This link consists of 10 anonymized non-small cell lung cancer (NSCLC) field of Views (FoVs) to test Mistic.

Mistic

Understanding the complex ecology of a tumor tissue and the spatio-temporal relationships between its cellular and microenvironment components is becoming a key component of translational research, especially in immune-oncology. The generation and analysis of multiplexed images from patient samples is of paramount importance to facilitate this understanding. In this work, we present Mistic, an open-source multiplexed image t-SNE viewer that enables the simultaneous viewing of multiple 2D images rendered using multiple layout options to provide an overall visual preview of the entire dataset. In particular, the positions of the images can be taken from t-SNE or UMAP coordinates. This grouped view of all the images further aids an exploratory understanding of the specific expression pattern of a given biomarker or collection of biomarkers across all images, helps to identify images expressing a particular phenotype or to select images for subsequent downstream analysis. Currently there is no freely available tool to generate such image t-SNEs.

Links

Mistic code

Mistic documentation

Paper

47,294 highly cited papers in the Essential Science Indicators (ESI) were used to test the proposed visualization approach for science mapping.

In our research paper, the highly cited papers were visualized using three network embedding models plus the t-SNE dimensionality reduction technique. Besides, three base maps were created with the same dataset for evaluation purposes. All base maps used the classic OpenOrd method with different edge cutting strategies and parameters.

We are publishing the dataset to allow other researchers to create a roughly equivalent experiment based on the highly-cited papers.

The download date is 2018 March. File "RF201803_TopPaper_FULL_links.gexf" is the Gephi network file created with 47,294 highly cited paper and all 3.6 million co-citation relationships. File "RF201803_TopPaper_top15_Edges.net" is the network file with the top 15 highest weight edges per node relationships. File "RF201803_TopPaper_UT_FIELD.csv" contains all paper's UT, Field, Publish Year, Cites data.

The data downloaded from the Essential Science Indicator web site: https://esi.clarivate.com/

The timely and accurate diagnosis of severe faults in the high-speed train air compressor is crucial due to the potential for significant safety issues. In response to this problem