Metadata of DSID000001

DeepSpace IDDSID000001
Sample IDGSM4565823
Series IDGSE144239
PlatformVisium V1
Organismhuman
Organ skin of body
Organ Detailed skin epidermis
Condition cancer
Condition Detailed squamous cell carcinoma
Sex--
Age or Stage--
Post Mortem--
Ethnicity or Strain--
Descriptioncutaneous squamous cell carcinoma; same patient as sample GSM4565824
SourceGEO
Source URL GSE144239
PMID URL: 32579974
Date Published:2020-06-22
Sample image

Location of the sample in the database

Embedding of all samples in DeepSpaceDB. The current sample is indicated as the point with a larger size. Select a spot to see it's preview information:

Information about selected sample:

Quality of this sample

Inspect various quality measures of this sample.

Ploting the spatial pattern of quality measures of this sample

Select quality measure

Plotting the average quality of this sample compared to that of other samples in DeepSpaceDB.















Plotting the quality of spots in this sample compared to all spots in DeepSpaceDB. Because of the large number of spots in DeepSpaceDB, plots take a few seconds to generate.















Image annotation

Annotation by a human expert is on the right:

Spot clustering

Visualization of spot clustering within a 2D embedding and inside the tissue. Colors represent clusters of this sample only, clusters found in the entire database, or BASS/BayesSpace domain clusters.

Choose clustering method:

Choose which clusters to display:

Clusters inside 2D embedding

Clusters inside tissue section

Spatially variable genes

singleCellHaystack was used to predict genes with differential expression patterns within the tissue space. Check our FAQ for details on gene expression units.

Spatially variable pathways

We estimated the activity of biological processes in each spot based on the expression of genes known to be involved in each process. singleCellHaystack was used to predict pathways with significant non-random patterns within the tissue. Check our FAQ for details on pathway activity units.

Cell type compositions

We predicted the cell type composition of each spot using the RCTD method. For each spatial sample, we selected a suitable reference single-cell RNA-seq data to use for this prediction. The plots below show the predicted fraction of each cell type in each spot, as well as the cell type with the highest predicted fraction in each spot.

Select cell type

Predicted cell type fractions

Top predicted cell type

Average cell composition

Tissue Explorer

Select spots of interest

Current selections

Select a set:


Number of spots in Set 1: 0

Number of spots in Set 2: 0

Select clusters:

Table expression in selections

Cell-cell Сommunication

Cell-cell communication analysis results from CellChat.

Download CellChat Results (CellChat.csv)