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Tools from the CESCG groups

Tag Storm: Metadata made simple

The tag storm format offers an easy way to describe a hierarchical set of metadata for your data. The format is presented along with a suite of utilities that allow you to manipulate and interrogate your tag storm files.

The Tag Storm format and utilities were developed by Jim Kent.

Sample Psychic: See how your cells compare

Sample Psychic can take your gene expression data and compare it to curated references. This comparison is used to produce a scorecard which shows how closely your data maps up to the curated cell types.

Report Card view, showing matches grouped by sample. Has CSV download.

By-sample view. Similar to Report Card but easier to navigate.

Experimental tSNE clustering of input samples.

The Sample Psychic web application allows users to upload gene expression data and gain insights into those samples by applying suites of pre-trained concept classifiers to each sample. The resulting set of classification scores is presented as a report card that can be used to validate the identity of the samples and explore similarity between these samples and target concepts represented in in the Sample Psychic suite. For CIRM, classifiers were built for both fetal tissue clusters and hand labeled brain regions. One binary classifier is created for tissue type/cluster. A total of 96 fetal tissue clusters are represented in the Sample Psychic classifier compendium along with 30 classifiers representing hand-labeled brain regions. The pattern of individual classifier scores can give a picture of how user samples map to brain regions and to previously analyzed clusters.

Website coming soon!

Sample Psychic was created by James Durbin.

SCIMITAR: Single Cell Inference of MorphIng Trajectories and their Associated Regulation

The SCIMITAR package contains a number of tools to help scientists analyze cell trajectory maps based on single cell sequencing data. Read more:

Cordero P, Stuart JM. Tracing co-regulatory network dynamics in noisy, single-cell transcriptome trajectories . Pac Symp Biocomput. 2016;22:576-587. PMID: 27897008; PMC: PMC5203771

SCIMITAR was created Pablo Cordero.

RIGGLE: Regulator Inference by Graph-Guided LASSO Estimation

RIGGLE (Regulatory Inference by Graph-Guided LASSO Estimation) is a machine learning framework designed to discover the transcription factor regulators of a cell development trajectory by taking in expression data, known transcription factor targets, and a cell development graph.

This process produces predictions for the transcription factor's activities in each of the cell types represented in the graph based on the coordinated expression of its targets, while respecting the developmental relationships between the cells.

More coming soon!

RIGGLE was created by Dan Carlin.

MISCE: A Minimum Information About a Stem Cell Experiment

MISCE, or Minimum Information about a Stem Cell Expriment, is a spreadsheet that attempts to collect a rich set of useful information (metadata) about any experiment involving stem cells. The hope is that in using MISCE, the detailed metadata will make the experimental data useful to others attempting to use it in the future.

MISCE consists of a number of different modules with each module describing a different experimental process, e.g. celluar reprogramming or RNA sequencing, and all of the important information related to this process, e.g. "Biosample disease stage" or "Assay platform". Each module can be included or excluded based on the type of experiment being performed. For example, you might not include the "Assay_DNAMethyl" module if you are not performing DNA methylation assays as part of your experiment.

View PDF of MISCE modules

MISCE was created by Sagar Jain and Richard Scheuermann; it is currently maintained by UCSC.