Choose data: FishCODE analysis samples can come from built-in datasets or/and from user-uploaded datasets, so in the "Choose data" section, you can select "Build-in datasets" (selected by default) or/and "User's own datasets", select the corresponding Checkbox means selecting the corresponding part, unchecking the box means not using the part.

Selectors: user selects the species selector on the left and then the experimental factor selector on the right, and each time the user clicks on the selector, the dataset list below refreshes. The content of the experimental factor selector on the right will actively change and refresh as the species selected by the user changes. When canceling the checkbox in the upper left corner, an operation prompt box will pop up. The user needs to confirm his cancellation operation. Click "Confirm" in the lower right corner to confirm this operation, or click the "Cancel" button to cancel this operation.

User's own datasets: this module is closed by default. Click the checkbox in the upper left corner to expand the module and upload data. It should be noted that the "Group File" file must be uploaded. When you cancel the checkbox in the upper left corner, an operation prompt box will pop up, which is similar to the description in the "Build-in datasets selectors" section.

Design experiment: user arranges the combined data samples into groups. Note that the transcript group must meet at least 2 groups, and each group has at least 2 samples. It is worth noting that the files uploaded by users should not be larger than 200MB and the sample size of a task cannot exceed 30 samples.

Remove batch effects: original distribution: It shows the pca results of the data set finally formed by the user after scale normalization, and the default is to draw the confidence interval according to the project name. new distribution: There are many ways to process the batch effect of data. 1. Use the built-in function of DESeq to add the batch effect category as a covariate to the analysis. 2. ComBat uses the ComBat_seq function of the sva R package for data correction. Of course, if the original distribution has no obvious batch effect, the user can also select no to use the original distribution for the following analysis.

Analyze data : parameters cover quality control, differential analysis parameter settings, visualization parameters for display results (heat maps), etc.

Result visualization : (A) Visualization of sample PCA analysis results. (B) The information (including gene abundance, fold change, P value, etc.) of the genes (the top 10 genes with the largest absolute value of default log2FoldChange). (C) Volcano plot of differentially expressed genes. (D) Heatmap of the most significant differentially expressed gene (the top 50 genes with the smallest p value by default).

Fine tuning parameters: all parameters are divided into seven parts as shown in a. Overall: Represents the overall parameters of the image such as image format, resolution, etc. Title: Represents the title settings of the most commonly used pictures, including the main title, subtitle, lower right/upper left title, x/y axis title and legend title. Drawing object: Represents the properties of the picture drawing object, that is, the data is mapped to color, size, padding, etc. Coordinate axis: style: All settings of the x/y axis except the title include the styles of the axis, labels, etc., as well as the property settings for the x/y axis of the data. content: For continuous and discrete designs, including data transformation, location, label reset (breaks, labels). Legend: The style setting parameter part of the legend. Panel: This part mainly sets the panel style, you can cancel the auxiliary line, change the panel color, etc. in this part. Function: This part integrates some functions, including adding image element name, adding text on the image, enlarging the image, reversing the coordinates and other functions. After setting the parameters, click the detail plot in part b to modify the picture. By default, the results of each modification will not be cleared. If you want to clear all previous options, please click the clear all button in part b.

Fine tuning behavior: each time you click detail modify, you will submit once, as shown in A. at present, the user has submitted to PIC4. At this time, click pic. 3, as shown in B, and you will jump to pic. 3 to submit the picture, as shown in C. Any modification at this time is based on PIC. 3. As shown in pic. 5, the figure is actually modified from pic. 3 rather than PIC4. If you find that you still haven't added after switching, it should be that the content of your selection panel is not clear and clean.

FishCODE sources: the analysis results provide an online differential analysis gene list, and the user clicks on the gene to jump to the detailed information page of the gene in the database.