Rna-seq data analysis a practical approach free download

As a consequence, statistical tests that assume normally distributed data used for example for detecting differentially expressed genes are likely to perform suboptimally on scRNA-seq data. Nevertheless, this is a highly dynamic area, where gold-standard analysis platforms are yet to emerge. Recent reports indicate that more-user-friendly, web-browser-based interfaces will become available soon [ 75 ].

However, the precise functionalities that need to be offered continue to be an area of active development. In summary, an understanding of the bioinformatic and computational issues involved in scRNA-seq studies is needed, and specialist support for biomedical researchers and clinicians from bio-informaticians who are comfortable with handling scRNA-seq datasets would be beneficial.

Before further analyses, scRNA-seq data typically require a number of bio-informatic QC checks, where poor-quality data from single cells arising as a result of many possible reasons, including poor cell viability at the time of lysis, poor mRNA recovery and low efficiency of cDNA production can be justifiably excluded from subsequent analysis.

Currently, there is no consensus on exact filtering strategies, but most widely used criteria include relative library size, number of detected genes and fraction of reads mapping to mitochondria-encoded genes or synthetic spike-in RNAs [ 76 , 77 ]. Recently, sophisticated computational tools for identifying low-quality cells have also been introduced [ 78 , 79 , 80 , 81 ].

Other considerations are whether single cells have actually been isolated or whether indeed two or more cells have been mistakenly assessed in a particular sample.

This can sometimes be assessed at the time of single-cell isolation, but, depending on the chosen technique, this might not always be possible. Once the scRNA-seq data are filtered for poor samples, they can be interpreted by an ever-increasing range of bio-informatic and computational methods, which have been reviewed extensively elsewhere [ 74 , 82 ].

The crux of the issue is how to examine tens of thousands of genes possibly being expressed in one cell, and provide a meaningful comparison to another cell expressing the same large number of genes, but in a very different manner.

Principal component analysis PCA is a mathematical algorithm that reduces the dimensionality of data, and is a basic and very useful tool for examining heterogeneity in scRNA-seq data.

This has been augmented by a number of methods involving different machine-learning algorithms, including for example t-distributed stochastic neighbour embedding t-SNE and Gaussian process latent variable modelling GPLVM , which have been reviewed in detail elsewhere [ 74 , 82 , 83 ].

Dimensionality reduction and visualization are, in many cases, followed by clustering of cells into subpopulations that represent biologically meaningful trends in the data, such as functional similarity or developmental relationship.

Owing to the high dimensionality of scRNA-seq data, clustering often requires special consideration [ 84 ], and a number of bespoke methods have been developed [ 45 , 86 , 87 ,, 85 — 88 ]. Likewise, a variety of methods exist for identifying differentially expressed genes across cell populations [ 89 ]. An increasing number of algorithms and computational approaches are being published to help researchers define the molecular relationships between single cells characterized by scRNA-seq and thus extend the insights gained by simple clustering.

These trajectory-inference methods are conceptually based on identification of intermediate cell states, and the most recent tools are able to trace both linear differentiation processes as well as multipronged fate decisions [ 22 , 91 , 92 , 93 , 94 ,, 24 , 90 — 95 ]. While these approaches currently require at least elementary programming skills, the source codes for these methods are usually freely available for bio-informaticians to download and use.

This reinforces the need to cultivate a good working relationship with bio-informaticians if scRNA-seq data are to be analysed effectively. Over the past 6 or so years, there has been an explosion of interest in using scRNA-seq to provide answers to biologically and medically related questions, both in experimental animals and in humans.

Many of the studies from this period either pioneered new wet-lab scRNA-seq protocols and methodologies or reported novel bio-informatic and computational approaches for quality-controlling and interpreting these unique datasets. Some studies also provided tantalizing glimpses of new biological phenomena that could not have been easily observed without scRNA-seq.

