When a feature (gene) has low expression, it will be filtered by automatic independent filtering. To avoid this, you can either filter features to exclude low expression features before Deseq2, or in the Deseq2 advanced options, choose apply independent filtering setting. Details about independent filtering can be found at the Deseq2 documentation.
Click here for troubleshooting other differential analysis models and "?" results
Fold change indicates the extent of increase or decrease in feature expression in a comparison. In Partek Flow, fold change is in linear scale (even if the input data is in log scale). It is converted from ratio, which is the LSmean of group one divided by LSmean of group two in your comparison. When the ratio is greater than 1, fold change is identical to ratio; when the ratio is less than 1, fold change is -1/ratio. There is no fold change value between -1 to 1. When ratio/fold change is 1, that means there is no change between the two groups.
Log ratio option in Partek Flow is converted from ratio, this is a value comparable to log fold change in some other tools.
Yes, go to Style in the Data Viewer and make sure Gene name is selected under "Labeling". Next, go to the in plot selection tools (right side of the graphic) and use any of the selection tools to select the cells that you would like to label. You can use ctrl or shift to select multiple populations at once. For more information on the Volcano plot click here.
By default, Flow is using the p value <= 0.05 and |fold change|>=2 as the significance cutoff. If genes meet both p value and fold change cutoff, they are significantly up or down regulated genes. If they only meet one criteria, they are called inconclusive. If genes won't pass either criteria, they are not significant. Click on the Statistics button in the Configure section in the left control panel, you can change the cutoff. Click on the Style button to change the color of significance categories.
FDR is the expected proportion of false discoveries among all discoveries. FDR Step-up is a particular method to keep FDR under a given level, alpha, that was proposed in this paper. In Partek Flow, if one calls all of the features with p-values 0.02 or less, the FDR is less or equal to 0.41.
You should have at least the following two attributes in the Metadata, treatment (including two subgroups) and subject ID (to pair the two samples). When performing differential analysis, choose ANOVA and include both attributes into the ANOVA model, the two-way ANOVA is mathematically equivalent to paired t-Test.
Yes, you can use the Compute biomarkers task to compare one subgroup at a time to all of the others combined. An alternative option is to set up the differential analysis model in this way; for more information please see the information here for each model.
In the Quantifying to an annotation model dialog, by default, Partek Flow filters features based on the total count across all of the samples and features with a total count greater than 10 will be reported. If you want to report all of the genes in the annotation file, change the Filter features value to 0.