Trajectory inference

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Trajectory inference as implemented in Slingshot for (a) a simulated two-dimensional dataset and (b) a single-cell RNA-seq dataset of the olfactory epithelium. Slingshot-pseudotime.png
Trajectory inference as implemented in Slingshot for (a) a simulated two-dimensional dataset and (b) a single-cell RNA-seq dataset of the olfactory epithelium.

Trajectory inference or pseudotemporal ordering is a computational technique used in single-cell transcriptomics to determine the pattern of a dynamic process experienced by cells and then arrange cells based on their progression through the process. Single-cell protocols have much higher levels of noise than bulk RNA-seq, [1] so a common step in a single-cell transcriptomics workflow is the clustering of cells into subgroups. [2] Clustering can contend with this inherent variation by combining the signal from many cells, while allowing for the identification of cell types. [3] However, some differences in gene expression between cells are the result of dynamic processes such as the cell cycle, cell differentiation, or response to an external stimuli. Trajectory inference seeks to characterize such differences by placing cells along a continuous path that represents the evolution of the process rather than dividing cells into discrete clusters. [4] In some methods this is done by projecting cells onto an axis called pseudotime which represents the progression through the process. [5]

Contents

Methods

Since 2015, more than 50 algorithms for trajectory inference have been created. [6] Although the approaches taken are diverse there are some commonalities to the methods. Typically, the steps in the algorithm consist of dimensionality reduction to reduce the complexity of the data, trajectory building to determine the structure of the dynamic process, and projection of the data onto the trajectory so that cells are positioned by their development through the process and cells with similar expression profiles are situated near each other. [6] Trajectory inference algorithms differ in the specific procedure used for dimensionality reduction, the kinds of structures that can be used to represent the dynamic process, and the prior information that is required or can be provided. [2]

PCA of a multivariate Gaussian distribution. The vectors shown are the first (longer vector) and second principal components, which indicate the directions of maximum variance. GaussianScatterPCA.svg
PCA of a multivariate Gaussian distribution. The vectors shown are the first (longer vector) and second principal components, which indicate the directions of maximum variance.

Dimensionality reduction

The data produced by single-cell RNA-seq can consist of thousands of cells each with expression levels recorded across thousands of genes. [7] In order to efficiently process data with such high dimensionality many trajectory inference algorithms employ a dimensionality reduction procedure such as principal component analysis (PCA), independent component analysis (ICA), or t-SNE as their first step. [8] The purpose of this step is to combine many features of the data into a more informative measure of the data. [4] For example, a coordinate resulting from dimensionality reduction could combine expression levels from many genes that are associated with the cell cycle into one value that represents a cell's position in the cell cycle. [8] Such a transformation corresponds to dimensionality reduction in the feature space, but dimensionality reduction can also be applied to the sample space by clustering together groups of similar cells. [1]

Trajectory building

A graph with six vertices. Many trajectory inference algorithms use graphs to build the trajectory. 6n-graf.svg
A graph with six vertices. Many trajectory inference algorithms use graphs to build the trajectory.

Many methods represent the structure of the dynamic process via a graph-based approach. In such an approach the vertices of the graph correspond to states in the dynamic process, such as cell types in cell differentiation, and the edges between the nodes correspond to transitions between the states. [6] The creation of the trajectory graph can be accomplished using k-nearest neighbors or minimum spanning tree algorithms. [9] The topology of the trajectory refers to the structure of the graph and different algorithms are limited to creation of graph topologies of a particular type such as linear, branching, or cyclic. [4]

Use of prior information

Some methods require or allow for the input of prior information which is used to guide the creation of the trajectory. The use of prior information can lead to more accurate trajectory determination, but poor priors can lead the algorithm astray or bias results towards expectations. [6] Examples of prior information that can be used in trajectory inference are the selection of start cells that are at the beginning of the trajectory, the number of branches in the trajectory, and the number of end states for the trajectory. [10]

Software

MARGARET

MARGARET employs a deep unsupervised metric learning approach for inferring the cellular latent space and cell clusters. The trajectory is modeled using a cluster-connectivity graph to capture complex trajectory topologies. MARGARET utilizes the inferred trajectory for determining terminal states and inferring cell-fate plasticity using a scalable Absorbing Markov chain model. [11]

Monocle

Monocle first employs a differential expression test to reduce the number of genes then applies independent component analysis for additional dimensionality reduction. To build the trajectory Monocle computes a minimum spanning tree, then finds the longest connected path in that tree. Cells are projected onto the nearest point to them along that path. [5]

p-Creode

p-Creode finds the most likely path through a density-adjusted k-nearest neighbor graph. Graphs from an ensemble are scored with a graph similarity metric to select the most representative topology.  p-Creode has been tested on a range of single-cell platforms, including mass cytometry, multiplex immunofluorescence, [12] and single-cell RNA-seq. No prior information is required. [13]