Here, we consider what the next 5 years might hold for scRNA-seq from the perspective of clinical and experimental researchers looking to use this technology for the first time. Given that the field of single-cell genomics is experiencing rapid growth, aside from being confident that numerous advances will be made, exactly what these will be remains difficult to predict.

Nevertheless, we point towards various areas in which we hope and expect numerous advances to be made. First, most scRNA-seq studies have tended to examine freshly isolated cells. We expect many more studies will explore cryopreserved and fixed tissue samples using scRNA-seq, which will further open up this technology to clinical studies.

As isolation of single cells is of paramount importance to this approach, we expect more advances in wet-lab procedures that rapidly dissociate tissue into individual cells without perturbing their transcriptomes. In addition, while many scRNA-seq studies have employed expensive hardware, including microfluidic and droplet-based platforms, future studies will reduce costs by further reducing reaction volumes, and perhaps also by avoiding the need for bespoke pieces of equipment [ 38 ].

Given ongoing trends for decreasing sequencing costs, we anticipate that these cost benefits will also make scRNA-seq more affordable on a per-cell basis. This will likely drive another trend—the ever-increasing number of cells examined in a given study.

While early studies examined a few hundred cells, with reduced costs and the widespread adoption of newer droplet-based technologies, we anticipate that analysis of millions to billions of cells will become commonplace within the next 5 years [ 96 ].

The Human Cell Atlas project [ 51 ], with the ultimate goal of profiling all human cell states and types, is evidence of this trend. With the accumulation of such enormous datasets, the issue arises regarding how to use them to their full potential. Many researchers would without doubt benefit from centralized repositories where data could be easily accessed at the cellular level instead of just sequence level [ 97 ].

We expect that mRNA capture rates will continue to improve over the next 5 years, to an extent where perhaps almost all mRNA molecules will be captured and detected. This will permit more-sensitive analysis of gene expression in individual cells and might also serve to reduce the number of cells required in any given study. Given the unique analytical challenges posed by scRNA-seq datasets, we expect great advances in bioinformatic and computational approaches in the coming years.

In particular, user-friendly, web-browser-like interfaces will emerge as gold-standard packages for dealing with scRNA-seq data. These will contain all the necessary functionality to allow researchers first to QC their data and then to extract biological information relating to heterogeneity, the existence of rare populations, lineage tracing, gene—gene co-regulation and other parameters.

Recent studies are providing exciting possibilities for combining scRNA-seq with other modalities. We expect that many new combination approaches will emerge using proteomics, epigenomics and analysis of non-coding RNA species alongside scRNA-seq reviewed in [ ].

We speculate that the next decade will take us closer to a truly holistic examination of single cells, which takes into account not only mRNA, but also the genome, epigenome, proteome and metabolome. Finally, we believe that several clinical applications will emerge for scRNA-seq in the next 5 or so years.

For example, resected tumours might be routinely assessed for the presence of rare malignant and chemo-resistant cancer cells. This information will provide crucial diagnostic information and will guide decisions regarding treatment. Next, as an extension to a full blood count, scRNA-seq assessments will provide in-depth information on the response of immune cells, which again will inform diagnoses and the choice of therapy.

Finally, the relatively small numbers of cells present in a range of other tissue biopsies, for example from the skin and gut mucosal surfaces, will be ideal for providing molecular data that informs on diagnosis, disease progression and appropriate treatments.

Thus, scRNA-seq will progress out of specialist research laboratories and will become an established tool for both basic scientists and clinicians alike. This decade has marked tremendous maturation of the field of single-cell transcriptomics. This has spurred the launch of numerous easily accessible commercial solutions, increasingly being accompanied by dedicated bioinformatics data-analysis suites.

With the recent advances in microfluidics and cellular barcoding, the throughput of scRNA-seq experiments has also increased substantially. At the same time, protocols compatible with fixation and freezing have started to emerge. These developments have made scRNA-seq much better suited for biomedical research and for clinical applications.

