@oruebel Yesterday we uploaded a file that was acquired on a MALDI LTQ/XL (Thermo) and had been converted to mzML using msconvert with default parameters. BASTet correctly identified the unique m/z values and binned the data on m/z axis, but its really hard to read when plotted as a line plot.
I think adding ~5ppm binning as a minimum failsafe would help prevent this.
Basically debunch the closely spaced m/z values and then add in m/z values where closest is far away.
I'd be happy if this was in "analysis_0" so the raw data could be preserved. With the new jupyter.nersc.gov setup users should be able to add custom analysis to their openmsi files without much trouble at all.
@oruebel Yesterday we uploaded a file that was acquired on a MALDI LTQ/XL (Thermo) and had been converted to mzML using msconvert with default parameters. BASTet correctly identified the unique m/z values and binned the data on m/z axis, but its really hard to read when plotted as a line plot.
I think adding ~5ppm binning as a minimum failsafe would help prevent this.
Basically debunch the closely spaced m/z values and then add in m/z values where closest is far away.
I'd be happy if this was in "analysis_0" so the raw data could be preserved. With the new jupyter.nersc.gov setup users should be able to add custom analysis to their openmsi files without much trouble at all.