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536:. As a result of the aforementioned properties of single-cell transcriptomic data, batch correction methods developed for bulk sequencing data were observed to perform poorly. Consequently, researchers developed statistical methods to correct for batch effects that are robust to the properties of single-cell transcriptomic data to integrate data from different sources or experimental batches. Laleh Haghverdi performed foundational work in formulating the use of mutual nearest neighbors between each batch to define batch correction vectors. With these vectors, you can merge datasets that each include at least one shared cell type. An orthogonal approach involves the projection of each dataset onto a shared low-dimensional space using
115:
540:. Mutual nearest neighbors and canonical correlation analysis have also been combined to define integration "anchors" comprising reference cells in one dataset, to which query cells in another dataset are normalized. Another class of methods (e.g., scDREAMER) uses deep generative models such as variational autoencoders for learning batch-invariant latent cellular representations which can be used for downstream tasks such as cell type clustering, denoising of single-cell gene expression vectors and trajectory inference.
477:
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bifurcate or follow more complex graph structures. The trajectory, therefore, enables the inference of gene expression dynamics and the ordering of cells by their progression through differentiation or response to external stimuli. The method relies on the assumptions that the cells follow the same path through the process of interest and that their transcriptional state correlates to their progression. The algorithm can be applied to both mixed populations and temporal samples.
78:
often form subpopulations with unique transcriptional profiles. Correlations in the gene expression of the subpopulations can often be missed due to the lack of subpopulation identification. Secondly, bulk assays fail to recognize whether a change in the expression profile is due to a change in regulation or composition — for example if one cell type arises to dominate the population. Lastly, when your goal is to study cellular progression through
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represent the genes and edges indicate co-regulatory interactions. The method relies on the assumption that a strong statistical relationship between the expression of genes is an indication of a potential functional relationship. The most commonly used method to measure the strength of a statistical
484:
Pseudo-temporal ordering (or trajectory inference) is a technique that aims to infer gene expression dynamics from snapshot single-cell data. The method tries to order the cells in such a way that similar cells are closely positioned to each other. This trajectory of cells can be linear, but can also
493:
using the transformed data, orders cells in pseudo-time by following the longest connected path of the tree and consequently labels cells by type. Another example is the diffusion pseudotime (DPT) algorithm, which uses a diffusion map and diffusion process. Another class of methods such as MARGARET
488:
More than 50 methods for pseudo-temporal ordering have been developed, and each has its own requirements for prior information (such as starting cells or time course data), detectable topologies, and methodology. An example algorithm is the
Monocle algorithm that carries out dimensionality reduction
368:
Biclustering provides several advantages by improving the resolution of clustering. Genes that are only informative to a subset of cells and are hence only expressed there can be identified through biclustering. Moreover, similarly behaving genes that differentiate one cell cluster from another can
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and transforms the data so that the first principal component has the largest possible variance, and successive principle components in turn each have the highest variance possible while remaining orthogonal to the preceding components. The contribution each gene makes to each component is used to
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allows for the formation of subgroups in the cell population. Cells can be clustered by their transcriptomic profile in order to analyse the sub-population structure and identify rare cell types or cell subtypes. Alternatively, genes can be clustered by their expression states in order to identify
77:
These studies are limited as they provide measurements for whole tissues and, as a result, show an average expression profile for all the constituent cells. This has a couple of drawbacks. Firstly, different cell types within the same tissue can have distinct roles in multicellular organisms. They
461:
is a technique used to identify which GO terms are over-represented or under-represented in a given set of genes. In single-cell analysis input list of genes of interest can be selected based on differentially expressed genes or groups of genes generated from biclustering. The number of genes
271:
A problem associated with single-cell data occurs in the form of zero inflated gene expression distributions, known as technical dropouts, that are common due to low mRNA concentrations of less-expressed genes that are not captured in the reverse transcription process. The percentage of mRNA
89:
have enabled the generation of single-cell transcriptomic data, they also presented new computational and analytical challenges. Bioinformaticians can use techniques from bulk RNA-seq for single-cell data. Still, many new computational approaches have had to be designed for this data type to
1009:
Zheng, Grace X. Y.; Terry, Jessica M.; Belgrader, Phillip; Ryvkin, Paul; Bent, Zachary W.; Wilson, Ryan; Ziraldo, Solongo B.; Wheeler, Tobias D.; McDermott, Geoff P.; Zhu, Junjie; Gregory, Mark T.; Shuga, Joe; Montesclaros, Luz; Underwood, Jason G.; Masquelier, Donald A. (2017-01-16).
