The home page provides a global view of body maps of human and mouse to quickly explore cell markers by clicking on hyperlinks embedded in the web images - 'Anatomical location of human or mouse cells' (1). The mouse body map can be switched by clicking the icon at the top right (2). The body maps can facilitate quick browse of cell markers for the listed cell types and more detailed cell types can be shown by clicking the tissue icons (3). When click a cell type, it will jump to the corresponding search result of the cell markers related to the cell type (4). Click through to use the six analysis tools in Cell Tools (5).

The right is a quick search box. Users can search cell markers by inputting cell name, cell marker name, or tissue name of interest.

Users can query the database through three search panels: Cell Search, Marker Search and Quick search.
Cell search help
Users can search for cell markers of cells of interest by selecting species from the pull-down menu, tissue type from the hierarchical tree of tissues and cell type from the hierarchical trees of cell types (1-3). When input key words into corresponding search box, the candidate tissues or cells stored in the database will be listed for further selection. In particular, cell markers associated with cancers in the corresponding tissue will be returned if choosing the 'Cancer' box (4).
Here, we take 'Adipose-derived stem cell' as an example to explain how to search cell markers in a particular cell. First, choose 'Human' in the 'Species type' search box (1). Next, choose the 'Adipose tissue' in the 'Tissue type' box (2) and then input or choose 'Adipose-derived stem cell' in the 'Cell type' box (3). After clicking on the 'Submit' button, the Result page is displayed.

Result help
The result page contains an intuitive statistical graph of cell marker prevalence (1), which is quantified by the number of supportive evidence (i.e., publications) and an integrative cell marker list for the queried cell type (2). The detail data can be downloaded by the download icon. Also, the Result page displays four detailed tables of cell marker entries derived from different sources including single-cell RNA sequencing, experiment, review and company. Each entry contains Species, Tissue, Cell Type, Cancer, Cell Marker, Source and Supported Sources (3). By clicking on the hyperlink 'more details' for an individual entry in these tables, users can obtain more detailed information including gene symbols, gene ids, gene names, protein ids, publication information (i.e., title, PubMed ID, journal and publication year) and the cross references to external databases (4). In addition, CellMarker2.0 provides several features for browsing the query results. Users can filter entries by typing terms in the 'Search' box at the top right of the table, sort columns in an ascending (or a descending) fashion by clicking the arrows in the column headers and select table size per page by using the 'Show entries' pull-down menu on the top left side above the table.

Marker search help
Users can search any genes of interest by entering gene symbols, gene ids, gene names or protein ids to query in which cell types of which tissues the specific gene can act as cell marker (1). After clicking the 'Submit' button, the search engine will return an interactive bubble chart and a table showing comprehensive information of cell markers (2-3). The interactive bubble chart exhibits how often the gene of interest is used as a cell marker in different cells of different tissues.

Quick search help
Users can also use “Quick search” in the “Search” section.
Details help
Details interface provides detailed information on each deposited entry of cell markers, which contains comprehensive information of each single record in the entry.

The browse page presents a hierarchical classification of cells and tissues. Users can browse cell markers by clicking cell types in different tissues of human (or mouse), and the complete list of matched cell marker entries can be returned (1-4). For example, to browse the entries related to Adipose tissue in human, you can click 'Human', choose the 'Adipose tissue' (2) and find the interested cell type, such as 'Adipose-derived stem cell' (3). The related cell markers will be shown on the right panel, including a statistical graph of the cell markers for the cell type and the entries from different sources (4).

Users can also browse all entries associated with an interested cell type by directly selecting the corresponding cell (1-3).

Cell marker entries derived from Sequencing technology are listed separately on the bottom of the browse list.

To download data in the database, select the menu 'Download'. CellMarker2.0 database provides downloadable file in TXT format. Users can download all cell marker data sets, which include four files: 1) all cell markers of different cell types from different tissues in human and mouse, 2) cell markers in human, 3) cell markers in mouse, and 4) Cell marker from all sequencing technologies researches.

Users can perform single-cell analysis in CellMarker2.0 by uploading data using cell annotation, cell clustering, cell differentiation,cell malignancy, cell feature and cell communication.
Cell Annotation help
For the clustered cells, we provide cell annotation function in cellmarker2.0.
1.Click to select species and tissue type.
2.Enter genes in the query box or selection files to match marker information.
3.Click on the submit.
4.The heatmap shows that different each cell type’s matching degree through highlight.

Cell Clustering help
For quality-controlled single-cell data we provide cell clustering analysis based on SeuratV3.0.
1.Click 'load' to load the built-in data of cellmarker 2.0 for analysis.
2.Click 'Upload' to upload the single cell data after quality control.
3.Input the number of principal components shown in the elbow plot.
4.Input the dimensions of reduction.
5.Input the value of the resolution parameter that determines the number of communities.
6.Select the method of dimensionality reduction clustering (TSNE or UMAP)
7.All the plots will be shown here.
8.Select clusters for differential expression analysis.
9.Parameter settings for the logfc.threshold, test.use and min.pct.
10.List of differential-expressed genes.
11.Click to select species.
12.Click to select the gene name type for the data.
13.Click to select the enriched GO classification (BP, CC, MF or ALL) and click to input threshold for pvalueCutoff and qvalueCutoff.
14.List of Functional enrichment list.
15.Users can input a value summarized by GO function results and add it by turns in the annotation box.

Cell feature help
The cell feature can display the expression of the interesting gene in different cell clusters.
1.Click 'load' to load the built-in data of cellmarker 2.0 for analysis.
2.Click 'Upload' to upload the single cell data after quality control.
3.Input the number of principal components shown in the elbow plot.
4.Input the dimensions of reduction.
5.Input the value of the resolution parameter that determines the number of communities.
6.Select the method of dimensionality reduction clustering (TSNE or UMAP).
7.Plots will be shown here.
8.Input the interesting gene.
9.Select clusters for differential expression analysis.
10.Parameter settings for the logfc.threshold, test.use and min.pct.
11.List of differential-expressed genes.

Cell malignancy help
For single cell data completed with cell annotation, we provide identification of malignant cells base on infCNV.
1.Click 'load' to load the built-in data of cellmarker 2.0 for analysis.
2.Click 'Upload' to upload the three files required for infCNV including the expression matrix file, cell annotation file and gene locus data.
3.Select the cell type for reference.
4.Click ‘Cut Off’ to select the cut Off value, cutoff=1 works well for Smart-seq2, and cutoff=0.1 works well for 10x Genomics.
5.Click ‘Get Files’ to download the metadata files.
6.Select the method of dimensionality reduction clustering (TSNE or UMAP), users can see the distribution of normal and malignant cells.
7.Plots will be shown here.

Cell differentiation help
For quality-controlled single-cell data we provide Cell differentiation based on Monocle 2.
1.Click 'load' to load the built-in data of cellmarker 2.0 for analysis.
2.Click 'Upload' to upload the single cell data after quality control.
3.Select the method of dimensionality reduction clustering (TSNE or UMAP).
4.Click to select the coloring method based on time or cluster.
5.Click to select the root nodes.
6.All the plots will be shown here
7.Input the interesting gene to show changes in its expression over time.

Cell communication help
For single cell data completed with cell annotation, we provide cell communication analysis base on cellphonedb.
1.Click 'load' to load the built-in data of cellmarker 2.0 for analysis.
2.Click 'Upload' to upload the two files required for cellphonedb including the expression matrix file and cell annotation file.
3.Click ’run’ to run.
4.Click ‘Get Files’ to download the metadata files.
5.Click to select the ploting method including dotplot or heatmap.
6.Click to select the color to plot.