LIPID PROFILING ANALYSIS
The raw data obtained by lipidomics mapping is multi-dimensional and complex. In order to facilitate the interpretation of your data, we have provided standardized bio-information analysis services, and can also provide customized high-level analysis services according to your needs.
A large-scale comprehensive analysis was performed for each test sample. It includes statistics on the total expression of total lipidomics in each sample, the number of lipids expressed, the distribution of lipid expression in the experimental and control groups, the expression of lipid classes, and the composition of lipid classes, etc., revealing the lipid expression between samples. , the difference in lipid categories.
ØPerformance difference lipid analysis
Calculate the difference in the amount of expression between the whole lipidomics and the control group and the control group, thereby quantifying the difference in the lipid expression between the experimental group and the control group, and then based on the two-sample median difference test (Wilcoxon signed-rank) The p value of test) screens for lipids that show significant differences. The screening conditions were those in which the difference in the amount of expression was changed by a factor of 2 (the difference multiplier was increased by 2 times or decreased by 2 times) and the p value was less than 0.05, which was regarded as a significantly different expression of the lipid.
ØLipid class enrichment analysis
Lipids can be subdivided into more than 20 lipid classes based on differences in functionalities on the chemical structure. We will target lipids with significant differences, with Fisher's assay and the set p-value to determine the significant differences in which lipid classes are present in the lipid class.
We have now established a "lipid biological interpretation platform" that integrates sequencing and mass spectrometry data technology across the body, linking the products of lipidomics to biological functions by category or species, and integrating various physical sciences with biological functions as a platform. Information, and then learn about transcriptome and lipidomics that affect the function of the organism. The general experimental design hopes to understand the difference between the experimental group and the control group by comparing the experimental group with the control group. Our analysis strategy is also based on this. Whether it is a transcriptome or a lipidomics, the spectral data is determined by the difference multiple and the statistical test. In a way, the genes or lipids that show significant differences between the experimental and control groups are screened, and the biological functions that affect the transcriptome and lipidomics are compared according to the lipid biological interpretation platform we have established.
Trans-omics research has been the focus of current systemic biomedical research, but lacks integrated analysis of transcriptome and liposomes. Currently we have collected multiple lipid-related databases, including KEGG (https://www.genome.jp/kegg/), GO (http://www.geneontology.org/), Lipid maps (https://www.lipidmaps.org/), Swiss lipid (http://www.swisslipids.org/), HMDB (http://www.hmdb.ca/), ChEBI (https://www.ebi.ac.uk/chebi/). Compare differences in lipid naming rules between databases; integrate information on lipid-related enzymes, genes, biological pathways, and biochemical reactions in various databases; clarify upstream and downstream relationships between transcriptome and lipidomics,and systematically resolve transcriptome and lipidomics The role played in a particular biological function.