When this “early adopter” phase is complete, the authors expect to distribute the software free of charge to. The first AutoComp/AutoGate version is currently in the hands of a small group of users at Stanford, Emory and NIH. Ultimately, this process generates analysis “trees” that can be applied to automatically guide analyses for similar samples. Users guide analyses by successively specifying axes (flow parameters) for data subset displays and selecting statistically defined subsets to be used for the next analysis round. 10 FlowJo Version X Hacks That Will Help You Publish Your Flow Cytometry Data Written by Tim Bushnell, PhD So you just got the most amazing results of your life and you can wait to show it off in lab meeting, or create the figure for a publication.
AutoGate replaces the manual subsetting capabilities provided by current analysis packages with newly defined statistical algorithms that automatically and accurately detect, display and delineate subsets in well-labeled and well-recognized formats (histograms, contour and dot plots).
AutoComp acquires sample and reagent labels from users or flow data files, and uses this information to complete the flow data compensation task. To address this deficit, we have developed automated flow analysis software technology, provisionally named AutoComp and AutoGate. But while flow instruments have improved markedly, the development of automated tools for processing and analyzing flow data has lagged sorely behind.
Similarly, without flow cytometry, there would be a bleak future for stem cell deployment, HIV drug development and full characterization of the cells and cell interactions in the immune system. Note: Users would likely want to repeat this entire process of differential expression analysis (from setup to volcano plot filtering) for all populations of interest.Nowadays, one can hardly imagine biology and medicine without flow cytometry to measure CD4 T cell counts in HIV, follow bone marrow transplant patients, characterize leukemias, etc. Try gating genes upregulated and downregulated in your cluster of interest using the Volcano Plot: By default this correction is done with the Bonferroni method, but can be adjusted to the False Discovery Rate (FDR) method, or turned off entirely within the Graphs section of SeqGeq’s preferences. The q-value results from a Mann-Whitney U test p-Value, to which a correction for multiple observations has been applied. a q-Value).Ī Fold Change is simply the ratio of expression within the test population over the control population, which is calculated for each gene. You’ll note that these DOG parameters in the volcano plot are the result of two statistical tests between the populations set within the initial Gene View Graph Window: Fold Change between the two populations, and an adjusted p-Value (i.e.
This results in the creation of a new Gene View Graph Window illustrating a pair of Derived Observations of the Genes (DOGs for short). To do so, click the Volcano Plot icon at the top of the Gene View Graph Window: Now that you’ve set the Gene View Graph Window up properly, you can define statistically significant up and down-regulated genes for the populations being compared thereby opening the Volcano Plotting tool within SeqGeq. Try creating a NOT gate from one of the clusters within your workspace, open a Gene View Graph Window of the parent population, and place your population of interest (the “test”) onto the y-axis, and your control NOT gate, on the x-axis there: To create a NOT gate for one of the K-Means clusters, select that cluster within the workspace, visit the Boolean ribbon, and select NOT – This will create a NOT gate of the cluster within the workspace, as a sibling population of the cluster selected, denoted with a minus sign: In section 3 of this tutorial we added the Boolean ribbon to the Workspace tab of SeqGeq’s workspace. In the case of clusters defined by K-Means, a natural comparator population might be the Boolean NOT gate of a given cluster of interest. To begin differential expression analysis in SeqGeq you can open a Gene View Graph Window with your test population on the y-axis and the control (“comparator”) population on the x-axis. This type of analysis can identify a signature (aka “Hallmark”) Geneset for a disease state, cell type, or even individual subject. Differential Expression analysis is a way of identifying genes significantly upregulated and downregulated within a population of interest relative to a comparator population.