Thermo Scientific Cellomics ArrayScan VTI HCS 软件使用介绍.ppt

Slide/Page# * Slide/Page# * Slide/Page# * Slide/Page# * Slide/Page# * Slide/Page# * Slide/Page# * Slide/Page# * Slide/Page# * Slide/Page# * Slide/Page# * Slide/Page# * Illustrates risk of isodata threshold- if the object is bright enough and big enough the threshold will be too high to detect objects of interest Upper right panel show fixed threshold + local background correctio. BEST CHOICE. Smal dead zone around bright object, but most objects ID’d Slide/Page# * Slide/Page# * Slide/Page# * Slide/Page# * Slide/Page# * Slide/Page# * Slide/Page# * Slide/Page# * Slide/Page# * Slide/Page# * “For today’s training we ar going to focus on the image acquisition and analysis aspect, not so much on the data analysis aspect” Best Practice- Status classification is based on comparing (1) a raw cell feature to (2) a Reference Level, implying the Level meaningfully marks a boundary between distributions of normal response states that can be determined by preliminary analyses for which Levels are “garbage inputs”. In a second pass with meaningful Levels set, the Population Characterization outputs then are not garbage. Slide/Page# * Slide/Page# * Caveats benefits of different Object Identification Methods and Assay Parameters Images show caveats of Isodata (or PeakTriang or other histogram-based, dynamic) Object Identification Methods, as well as a caveat of the ObjectSegmentation Assay Parameter, and a benefit of the BackgroundCorrection Assay Parameter. I-V-S in non-1° (2°) channel(s) “1° object mask” MaskModifierChN(Assay Parameter) Object Selection(a.k.a. “gating”) Selected_ 1° Object Identification 1° Object Validationincludes 1° Object Selection “1° objects”with “1° masks” data results “Rejected_” overlay, usually*. If “Rejected_” because of 1°Ch, excluded from Valid_Count ? from Selected_Count ? from %Selected_ If “Rejected_” because of 2°Ch, included in Valid_Count excluded from Selected_Count reflected in %Selected_( = Selected_ ? Valid_Count) * BioApp-spe