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https://bioconductor.org/packages/release/bioc/html/DESeq2.html
Software package for differential gene expression analysis based on the negative binomial distribution. Used for analyzing RNA-seq data for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates.
Proper citation: DESeq2 (RRID:SCR_015687) Copy
https://www.rdocumentation.org/packages/DGCA/versions/1.0.2
Software R package to perform differential gene correlation analysis. Performs differential correlation analysis on input matrices, with multiple conditions specified by design matrix.
Proper citation: Differential Gene Correlation Analysis (RRID:SCR_020964) Copy
https://github.com/KrishnaswamyLab/MAGIC
Software tool for imputing missing values restoring structure of large biological datasets.Method that shares information across similar cells, via data diffusion, to denoise cell count matrix and fill in missing transcripts.
Proper citation: Markov Affinity based Graph Imputation of Cells (RRID:SCR_022371) Copy
https://www.miti-consortium.org/
Consortium provides guidelines for highly multiplexed tissue images. Standard that applies best practices developed for genomics and other microscopy data to highly multiplexed tissue images and traditional histology. Data and metadata standards consistent with Findable, Accessible, Interoperable, and Reusable (FAIR) standards that guide data deposition, curation and release.
Proper citation: Minimum Information about Tissue Imaging (RRID:SCR_022830) Copy
https://github.com/greenelab/miQC
Software tool as flexible, probablistic metrics for quality control of scRNA-seq data. Adaptive probabilistic framework for quality control of single-cell RNA-sequencing data. Data driven QC metric that jointly models proportion of reads mapping to mtDNA and number of detected genes with mixture models in probabilistic framework to predict which cells are low quality in given dataset.
Proper citation: miQC (RRID:SCR_022697) Copy
http://cahub.cancer.gov/about/
THIS RESOURCE IS NO LONGER IN SERVICE. Documented July 5, 2018. A national center for biospecimen science and standards to advance cancer research and treatment. It was created in response to the critical and growing need for high-quality, well-documented biospecimens for cancer research. The initiative builds on resources already developed by the NCI, including the Biospecimen Research Network and the NCI Best Practices for Biospecimen Resources, both of which were developed to address challenges around standardization of the collection and dissemination of quality biospecimens. caHUB will develop the infrastructure for collaborative biospecimen research and the production of evidence-based biospecimen standard operating procedures.
Proper citation: caHUB (RRID:SCR_009657) Copy
https://geodacenter.github.io/
Software program for spatial analysis for non geographic information systems specialists. Includes functionality ranging from simple mapping to exploratory data analysis, visualization of global and local spatial autocorrelation, and spatial regression.
Proper citation: GeoDa (RRID:SCR_018559) Copy
https://delaney.shinyapps.io/FairSubset/
Web tool to choose representative subsets of data for use with replicates or groups of different sample sizes. Used to retain distribution information at single datum level and may be considered for standardized use in fair publishing practices.
Proper citation: FairSubset (RRID:SCR_019102) Copy
https://www.bioconductor.org/packages/release/bioc/html/GSVA.html
Open source software R package for assaying variation of gene set enrichment over sample population.Used for microarray and RNA-seq data analysis. Gene set enrichment method that estimates variation of pathway activity over sample population in unsupervised manner.
Proper citation: GSVA (RRID:SCR_021058) Copy
https://cran.rstudio.com/web/packages/accucor/index.html
Software as isotope natural abundance correction algorithm that is needed especially for high resolution mass spectrometers. Natural abundance correction of mass spectrometer data.
Proper citation: AccuCor (RRID:SCR_023046) Copy
http://neurosurgery.ucsf.edu/index.php/research_tissue_bank.html
THIS RESOURCE IS NO LONGER IN SERVICE. Documented on May 4th,2023. Brain Tumor Research Center Tissue Bank began collecting tissue in 1978 and has established an organized repository of characterized tissues--frozen, paraffin-embedded, blood and cultures--that are maintained in a manner useful for a wide range of studies. Samples are collected only from patients who have agreed to have their tissues banked and used for future research. Consent documents are maintained in a secure area and associated clinical data are held in a double-password protected computer database. Each sample received into the Tissue Bank is non-identifying number. No protected health information (PHI) is released. To obtain samples, investigators submit a request form to the Manager. The request form requires an explanation of the tissue requested (type, number of samples, justification), description of the study, CHR approval (see new policy regarding human vs. non-human research) and Project Leader authorization. The Manager reviews each request for feasibility before presentation to the Scientific Core Committee. The UCSF Neurosurgery Tissue Bank makes its inventory of stock cell lines available to all investigators. Requested cells are grown in T-25 flasks and shipped FedEx Priority Overnight at the receipient's expense. However, if you prefer, we can ship the frozen cells, packed in dry ice. (Note: some countries restrict dry ice shipments.)
