Update README.md

README.md

@@ -49,7 +49,8 @@ At least one of these noise estimation functions should be run on the mass list
 The SNplot function is used to show the mass spectrum with the masses below and above the cut point denoted using the same color scheme as in the histogram plots from either HistNoise() or KMDNoise().
 
 ## Internal Mass Recalibration
-RecalList(), Recal(), and Recal_2() are functions pertaining to the internal mass recalibration method adapted from Kozhinov et al. (2013) and Savory et al. (2011) using a polynomial central moving average to estimate the weights used to recalibrate the masses (Kozhinov et al., 2013) applied to spectral segments (Savory et al., 2011). The function RecalList() can be used with the output of MFAssign() or MFAssignCHO() to generate a data frame containing potential recalibrant CH2 homologous series. There are a variety of metrics included in the output of this function to aid the user in picking suitable recalibrant series, these are described in greater detail in the example of RecalList() below. The user can select up to 10 homologous series as inputs for the mass recalibration with Recal() and Recal_2(). Recal() uses H2 and O KMD and z* series to identify additional MF that are related to the user selected recalibrants. In contrast, Recal_2() does not used those series to expand the pool of potential recalibrants, using only the peaks that correspond to the homologous series chosen as recalibrants. Other than this difference Recal() and Recal_2() work exactly the same. To avoid recalibration problems associated with too many recalibrant masses, the function uses a user-defined number of tallest peaks within a user-defined mass range “bin”. For example, if the bin width is set at 20 and the number of peaks is set at 2, the function will select the two tallest peaks within each 20 m/z window across the range of the spectrum. Additionally, when the monoisotopic peak chosen as a recalibrant has an identified 13C peak, that isotopic peak will also be added to the pool of recalibrants being used. After the recalibrants have been selected, they are split into mass windows of a user defined width (default is 50 m/z) and  used to calculate the correction term according to the the adapted form of the Kozhinov et al. method. This will provide a different mass correction term for each mass window in the spectrum. Then the raw mass list(s) that are being recalibrated are split into the same mass windows, and the correction term that is associated with each window is used to correct the masses in that window, thus recalibrating the full spectrum section by section. In addition to the output of recalibrated mass lists the function also generates a plot that shows the recalibration peaks that were used in context with the overall mass spectrum, and produces an output data frame containing the mass, abundance, formula, and error for the recalibrants that were used. 
+RecalList(), Recal(), Recal_2(), RecalX(), and Recal_2X() are functions pertaining to the internal mass recalibration method adapted from Kozhinov et al. (2013) and Savory et al. (2011) using a polynomial central moving average to estimate the weights used to recalibrate the masses (Kozhinov et al., 2013) applied to spectral segments (Savory et al., 2011). The function RecalList() can be used with the output of MFAssign() or MFAssignCHO() to generate a data frame containing potential recalibrant CH2 homologous series. There are a variety of metrics included in the output of this function to aid the user in picking suitable recalibrant series, these are described in greater detail in the example of RecalList() below. The user can select up to 10 homologous series as inputs for the mass recalibration with Recal() and Recal_2(). Recal() uses H2 and O KMD and z* series to identify additional MF that are related to the user selected recalibrants. In contrast, Recal_2() does not used those series to expand the pool of potential recalibrants, using only the peaks that correspond to the homologous series chosen as recalibrants. Other than this difference Recal() and Recal_2() work exactly the same. To avoid recalibration problems associated with too many recalibrant masses, the function uses a user-defined number of tallest peaks within a user-defined mass range “bin”. For example, if the bin width is set at 20 and the number of peaks is set at 2, the function will select the two tallest peaks within each 20 m/z window across the range of the spectrum. Additionally, when the monoisotopic peak chosen as a recalibrant has an identified 13C peak, that isotopic peak will also be added to the pool of recalibrants being used. After the recalibrants have been selected, they are split into mass windows of a user defined width (default is 50 m/z) and  used to calculate the correction term according to the the adapted form of the Kozhinov et al. method. This will provide a different mass correction term for each mass window in the spectrum. Then the raw mass list(s) that are being recalibrated are split into the same mass windows, and the correction term that is associated with each window is used to correct the masses in that window, thus recalibrating the full spectrum section by section. In addition to the output of recalibrated mass lists the function also generates a plot that shows the recalibration peaks that were used in context with the overall mass spectrum, and produces an output data frame containing the mass, abundance, formula, and error for the recalibrants that were used. 
+RecalX() and Recal_2X() are similar to Recal() and Recal_2(), but provide some iteration of the mass calibration and can be used more effectively with small mass windows. The homologous series are chosen in the same way as in Recal() and Recal_2(), but then they are used to do a single term recalibration for the entire spectrum instead of segments. These calibrated masses are then used to do a segmented recalibration. Within each segment the recalibrants from the previous step are used and then the tallest peaks assigned a molecular formula within each window are selected as recalibrants, with half above the central recalibrant and half below. 
 
 # Function Examples
 ## Recommended Order of Operations
@@ -64,7 +65,7 @@ The functions will be described in the order that they are most effectively used
 
 5. Use RecalList() to generate a list of the potential recalibrant series.
 
-6. After choosing a few recalibrant series, use Recal() (or Recal_2()) to check whether they are good recalibrants and recalibrate the mass lists using those recalibrants.
+6. After choosing a few recalibrant series, use Recal() (or Recal_2(), RecalX(), Recal_2X()) to check whether they are good recalibrants and recalibrate the mass lists using those recalibrants.
 
 7. Use MFAssign() with the recalibrated mass lists to assign MF to the data.