For example, the ability to study thousands of cells in a single run has greatly facilitated prospective studies of highly heterogeneous clinical samples.

This can be expected to have a profound impact on both translational applications as well as our understanding of basic tissue architecture and physiology. With these increasing opportunities for single-cell transcriptome characterization, we have witnessed remarkable diversification of experimental protocols, each coming with characteristic strengths and weaknesses. Researchers therefore face decisions such as whether to prioritize cell throughput or sequencing depth, whether full-length transcript information is required, and whether protein-level or epigenomic measurements are to be performed from the same cells.

Having clearly defined biological objectives and a rational experimental design are often vital for making an informed decision about the optimal approach. Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry. Nat Methods. Mapping the human DC lineage through the integration of high-dimensional techniques.

Article PubMed Google Scholar. A gene stemness score for rapid determination of risk in acute leukaemia. Quartz-Seq: a highly reproducible and sensitive single-cell RNA sequencing method, reveals non-genetic gene-expression heterogeneity. Genome Biol. Accounting for technical noise in single-cell RNA-seq experiments. Single-cell RNA sequencing reveals T helper cells synthesizing steroids de novo to contribute to immune homeostasis.

Cell Rep. Single-cell RNA-seq reveals dynamic, random monoallelic gene expression in mammalian cells. Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types. Reconstructing lineage hierarchies of the distal lung epithelium using single-cell RNA-seq. Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells. Single-cell RNA-seq reveals dynamic paracrine control of cellular variation. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma.

Brain structure. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Single cell RNA-sequencing of pluripotent states unlocks modular transcriptional variation. Cell Stem Cell. Single-cell RNA-Seq profiling of human preimplantation embryos and embryonic stem cells. Nat Struct Mol Biol. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq.

T cell fate and clonality inference from single-cell transcriptomes. Defining the three cell lineages of the human blastocyst by single-cell RNA-seq. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat Biotechnol. Single-cell RNA-seq reveals lineage and X chromosome dynamics in human preimplantation embryos. Sci Immunol. Deterministic and stochastic allele specific gene expression in single mouse blastomeres. PLoS One. Analysis of allelic expression patterns in clonal somatic cells by single-cell RNA-seq.

Nat Genet. Characterizing noise structure in single-cell RNA-seq distinguishes genuine from technical stochastic allelic expression. Nat Commun. Flipping between Polycomb repressed and active transcriptional states introduces noise in gene expression. Liu S, Trapnell C. Single-cell transcriptome sequencing: recent advances and remaining challenges.

Revealing the vectors of cellular identity with single-cell genomics. Comparative analysis of single-cell RNA sequencing methods. Mol Cell. Power analysis of single-cell RNA-sequencing experiments. Nuclear RNA-seq of single neurons reveals molecular signatures of activation.

Single-nucleus RNA-seq of differentiating human myoblasts reveals the extent of fate heterogeneity. Nucleic Acids Res. Comprehensive single cell transcriptional profiling of a multicellular organism by combinatorial indexing. In BioRxiv. Scaling single cell transcriptomics through split pool barcoding. Effective detection of variation in single-cell transcriptomes using MATQ-seq.

Counting absolute numbers of molecules using unique molecular identifiers. Donati G. The niche in single-cell technologies. Immunol Cell Biol. Ten years of next-generation sequencing technology. Trends Genet.

Quantitative assessment of single-cell RNA-sequencing methods. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets.

Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Massively parallel digital transcriptional profiling of single cells. As a variance-based feature selection method, it selects genes according to its contribution to the first singular value direction of input data in a data-driven approach.

It demonstrates robustness to depth bias and gene length bias in feature selection in comparison with its five peer methods. Combining with state-of-the-art RNA-seq differential expression analysis, it contributes to enhancing differential expression analysis by lowering false discovery rates caused by the biases.