Slingshot

Slingshot takes cluster labels as input and then orders these clusters into lineages by the construction of a minimum spanning tree. Paths through the tree are smoothed by fitting simultaneous principal curves and a cell's pseudotime value is determined by its projection onto one or more of these curves. Prior information, such as initial and terminal clusters, is optional. [10]

TSCAN

TSCAN performs dimensionality reduction using principal component analysis and clusters cells using a mixture model. A minimum spanning tree is calculated using the centers of the clusters and the trajectory is determined as the longest connected path of that tree. TSCAN is an unsupervised algorithm that requires no prior information. [14]

Wanderlust/Wishbone

Wanderlust was developed for analysis of mass cytometry data, but has been adapted for single-cell transcriptomics applications. A k-nearest neighbors algorithm is used to construct a graph which connects every cell to the cell closest to it with respect to a metric such as Euclidean distance or cosine distance. Wanderlust requires the input of a starting cell as prior information. [15]

Wishbone is built on Wanderlust and allows for a bifurcation in the graph topology, whereas Wanderlust creates a linear graph. Wishbone combines principal component analysis and diffusion maps to achieve dimensionality reduction then also creates a KNN graph. [16]

Waterfall

Waterfall performs dimensionality reduction via principal component analysis and uses a k-means algorithm to find cell clusters. A minimal spanning tree is built between the centers of the clusters. Waterfall is entirely unsupervised, requiring no prior information, and produces linear trajectories. [17]

Related Research Articles

<span class="mw-page-title-main">DNA microarray</span> Collection of microscopic DNA spots attached to a solid surface

A DNA microarray is a collection of microscopic DNA spots attached to a solid surface. Scientists use DNA microarrays to measure the expression levels of large numbers of genes simultaneously or to genotype multiple regions of a genome. Each DNA spot contains picomoles of a specific DNA sequence, known as probes. These can be a short section of a gene or other DNA element that are used to hybridize a cDNA or cRNA sample under high-stringency conditions. Probe-target hybridization is usually detected and quantified by detection of fluorophore-, silver-, or chemiluminescence-labeled targets to determine relative abundance of nucleic acid sequences in the target. The original nucleic acid arrays were macro arrays approximately 9 cm × 12 cm and the first computerized image based analysis was published in 1981. It was invented by Patrick O. Brown. An example of its application is in SNPs arrays for polymorphisms in cardiovascular diseases, cancer, pathogens and GWAS analysis. It is also used for the identification of structural variations and the measurement of gene expression.

The transcriptome is the set of all RNA transcripts, including coding and non-coding, in an individual or a population of cells. The term can also sometimes be used to refer to all RNAs, or just mRNA, depending on the particular experiment. The term transcriptome is a portmanteau of the words transcript and genome; it is associated with the process of transcript production during the biological process of transcription.

<span class="mw-page-title-main">Biological network inference</span> Type of inference

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ChIP-sequencing, also known as ChIP-seq, is a method used to analyze protein interactions with DNA. ChIP-seq combines chromatin immunoprecipitation (ChIP) with massively parallel DNA sequencing to identify the binding sites of DNA-associated proteins. It can be used to map global binding sites precisely for any protein of interest. Previously, ChIP-on-chip was the most common technique utilized to study these protein–DNA relations.

<span class="mw-page-title-main">RNA-Seq</span> Lab technique in cellular biology

RNA-Seq is a technique that uses next-generation sequencing to reveal the presence and quantity of RNA molecules in a biological sample, providing a snapshot of gene expression in the sample, also known as transcriptome.

De novo transcriptome assembly is the de novo sequence assembly method of creating a transcriptome without the aid of a reference genome.

Single-cell sequencing examines the nucleic acid sequence information from individual cells with optimized next-generation sequencing technologies, providing a higher resolution of cellular differences and a better understanding of the function of an individual cell in the context of its microenvironment. For example, in cancer, sequencing the DNA of individual cells can give information about mutations carried by small populations of cells. In development, sequencing the RNAs expressed by individual cells can give insight into the existence and behavior of different cell types. In microbial systems, a population of the same species can appear genetically clonal. Still, single-cell sequencing of RNA or epigenetic modifications can reveal cell-to-cell variability that may help populations rapidly adapt to survive in changing environments.

<span class="mw-page-title-main">Gene co-expression network</span>

A gene co-expression network (GCN) is an undirected graph, where each node corresponds to a gene, and a pair of nodes is connected with an edge if there is a significant co-expression relationship between them. Having gene expression profiles of a number of genes for several samples or experimental conditions, a gene co-expression network can be constructed by looking for pairs of genes which show a similar expression pattern across samples, since the transcript levels of two co-expressed genes rise and fall together across samples. Gene co-expression networks are of biological interest since co-expressed genes are controlled by the same transcriptional regulatory program, functionally related, or members of the same pathway or protein complex.

