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Data Preparation

Data preparation is about constructing a dataset from one or more data sources to be used for exploration and modeling. It is a solid practice to start with an initial dataset to get familiar with the data, to discover first insights into the data and have a good understanding of any possible data quality issues. Data preparation is often a time consuming process and heavily prone to errors. The old saying "garbage-in-garbage-out" is particularly applicable to those data science projects where data gathered with many invalid, out-of-range and missing values. Analyzing data that has not been carefully screened for such problems can produce highly misleading results. Then, the success of data science projects heavily depends on the quality of the prepared data.
Multi-omics Data Preparation
Multi-omics techniques (e.g., RNA-Seq) have a wide variety of applications, but no single analysis pipeline can be used in all cases. The following pipeline is a review all of the major steps in RNA-Seq data analysis, including experimental design, quality control, read alignment, quantification of gene and transcript levels, visualization, differential gene expression, alternative splicing, functional analysis and more. Our focus will only be on the "Core-analysis" step.

A generic roadmap for RNA-Seq computational analyses


Public Multi-omics Data Sources:
GEO : GEO is a public functional genomics data repository supporting MIAME-compliant data submissions. Array- and sequence-based data are accepted. Tools are provided to help users query and download experiments and curated gene expression profiles.
ArrayExpress : ArrayExpress Archive of Functional Genomics Data stores data from high-throughput functional genomics experiments, and provides these data for reuse to the research community.
TCGA : The Cancer Genome Atlas (TCGA), a landmark cancer genomics program, molecularly characterized over 20,000 primary cancer and matched normal samples spanning 33 cancer types. This joint effort between NCI and the National Human Genome Research Institute began in 2006, bringing together researchers from diverse disciplines and multiple institutions.
ADDI : The Alzheimer’s Disease Data Initiative (ADDI) is on a mission to fundamentally transform Alzheimer’s disease (AD) research. Through a data sharing platform, data science tools, funding opportunities, and global collaborations, ADDI is advancing scientific breakthroughs and accelerating progress towards new treatments and cures for AD and related dementias.
ADNI : Alzheimer's Disease Neuroimaging Initiative is a multisite study that aims to improve clinical trials for the prevention and treatment of Alzheimer’s disease.
DepMap : The goal of the Dependency Map (DepMap) portal is to empower the research community to make discoveries related to cancer vulnerabilities by providing open access to key cancer dependencies analytical and visualization tools.
PPMI : PPMI is a landmark study collaborating with partners around the world to create a robust open-access data set and biosample library to speed scientific breakthroughs and new treatments.