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ibiomic

Retrieve Maximum from Big Bio Data 

Analysis Packages

Small dataset 

<10 samples, 1-2 groups of samples

  • sample-to-sample comparisons 

  • 2 groups of samples: differential expression analysis

  • gene ontology for the obtained gene lists

  • visualization and reporting: sample-to-sample scatterplots, similarity indexes, volcano plots, box plots/violin plots for important groups of genes

 

Medium dataset

10-25 samples, 3-5 groups of samples

  • differential expression analysis between sample groups

  • gene ontology for the obtained gene lists

  • quality control by calculating similarity index

  • visualization and reporting: volcano plots, box plots/violin plots for important groups of genes, sample-to-sample scatterplots, tables of significant gene lists and corresponding gene ontology terms, similarity index tables,  samples-outliers are emphasized in separate plots if found.

 

Big dataset

>25 samples, >5 groups of samples

  • differential expression genes between groups of samples

  • gene ontology for the obtained gene lists

  • representation of samples in low-dimensional space

  • visualization and reporting: tables of significant gene lists and corresponding gene ontology terms, box plots/violin plots for important groups of genes, reduced-dimensionality scatterplots with samples represented as points,  volcano plots provided only if needed. 

 

Single Cell RNA

individual cell analysis, >1,000 cells

  • cells clustering and identification

  • differential expression

  • gene ontology

  • visualization and reporting: tables of significant gene lists, tables of gene clusters and cells, heatmaps, box plots/violin plots for important groups of genes, reduced-dimensionality scatterplots (tSNE).

 

Cohorts investigation

>90 samples, >2-3 groups of samples

  • multivariate biomarker search: in some cases a few genes or proteins are enough to clearly distinguish between groups of samples. In case of disease vs control group this indicates availability of diagnostic biomarker for the disease. This analysis is recommended for human blood proteomics.

  • visualization and reporting: tables of most indicative genes/proteins with formulas/rules how biomarker is created, box plots/violin plots for obtained multivariate biomarker(s), basic analysis of gene/protein list.

 

Time-series

from clinical or laboratory experiment 

  • clusters of highly correlated genes

  • gene ontology for the obtained gene lists

  • differential expression and similarity index are provided if reasonable for specific dataset

  • representation of samples in low-dimensional scatterplots with trajectories

  • visualization and reporting: tables of significant gene lists and corresponding gene ontology terms, time-series plots, box plots/violin plots for important groups of genes, gene-to-gene scatterplots with samples represented as points, with and without trajectories.

 

 

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