In molecular phylogenetics, relationships among individuals are determined using character traits, such as DNA, RNA or protein, which may be obtained using a variety of sequencing technologies. High-throughput next-generation sequencing has become a popular technique in transcriptomics, which represent a snapshot of gene expression. In eukaryotes, making phylogenetic inferences using RNA is complicated by alternative splicing, which produces multiple transcripts from a single gene. As such, a variety of approaches may be used to improve phylogenetic inference using transcriptomic data obtained from RNA-Seq and processed using computational phylogenetics.

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Single-cell transcriptomics examines the gene expression level of individual cells in a given population by simultaneously measuring the RNA concentration of hundreds to thousands of genes. Single-cell transcriptomics makes it possible to unravel heterogeneous cell populations, reconstruct cellular developmental pathways, and model transcriptional dynamics — all previously masked in bulk RNA sequencing.

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Patch-sequencing (patch-seq) is a modification of patch-clamp technique that combines electrophysiological, transcriptomic and morphological characterization of individual neurons. In this approach, the neuron's cytoplasm is collected and processed for RNAseq after electrophysiological recordings are performed on it. The cell is simultaneously filled with a dye that allows for subsequent morphological reconstruction.

<span class="mw-page-title-main">RNA timestamp</span> RNA editing application

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Single-cell genome and epigenome by transposases sequencing (scGET-seq) is a DNA sequencing method for profiling open and closed chromatin. In contrast to single-cell assay for transposase-accessible chromatin with sequencing (scATAC-seq), which only targets active euchromatin, scGET-seq is also capable of probing inactive heterochromatin.

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<span class="mw-page-title-main">Single-cell multi-omics integration</span> Computational methods in biology

Single-cell multi-omics integration describes a suite of computational methods used to harmonize information from multiple "omes" to jointly analyze biological phenomena. This approach allows researchers to discover intricate relationships between different chemical-physical modalities by drawing associations across various molecular layers simultaneously. Multi-omics integration approaches can be categorized into four broad categories: Early integration, intermediate integration, late integration methods. Multi-omics integration can enhance experimental robustness by providing independent sources of evidence to address hypotheses, leveraging modality-specific strengths to compensate for another's weaknesses through imputation, and offering cell-type clustering and visualizations that are more aligned with reality

