OmicsAnalyst

Omics layer	Provide as	Uploaded
Metadata sample groups & covariates — required for data tables · example	Upload	uploaded— not added
Genes / mRNAs example: table · list	Table List	table— not added
Proteins example: table · list	Table List	table— not added
miRNAs example: table · list	Table List	table— not added
Metabolites example: table · list	Table List	table— not added
Microbiome	Table List	table— not added

Example Datasets

Combine data tables with feature lists — the hypothesis-driven mixed case. The table(s) provide data-driven signal; the list is bridged onto a table's layer through interaction databases. Loading opens the mixed-input harmonization page; Proceed there opens the Analysis Overview, with the list as the hypothesis to test on the table(s).

Example	Omics layers	What it demonstrates
One table + one list	Transcriptomics table + miRNA list	Hypothesis-driven. TCGA glioblastoma transcriptomics as a data table plus a brain-cancer miRNA signature as a feature list (same study, human). Bridges the miRNAs onto the table's genes (predicted targets) in entity space.
Two tables + one list	Transcriptomics + miRNA tables + gene list	Multi-table. TCGA transcriptomics + miRNA as two data tables plus a gene (Entrez) feature list. Harmonization lets you pick which table the list bridges onto; Proceed opens the Analysis Overview with both tables.

Confirmation

Are you sure about resetting? All currently uploaded lists will be lost!

Upload Genes/mRNAs Data Table

Upload Proteins Data Table

Upload miRNAs Data Table

Upload Metabolites Data Table

Upload Microbiome Data Table

Upload Omics Data

Upload your Metadata File

Metadata file

Choose

Notes:

Metadata table should be in .txt or .csv format with samples in rows and factors in columns. The primary factor should be in the first column
Samples IDs in each omics table should match the sample names in the metadata table.
Please check your data for missing values, no missing values for the primary study factor (the first column).

Clinical/phenotype layer

Specify Organism

Upload a list of mRNAs

Upload a list of proteins

Upload a list of miRNAs

Upload a list of metabolites

Upload a list of taxon names

You can copy and paste your list of significant taxon names with optional associated abundance separated by tab. A small example data is shown below (first column: Genus name, second column: abundance):

Parabacteroides	47376.29
Roseburia	44882.80
Ruminococcus	1384440.62
Staphylococcus	6372.25
Streptococcus	15792.09
Sutterella	31861.25
unclassified_Clostridiales	9973.95
unclassified_Erysipelotrichaceae	399789.43
unclassified_Lachnospiraceae	674350.30
unclassified_Pseudomonadaceae	1939.38

Notes:

For taxon names, if there is a space (" ") in the name (i.e. unclassified Clostridiales) replace space with "_" (unclassified_Clostridiales).
If there are unclassified taxa in your data of the query taxonomy level, you can use the term “unclassified_” plus the parent taxonomy for estimation. e.g.: f__Lachnospiraceae; g__; will be “unclassified_Lachnospiraceae” in the input box.

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Diabetes [2]	Human multi-omics data from islet tissue (RNA-seq，proteomics): no diabetes (None), Prediabetes (PreT2D), and type 2 diabetes (T2D). See Ewald, J. et al. for more details.	Metadata RNA-seq Proteomics
Diabetes [2]	Human multi-omics data from islet tissue (lipidomics, RNA-seq): no diabetes (ND), impaired glucose tolerance (IGT), type 3c diabetes (T3D), and type 2 diabetes (T2D). See Wigger, L. et al. for more details.	Metadata RNA-seq Lipidomics
COVID-19 severity [3]	Human leukocyte and plasma multi-omics (transcriptomics, proteomics, metabolomics) from 125 patients, for classifying COVID-19 status and severity (ICU, mechanical ventilation) from Overmyer et al., Cell Systems 2021.	Metadata Transcriptomics Proteomics Metabolomics
Human pregnancy [2]	Human multi-omics data (proteomics, metabolomics) on modeling the chronology of these adaptations during full-term pregnancy. Multi-omics of pregnancy	Metadata Proteomics Metabolomics
Immune cells[3]	Mouse multi-omics data (transcriptomics, metabolomics, miRNA) on the effect of Ikaros transcription factor on B-cell differentiation from STATegRA	Metadata Transcriptomics Metabolomics miRNA
Brain cancer [2]	Human multi-omics data (transcriptomics, miRNA) on glioblastoma multiforme of four different subtypes from TCGA.	Meta-data Transcriptomics miRNA
Diabetes, one table [1]	Only the RNA-seq table of the islet study above, with age, BMI and HbA1c in the metadata. Shows what one table offers: the numeric metadata as a second layer, regulator (TF and miRNA) activity, and partners in other layers from the databases.	Metadata RNA-seq

Single gene list	Genes	Hypothesis-driven. 50 differentially expressed genes (Entrez IDs). Test which pathways are enriched and look up interacting partners in other omics from curated interaction databases.
Single metabolite list	Metabolites	Hypothesis-driven. A metabolite list (common names). Map to metabolic pathways and predict the genes/proteins (enzymes) that act on these metabolites.
Two lists	Genes + miRNA	Gene and miRNA lists from a brain cancer study (50 genes + 30 miRNAs). Multi-omics network integration with regulatory interactions.
Three lists	Genes + Proteins + Metabolites	Genes, proteins, and metabolites (50 + 28 + 28). Complete multi-layer network analysis.
Diet & Nutrient Sensing (3D)	Genes + Proteins + Metabolites	Foodomics example on lipid metabolism, gut hormones and dietary bioactives (50 genes + 25 proteins + 20 metabolites). Builds PPI and metabolite-protein networks; proceeds to 3D.

Omics layers

Notes: