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Multiple Omic's analysis
The online tool Visual Omics integrates various free analysis processes including DE analysis, Enrichment analysis, protein interaction prediction, protein domain prediction,traditional statistical tests, omics analysis single analysis.
Image Modification
The online image modification function mainly relies on the R package ggplot2 and its series of R packages. The main 38 elements and 21 functions in ggplot2 are selected, with a total of 262 parameters. The main features are additive modification and working picture switching.
Basic operation: a:  you need to select the type of picture you need to draw or analyze. Click (a) which means you have selected this drawing or analysis method. b:  shows the principle of the analysis selected by the user, the operation steps and the main output picture. c and d:  c shows that the user can upload data by copying the data (tab-splitting) and pasting it. d shows that users can upload data by uploading files. e:  The main parameter part of the analysis plot is displayed, and the required part in red is the option that must be set.
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.
Err report:If you find a bug, please inform the author of the error in time and declare again that this version is a test version and should have many bugs. If you find anything abnormal in the process of use, please be sure to feed back the error information to me. I will be very grateful. When you feed back the error, you need to click the place shown in the figure to feed back the err report screenshot and the error to me.
Download: Pictures in any format are based on orders_ It's named in the form of number. It's worth mentioning that the content of downloaded files will be switched with the switching when the switching is different. There's no need to worry about the mismatch between downloading and switching. Another important thing is, for example, PIC. 1 in the picture, but it is in the download_ 3. There is a conversion relationship between the two, so don't worry. Except for the pictures, all the intermediate results of the analysis are given in TXT format. The file at the end of. Data is the R native object, which may be helpful to you
DE Analysis
The gene count file and grouping file input by the user can get the output files of the up-regulated and down-regulated genes and the sample pca, differential volcano map, and gene expression heat map output pictures in about one minute. This module mainly powered by R package DESeq2
Steps:
1. [ Required ] Upload the file for gene expression information: Note: paste part only support TAB-delimited file as in the example file. |
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2. [ Required ] Upload the file for group information: Note: paste part only support TAB-delimited file as in the example file. |
1. [ Required ] Upload the file for gene epression information: Note: only supports csv, xls, xlsx, txt ( TAB-delimited ) as in the example file, and does not support unicode type files. |
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2. [ Required ] Upload the file for group information: Note: only supports csv, xls, xlsx, txt ( TAB-delimited ) as in the example file, and does not support unicode type files. |
3. Main parameters:
Filter low-expressed genes, at least
meet the number of reads of more than
Genes with a significance test p-value less than
and the absolute value of the fold change greater than
will be marked as significantly different.
The top
in ascending p-value order of the
regulation will be plotted as a heatmap.
Add sample group:
Data transform:
Add hclust on sample and gene:
Enrichment Analysis
The underlying foundation of enrichment analysis is the clusterProfiler R package, and the id conversion function uses the mapIds function of the Annotation package. The functions of this module include GO and KEGG data analysis with and without parameters, among which the involved genomes are provided by bioconductor, including ten popular model organisms including human, mouse, and zebrafish. For the enrichment analysis of the parameter-free genome, the user needs to provide the background file of the gene, that is, the KO id or GO id of the gene.
Steps:
1. [ Required ] Paste the file which contain colnum gene symbol or ncbi id or ensembl id: Note: paste part only support TAB-delimited file as in the example file. It is worth noting that the input file must require column names.The example file is the standard file of DEseq2 output results. In fact, the user input file only needs to contain a column of gene symbol/ID. |
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2. [ Optional ] Upload the file for background annotation file of the gene: Note: paste part only support TAB-delimited file as in the example file. |
1. [ Required ] which contain colnum gene symbol or ncbi id or ensembl id: Note: only supports csv, xls, xlsx, txt ( TAB-delimited ) as in the example file,and does not support unicode type files. It is worth noting that the input file must require column names.The example file is the standard file of DEseq2 output results. In fact, the user input file only needs to contain a column of gene symbol/ID. |
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2. [ Optional ] Upload background annotation file of the gene: Note: only supports csv, xls, xlsx, txt ( TAB-delimited ) as in the example file,and does not support unicode type files. It is worth noting that the input file must require column names |
3. Main parameters:
[ Required ]Select the column name that contains the gene name/gene id in the uploaded file:
The type of data the uploaded file contains:
Analysis type:
Only biological pathways or GO terms with a p-value less than:
after:
checks will be marked significantly
Have reference genome annotation:
Choose a reference genome:
Background target ID/symbol
GO/KO ID
Go enrichment classification display:
Transform the y-axis data:
Protein Domain Prediction
The gene protein sequences entered by the user are aligned with the Pfam-A.hmm data, and then the alignment results are visualized based on the R package drawProteins.
1. [Required] Paste the file which like fasta file: Note: only supports fasta type file as in the example file. |
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1. [ Required ] which contain colnum gene symbol or NCBI or Ensembl ID: Note: only supports fasta type file as in the example file, and does not support unicode type files. |
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Protein Interaction Prediction
This module mainly relies on the STRING database for protein interaction prediction. For the single visualization of the STRING database, our later visualization is mainly displayed by the ggraph package. This module is dedicated to protein interaction prediction and publication-level mapping in one website.
