1. Mass Spectrum Normalization
Deming Mi

2. Data 99 liver homogeneous lysate
Can be seen as homogeneous fractions of a single sample
Spectra were baseline corrected using ProTS Data
ProTS Data baseline subtraction algorithm may result in negative intensity
No normalization was performed

5. Pointwise Normalization
Add an amount to all intensities to make them all positive
Use “splinefun()” to perform cubic spline interpolation of spectrum data
For each interpolated intensity yij (intensity of spectrum i at m/zi), compute the median yi
For each spectrum i, compute the pointwise Log normalization factor log(yi/ yij)
For each spectrum i, use “ksmooth()” to obtain kernel regression estimate of those pointwise Log normalization factor log(yi/ yij)
Convert the kernel smoothed log(yi/ yij) back to yi/ yij
Use the smoothed normalization factor yi/ yij to normalize spectrum i.

14. Localized Normalization (TIC) by m/z windows
Generate windows with width of 250 data points
Compute the normalization factors (TIC) for each window (a total of 194 such windows)
Smooth (ksmooth: kernel regression smoother) those 194 normalization factors
Normalize each window of the spectrum with the smoothed normalization factor