References

  1. 1 2 Bacher, Rhonda; Kendziorski, Christina (2016-04-07). "Design and computational analysis of single-cell RNA-sequencing experiments". Genome Biology. 17 (1): 63. doi: 10.1186/s13059-016-0927-y . ISSN   1474-760X. PMC   4823857 . PMID   27052890.
  2. 1 2 Hwang, Byungjin; Lee, Ji Hyun; Bang, Duhee (2018-08-07). "Single-cell RNA sequencing technologies and bioinformatics pipelines". Experimental & Molecular Medicine. 50 (8): 1–14. doi:10.1038/s12276-018-0071-8. ISSN   2092-6413. PMC   6082860 . PMID   30089861.
  3. ↑ Stegle, Oliver; Teichmann, Sarah A.; Marioni, John C. (2015-01-28). "Computational and analytical challenges in single-cell transcriptomics". Nature Reviews Genetics. 16 (3): 133–145. doi:10.1038/nrg3833. ISSN   1471-0056. PMID   25628217. S2CID   205486032.
  4. 1 2 3 Cannoodt, Robrecht; Saelens, Wouter; Saeys, Yvan (2016-10-19). "Computational methods for trajectory inference from single-cell transcriptomics". European Journal of Immunology. 46 (11): 2496–2506. doi: 10.1002/eji.201646347 . hdl: 1854/LU-8510906 . ISSN   0014-2980. PMID   27682842. S2CID   19562455.
  5. 1 2 Trapnell, Cole; Cacchiarelli, Davide; Grimsby, Jonna; Pokharel, Prapti; Li, Shuqiang; Morse, Michael; Lennon, Niall J; Livak, Kenneth J; Mikkelsen, Tarjei S (2014-03-23). "The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells". Nature Biotechnology. 32 (4): 381–386. doi:10.1038/nbt.2859. ISSN   1087-0156. PMC   4122333 . PMID   24658644.
  6. 1 2 3 4 Saelens, Wouter; Cannoodt, Robrecht; Todorov, Helena; Saeys, Yvan (2019-01-04). "A comparison of single-cell trajectory inference methods". Nature Biotechnology. 37 (5): 547–555. doi:10.1038/s41587-019-0071-9. PMID   30936559. S2CID   89616753.
  7. ↑ Conesa, Ana; Madrigal, Pedro; Tarazona, Sonia; Gomez-Cabrero, David; Cervera, Alejandra; McPherson, Andrew; Szcześniak, Michał Wojciech; Gaffney, Daniel J.; Elo, Laura L. (2016-01-26). "A survey of best practices for RNA-seq data analysis". Genome Biology. 17 (1): 13. doi: 10.1186/s13059-016-0881-8 . ISSN   1474-760X. PMC   4728800 . PMID   26813401.
  8. 1 2 Yosef, Nir; Regev, Aviv; Wagner, Allon (November 2016). "Revealing the vectors of cellular identity with single-cell genomics". Nature Biotechnology. 34 (11): 1145–1160. doi:10.1038/nbt.3711. ISSN   1546-1696. PMC   5465644 . PMID   27824854.
  9. ↑ Cahan, Patrick; Tan, Yuqi; Kumar, Pavithra (2017-01-01). "Understanding development and stem cells using single cell-based analyses of gene expression". Development. 144 (1): 17–32. doi:10.1242/dev.133058. ISSN   1477-9129. PMC   5278625 . PMID   28049689.
  10. 1 2 Street, Kelly; Risso, Davide; Fletcher, Russell B.; Das, Diya; Ngai, John; Yosef, Nir; Purdom, Elizabeth; Dudoit, Sandrine (2018-06-19). "Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics". BMC Genomics. 19 (1): 477. doi: 10.1186/s12864-018-4772-0 . PMC   6007078 . PMID   29914354.
  11. ↑ Pandey, Kushagra; Zafar, Hamim (2022). "Inference of cell state transitions and cell fate plasticity from single-cell with MARGARET". Nucleic Acids Research. 50 (15): e86. doi: 10.1093/nar/gkac412 . ISSN   0305-1048. PMC   9410915 . PMID   35639499.
  12. ↑ Gerdes, M. J.; Sevinsky, C. J.; Sood, A.; Adak, S.; Bello, M. O.; Bordwell, A.; Can, A.; Corwin, A.; Dinn, S. (2013-07-01). "Highly multiplexed single-cell analysis of formalin-fixed, paraffin-embedded cancer tissue". Proceedings of the National Academy of Sciences. 110 (29): 11982–11987. Bibcode:2013PNAS..11011982G. doi: 10.1073/pnas.1300136110 . ISSN   0027-8424. PMC   3718135 . PMID   23818604.
  13. ↑ Lau, Ken S.; Coffey, Robert J.; Gerdes, Michael J.; Liu, Qi; Franklin, Jeffrey L.; Roland, Joseph T.; Ping, Jie; Simmons, Alan J.; McKinley, Eliot T. (2018-01-24). "Unsupervised Trajectory Analysis of Single-Cell RNA-Seq and Imaging Data Reveals Alternative Tuft Cell Origins in the Gut". Cell Systems. 6 (1): 37–51.e9. doi:10.1016/j.cels.2017.10.012. ISSN   2405-4712. PMC   5799016 . PMID   29153838.
  14. ↑ Ji, Zhicheng; Ji, Hongkai (2016-05-13). "TSCAN: Pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis". Nucleic Acids Research. 44 (13): e117. doi:10.1093/nar/gkw430. ISSN   0305-1048. PMC   4994863 . PMID   27179027.
  15. ↑ Bendall, Sean C.; Davis, Kara L.; Amir, El-ad David; Tadmor, Michelle D.; Simonds, Erin F.; Chen, Tiffany J.; Shenfeld, Daniel K.; Nolan, Garry P.; Pe'Er, Dana (2014-04-24). "Single-Cell Trajectory Detection Uncovers Progression and Regulatory Coordination in Human B Cell Development". Cell. 157 (3): 714–725. doi:10.1016/j.cell.2014.04.005. ISSN   0092-8674. PMC   4045247 . PMID   24766814.
  16. ↑ Setty, Manu; Tadmor, Michelle D; Reich-Zeliger, Shlomit; Angel, Omer; Salame, Tomer Meir; Kathail, Pooja; Choi, Kristy; Bendall, Sean; Friedman, Nir (2016-05-02). "Wishbone identifies bifurcating developmental trajectories from single-cell data". Nature Biotechnology. 34 (6): 637–645. doi:10.1038/nbt.3569. ISSN   1087-0156. PMC   4900897 . PMID   27136076.
  17. ↑ Shin, Jaehoon; Berg, Daniel A.; Zhu, Yunhua; Shin, Joseph Y.; Song, Juan; Bonaguidi, Michael A.; Enikolopov, Grigori; Nauen, David W.; Christian, Kimberly M.; Ming, Guo-li; Song, Hongjun (2015-09-03). "Single-Cell RNA-Seq with Waterfall Reveals Molecular Cascades underlying Adult Neurogenesis". Cell Stem Cell. 17 (3): 360–372. doi: 10.1016/j.stem.2015.07.013 . ISSN   1934-5909. PMC   8638014 . PMID   26299571.
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