Furthermore, we demonstrated the effectiveness of the proposed feature selection by proposing a data-driven differential expression analysis: NSVA-seq, besides conducting network marker discovery. Life and medical sciences. Computational biology. Systems biology. Check if you have access through your login credentials or your institution to get full access on this article. A novel feature selection for RNA-seq analysis.

Author: Henry Han. Published: 01 December This alert has been successfully added and will be sent to: You will be notified whenever a record that you have chosen has been cited. To manage your alert preferences, click on the button below. Manage my Alerts. New Citation Alert! Save to Binder. Create a New Binder Name.

Advanced platform capabilities inside a simple to use dashboard. Explore your data immediately and stop waiting for results. Seamlessly create new filters to experiment with cut-off values while your interactive plots and interpretation are updated in moments. Create unlimited cut-off filters with multiple fold change and p-adjusted parameters.

Select, search and create new gene lists and signatures. Choose your favorite color scheme for plot publishing. Create new filters to adjust cut-offs and focus on genes of interest. Experiment with different cut-off values to update plots and explore updated interpretation of enriched genes.

Why wait for days when you can explore your data now? Define a unique filter name. Select an easily identifiable icon color and initial. Set Filter Parameters for up-regulation, down-regulation and pValue. Download and export of filtered gene expression data. Download publication-ready figures with clear explanations for every Scientist.

Every plot and figure is rendered for high-quality and downloadable in multiple formats. Expand current plot to full-size and hide the explanation. Links to industry resources for additional explanation.

Focus on genes of interest using Gene Lists and Signatures to rapidly assess every experiment. Create, collaborate and update gene lists so that you can discover and focus on the most important signatures across oceans of data. Each plot dynamically updates when a new list is selected.

Select or Create New Gene List. Heatmap and Volcano Plot display only the genes from the selected list that pass the current fold change and pValue filter. The informational blue bar indicates how many genes from the selected list are not present in the current filter. Create new lists from selected genes. Remove genes. Add genes and entire pathways to the current list. All plots dynamically update in real-time to showcase changes made.

Navigate the most significant pathways and enriched terms with a simple click. Navigate the details of every term including Pathways, Gene Ontology, Proteins and many others.

Visually explore your results across any pathway or term with one click. Tooltips provide extended information for every gene and sample. Learn more from NCBI on each gene with the bottom bar magnifier. Dive even deeper into pathway interpretation by clicking the knowledge base magnifer. Dive deeper into the pathways and the networks that connect them.

Pathways are shown and sorted by significance. Review the number of genes in each term, including totals for up and down regulated genes. Click on a term to display genes within the current fold change and pVal filter. Click on a gene to display all significant pathways.

Sort genes by fold change, alphabetical or pValue significance. Toggle the gene list area into more Interactive plots. Toggle between pValue and pAdj sorting. Download complete set of all pathway interpretation details. Click the golden magnifier to access annotated pathway diagrams. Access rich pathway diagrams colored by gene expression levels. Experience pathway diagrams with detailed descriptions, annonated fold change colors, and gene heatmaps.

Interact with the pathway diagram to see corresponding genes highlight on the left. Interact with the gene list to see corresponding genes highlight in the pathway diagram. Access external references through the pathway magnifier. Download publication-ready pathway diagrams in preferred colors. The study of gene expression provides valuable insights into the nature of diseases and the effect of treatments by quantifying the activity of RNA in a biological sample. Scientists working in Oncology, Immunology, Regenerative Medicine, Drug Discovery and other areas of research often conduct experiments between healthy and disease states to identify Differentially expressed genes and biological pathways to discover therapeutic targets.

Comparisons between these differential patterns reveal unique gene signatures valuable for drug and diagnostic development. ROSALIND is a cloud platform that connects researchers to experiment design to quality control, differential expression and pathway exploration in a real-time collaborative environment. Receive same-day results with every experiment in an interactive experience designed for ease of use and saving valuable time.

ROSALIND enables scientists and researchers to analyze and interpret differential gene expression without the need for bioinformatics or programming skills.