the Lasso and its weighted version. We also show
numerical results on simulated pooled RT-PCR data to empirically validate our
bounds.
