detect_sig

<p>A p-length vector that represents a background-removed spectrum with p frequency channels.</p>

<p>Calculate the pvalues and false discovery rates (FDRs) for a background-removed spectrum</p>

Implements a new approach 'SABARSI' described in Wang et al., "A Statistical Approach of Background Removal and Spectrum Identification for SERS Data" (Unpublished).
Sabarsi forms a pipeline for SERS (surface-enhanced Raman scattering) data analysis including background removal, signal detection, signal integration, and cross-experiment comparison. The background removal algorithm, the very first step of SERS data analysis, takes into account the change of background shape.

Jun Li

sabarsi

detect_sig: Calculate the pvalues and false discovery rates (FDRs) for a background-removed spectrum

Description

Usage

Arguments

Value