Proper citation: UCSF Brain Tumor Tissue Bank (RRID:SCR_000647) Copy
Database to find literature, established protocols, and best practices for collecting, storing, and handling biological samples. Its main purpose is to help researchers improve the quality and reproducibility of biospecimen-based medical and genetic research. Provides a library of validated protocols that laboratories use to properly process human biospecimens (like blood or tissue) without degrading their molecular integrity. Offers procedural guidelines (BEBPs) backed by scientific literature to protect samples from pre-analytical variables (e.g., storage temperature, time-to-freezing). Catalogs thousands of peer-reviewed articles focused on biospecimen science.
Proper citation: Biospecimen Research Database (RRID:SCR_001944) Copy
https://github.com/labsyspharm/cylinter
Open source software tool as interactive image segmentation filter for multiplex microscopy that aids in identification and removal of cell segmentation instances corrupted by optical and image processing artifacts.
Proper citation: CyLinter (RRID:SCR_021157) Copy
Software R package for processing and analyzing single-cell ATAC-seq data. Used for integrative single cell chromatin accessibility analysis.Provides intuitive, user focused interface for complex single cell analysis, including doublet removal, single cell clustering and cell type identification, unified peak set generation, cellular trajectory identification, DNA element-to-gene linkage, transcription factor footprinting, mRNA expression level prediction from chromatin accessibility and multi-omic integration with single-cell RNA sequencing.
Proper citation: ArchR (RRID:SCR_020982) Copy
Open source software tool as multiple choice microscopy pipeline for multiplexed whole slide imaging and tissue microarrays. Scalable, modular image processing pipeline for multiplexed tissue imaging. Used for performing sequential steps needed to transform large, multi channel whole slide images into single cell data.
Proper citation: MCMICRO (RRID:SCR_021048) Copy
https://github.com/JamieHeather/stitchr
Software Python tool for stitching coding T cell receptors nucleotide sequences from V,J,CDR3 information. Produces complete coding sequences representing fully spliced TCR cDNA given minimal V,J,CDR3 information.
Proper citation: Stitchr (RRID:SCR_022139) Copy
https://maayanlab.cloud/chea3/
Web based transcription factor enrichment analysis. Web server ranks TFs associated with user-submitted gene sets. ChEA3 background database contains collection of gene set libraries generated from multiple sources including TF-gene co-expression from RNA-seq studies, TF-target associations from ChIP-seq experiments, and TF-gene co-occurrence computed from crowd-submitted gene lists. Enrichment results from these distinct sources are integrated to generate composite rank that improves prediction of correct upstream TF compared to ranks produced by individual libraries.
Proper citation: ChIP-X Enrichment Analysis 3 (RRID:SCR_023159) Copy
https://kleintools.hms.harvard.edu/tools/spring.html
Interactive web tool to visualize single cell data using force directed graph layouts. Kinetic interface for visualizing high dimensional single cell expression data. Collection of pre-processing scripts and web browser based tool for visualizing and interacting with high dimensional data.
Proper citation: SPRING (RRID:SCR_023578) Copy
Web app that allows users to search for the most important paths connecting any two nodes in Hetionet.
Proper citation: Hetnet Connectivity Search (RRID:SCR_023630) Copy
Web server application that infers overrepresentation of upstream kinases whose putative substrates are in user inputted list of proteins. Used to analyze data from phosphoproteomics and proteomics studies to predict upstream kinases responsible for observed differential phosphorylations.
Proper citation: Kinase Enrichment Analysis 3 (RRID:SCR_023623) Copy
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