1. [ Required ] Paste the file which Must include a column of gene symbol or gene ncbi/ebi id: Note: paste part only support TAB-delimited file as in the example file. It is worth noting that the input file must require column names.The example file is the standard file of DEseq2 output results. In fact, the user input file only needs to contain a column of gene symbol/ID. |
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1. [ Required ] which Must include a column of gene symbol or gene NCBI/Ensembl ID: Note: only supports csv, xls, xlsx, txt ( TAB-delimited ) as in the example file,and does not support unicode type files. It is worth noting that the input file must require column names.The example file is the standard file of DEseq2 output results. In fact, the user input file only needs to contain a column of gene symbol/ID. |
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2. Main parameters:
[ Required ]Select the column name that contains the gene name/gene ID in the uploaded file:
The type of data the uploaded file contains:
Choose a reference genome:
and score_threshold:
Removed free interactions:
Choose a layout:
and edge style:
and edge colour:
Node colour:
and node alpha:
Add node text:
Dege greater than
and repel:
and size:
Ordinations Analysis
The gene protein sequences entered by the user are aligned with the Pfam-A.hmm data, and then the alignment results are visualized based on the R package drawProteins.
1. [ Required ] Paste the file: Note: paste part only support TAB-delimited file as in the example file.It is worth noting that the input file must require column names |
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1. [ Required ] : Note: only supports csv, xls, xlsx, txt ( TAB-delimited ) as in the example file, and does not support unicode type files. It is worth noting that the input file must require column names |
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2. Main parameters:
Data configuration
[ Optional ] In general, sorting analysis needs to specify a classification variable as a grouping
Standardized settings:
Analytical method:
Ordinations specific methods:
Method distance settings:
Confidence ellipse:
The type of ellipse
The level at which to draw an ellipse
Classic Statistical Analysis
The user only needs to enter the data and simply select whether to add error bars and perform significance analysis to automatically get a graph (bar or box plot) with error bars and significance markers. Of course, the user can also specify which groups are to be subjected to the statistical test from the beginning step by step.
1. [ Required ] Paste the file: Note: paste part only support TAB-delimited file as in the example file( Dar graph of mean) or example file( draw bar charts ). It is worth noting that the input file must require column names |
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1. [ Required ] : Note: support csv,xls,xlsx ,txt(only separated by tab),not support unicode file! example file( Dar graph of mean) or example file(draw bar charts). It is worth noting that the input file must require column names |
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2. Main parameters:
Data configuration
[ Required ] Group column name:
Bar type:
Subgroup name:
Group mapping colour:
Add err bar:
Add all Significance markers:
The p-value is replaced by an asterisk:
Statistical method
The interval between different sets of different labels:
Line size:
Textsize:
Bubble Chart
The bubble chart part gives the user a greater degree of freedom. The user can choose the data type displayed on the x/y axis and set the data column mapped into point size, point continuous/category color.
1. [ Required ] Paste the file: Note: paste part only support TAB-delimited file as in the example file. |
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1. [ Required ] : Note: support csv,xls,xlsx ,txt(only separated by tab),not support unicode file! example file. It is worth noting that the input file must require column names |
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2. Main parameters:
x-axis data column name
and x-axis data type
y-axis data column name
and y-axis data type
The final rendered point-sized column is named
The column finally rendered as point color is named
Volcano map
The volcano plot uses the difference analysis result file. The user can either specify the up-down marker as a list, or re-limit the log2FoldChange and pvalue to determine the up-down marker.
1. [ Required ] Paste the file: Note: paste part only support TAB-delimited file as in the example file. |
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1. [ Required ] : Note: support csv,xls,xlsx ,txt(only separated by tab),not support unicode file! example file. It is worth noting that the input file must require column names |
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2. Main parameters:
Use newly specified parameters to determine up-down categories :
Up and down grouping category column namesUp and down grouping category column names
Genes with a significance test p-value less than (padj)
and the absolute value of the fold change greater than (log2FoldChange)
Venn Diagram
Venn diagram parts are visualized using the ggvenn R package. The adjustment of the picture in the fine adjustment part is overlay, pay attention to keep the modification.
1. [ Required ] Paste the file: Note: paste part only support TAB-delimited file as in the example file. |
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1. [ Required ] : Note: support csv,xls,xlsx ,txt(only separated by tab),not support unicode file! example file. It is worth noting that the input file must require column names |
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2. Main parameters:
Configuration
[ Required ] Group column name:
Show elements:
. Elements sep:
Show percentage:
. Persentage text digits:
garden edge color:
alpha:
size:
linetype:
Set name color:
size:
text color:
size:
Heatmap Drawing
Considering the complexity of heatmap debugging, the visual omics tool provides two drawing methods, one is based on the pheatmap package, and the other is based on the ggplot2 package. The former is simple and beautiful, and the latter can be finely adjusted.
1. [ Required ] Upload the file for gene epression information: Note: paste part only support TAB-delimited file as in the example file. |
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2. [ Optional ] Upload the file for group information: Note: paste part only support TAB-delimited file as in the example file. |
1. [ Required ] Upload the file for gene epression information: Note: only supports csv, xls, xlsx, txt ( TAB-delimited ) as in the example file, and does not support unicode type files. |
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2. [ Optional ] Upload the file for group information: Note: only supports csv, xls, xlsx, txt ( TAB-delimited ) as in the example file, and does not support unicode type files. |
3. Main parameters:
Choose different plot methods:
Add sample group:
Data transform:
Add hclust on sample and gene:
Hclust two important parameter:
Show group legend:
Show number:
Picture title:
Text angle:
TextSize:
Err report
input expression file : expression data
group file : group data