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sabarsi (version 0.1.0)

Background Removal and Spectrum Identification for SERS Data

Description

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described in Wang et al., "A Statistical Approach of Background Removal and Spectrum Identification for SERS Data" (Unpublished).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.

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Version

Install

install.packages('sabarsi')

Monthly Downloads

8

Version

0.1.0

License

GPL-3

Maintainer

Jun Li

Last Published

August 8th, 2019

Functions in sabarsi (0.1.0)

merge_signals

Obtain the set of signature signals Merge groups of concatenated signals and give the time indices of signature signals.
detect_sig

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

Perform background removal on the whole SERS spectrum data set. Divide the SERS spectrum data into time-frequency blocks and remove background locally.
SERS

A real SERS spectrum data set in
shift_match

Match signals from two experiments. For each signal in the first experiment, shift.match function finds the best matched signal in the second experiment. This function takes the potential frequency shifts into consideration for similarity measurement.
signal_detection

Detect signals in background-removed spectra