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ArrayGen introduces its new service for the custom script development throughout globe. Programming can be done using Perl/Python/Java. Anyone interested to develop tools /software/databases/any publication report generation and if needed programming skilled support we request you to contact us at email info@arraygen.com or call us on mobile +91-9673625446.
Also, we have started depositing some free scripts for public use and we will be adding more scripts soon. Hope the ready to use script will help all researchers or any life science professional to use in there research work.
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Heatmap
The heatmap may also be combined with clustering methods which group genes and/or samples together based on the similarity of their gene expression pattern.
This can be useful for identifying genes that are commonly regulated, or biological signatures associated with a particular condition.
For heatmap you need pandas modules such as seaborn, pandas, numpy sys, matplotlib.To get Heatmap use bellow commands:
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Program: python3 Heatmap_maker.cpython-36.pyc
Usage: python3 Heatmap_maker.cpython-36.pyc < command > [options]
Commands:
| --i | Compulsory. This must have option of input file path with name (file data must be tab separated) |
| --sep | Optional. file data separator default set to "\t" |
| --fc | Compulsory. Column number of FoldChange |
| --pval | Compulsory. Column number of PValue |
| --sig | Optional. Default Significant value set to < 0.05 |
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| --annot | Optional. Default value 0(No need of annotation). Must be 0 or 1(Annotation for points) |
| --geneid | Optional. Gene Id col number. If -annot is set to 1 then its compulsory* have gene id col number |
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| --c1 | Optional. Color 1 in Colorbar. |
| --c2 | Optional. Color 2 in Colorbar. |
| --c3 | Optional. Color 3 in Colorbar. |
Ex :
python3 Heatmap_maker.cpython-36.pyc --i "input/Combined_DEG1.csv" --fc [5,11] --pval [7,13] --sep "\t"
python3 Heatmap_maker.cpython-36.pyc --i "input/Combined_DEG1.csv" --fc [5,11] --pval [7,13] --sep "," --c1 green --c2 white --c3 red
python3 Heatmap_maker.cpython-36.pyc --i "input/demo.csv" --fc [2,3,4,5,6,7,8,9] --pval [10] --sep "," --annot 1 --geneid 1 --c1 red --c2 "#fffffa" --c3 green
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 NG17 Vs NG181.png)
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Volcano Plot
A volcano plot is a type of scatterplot that shows statistical significance (P value) versus magnitude of change (fold change).
In a volcano plot, the most upregulated genes are towards the right, the most downregulated genes are towards the left, and the most statistically significant genes are towards the top.
For volcano plot you need pandas modules such as seaborn, pandas, numpy sys, matplotlib.To get volcano plot use bellow commands:
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Program: python3 volcano_plot_maker.cpython-36.pyc
Usage: python3 volcano_plot_maker.cpython-36.pyc < command > [options]
Commands:
| --i | Compulsory. This must have option of input file path with name ( file data must be tab separated) |
| --sep | Optional. file data separator default set to "\t" |
| --fc | Compulsory. Column number of FoldChange |
| --pval | Compulsory. Column number of PValue |
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| --upreg | Optional. Default UpRegulated cut off set to > 0.8 |
| --downreg | Optional. Default DownRegulated cut off set to < -0.8 |
| --sig | Optional. Default Significant value set to < 0.05 |
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| --annot | Optional. Default value 0(No need of annotation). Must be 0 or 1(Annotation for points) |
| --geneid | Optional. Gene Id col number. If -annot is set to 1 then its compulsory* have gene id col number |
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| --annot_upreg | Optional. Default display annotation UpRegulated cut off set to > 3.5 |
| --annot_downpreg | Optional. Default display annotation UpRegulated cut off set to < -3.5 |
| --annot_sig_from | Optional. Default display annotation significant cut off set to > -log10(sig) |
| --annot_sig_to | Optional. Default display annotation significant cut off set to < 5 |
Ex :
python3 volcano_plot_maker.cpython-36.pyc --i "input/DEG_Case_Vs_Control.csv" --fc 5 --pval 7
python3 volcano_plot_maker.cpython-36.pyc --i "input/DEG_Case_Vs_Control.csv" --fc 5 --pval 7 --sep "," --annot 1 --geneid 1 --annot_upreg 3 --annot_downpreg -4 --annot_sig_to 4.5
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FastQC Report Generation
The output from FastQC, after analyzing a FASTQ file of sequence reads, is an html file. Our program will help you to combine all fastqc results in one excel file whcih make all things more reliable to work with it
For fastqc report you need pandas modules such as zip, bs4, xlsxwriter.
To get fastqc report use bellow steps / Read README file which is given in downloaded zip:
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Step 1 : Edit config file which is given in downloaded zip file
Config file column explaination given below :
| path of folder | We needs to pass folder path where fastq files present |
| f1 | First file name for pair end data / single end |
| f2 | [optional] : Second file name for pair end data |
| trim f1 | [optional] : First trimmed file name for pair end data / single end/td> |
| trim f2 | [optional] : Second trimmed file name for pair end data |
Step 2 : Execute Multiple_Fastqc_Report_Maker.cpython-36.pyc file using below command
python3 Multiple_Fastqc_Report_Maker.cpython-36.pyc
Check your output in respective input folders
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