424:
Detecting differences in gene expression level between two populations is used both single-cell and bulk transcriptomic data. Specialised methods have been designed for single-cell data that considers single cell features such as technical dropouts and shape of the distribution e.g.
260:(UMIs)-short DNA sequences (6–10nt) that are added to each cDNA before amplification and act as a bar code for each cDNA molecule. Normalisation is achieved by using the count number of unique UMIs associated with each gene to account for differences in amplification efficiency.
400:. The result of this method produces graphs with each cell as a point in a 2-D or 3-D space. Dimensionality reduction is frequently used before clustering as cells in high dimensions can wrongly appear to be close due to distance metrics behaving non-intuitively.
2042:
Moignard, Victoria; Macaulay, Iain C.; Swiers, Gemma; Buettner, Florian; Schütte, Judith; Calero-Nieto, Fernando J.; Kinston, Sarah; Joshi, Anagha; Hannah, Rebecca; Theis, Fabian J.; Jacobsen, Sten Eirik; de Bruijn, Marella F.; Göttgens, Berthold (1 April 2013).
527:
The presence or strength of technical effects and the types of cells observed often differ in single-cell transcriptomics datasets generated using different experimental protocols and under different conditions. This difference results in strong
161:
Combining FACS with scRNA-seq has produced optimized protocols such as SORT-seq. A list of studies that utilized SORT-seq can be found here. Moreover, combining microfluidic devices with scRNA-seq has been optimized in 10x
Genomics protocols.
202:
concentration. A plot of fluorescence vs. cycle number is made and a threshold fluorescence level is used to find cycle number at which the plot reaches this value. The cycle number at this point is known as the threshold cycle
927:
Muraro, Mauro J.; Dharmadhikari, Gitanjali; Grün, Dominic; Groen, Nathalie; Dielen, Tim; Jansen, Erik; van Gurp, Leon; Engelse, Marten A.; Carlotti, Francoise; de Koning, Eelco J. P.; van
Oudenaarden, Alexander (2016-10-26).
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Insights based on single-cell data analysis assume that the input is a matrix of normalised gene expression counts, generated by the approaches outlined above, and can provide opportunities that are not obtainable by bulk.
1286:
Islam, Saiful; Zeisel, Amit; Joost, Simon; La Manno, Gioele; Zajac, Pawel; Kasper, Maria; Lönnerberg, Peter; Linnarsson, Sten (1 February 2014). "Quantitative single-cell RNA-seq with unique molecular identifiers".
122:
There are several methods available to isolate and amplify cells for single-cell analysis. Low throughput techniques are able to isolate hundreds of cells, are slow, and enable selection. These methods include:
875:
Poulin, Jean-Francois; Tasic, Bosiljka; Hjerling-Leffler, Jens; Trimarchi, Jeffrey M.; Awatramani, Rajeshwar (1 September 2016). "Disentangling neural cell diversity using single-cell transcriptomics".
1077:
Radonić, Aleksandar; Thulke, Stefanie; Mackay, Ian M.; Landt, Olfert; Siegert, Wolfgang; Nitsche, Andreas (23 January 2004). "Guideline to reference gene selection for quantitative real-time PCR".
1882:
Trapnell, Cole; Cacchiarelli, Davide; Grimsby, Jonna; Pokharel, Prapti; Li, Shuqiang; Morse, Michael; Lennon, Niall J.; Livak, Kenneth J.; Mikkelsen, Tarjei S.; Rinn, John L. (23 March 2017).
144:
High-throughput methods are able to quickly isolate hundreds to tens of thousands of cells. Common techniques include:
1499:
Buettner, Florian; Natarajan, Kedar N.; Casale, F. Paolo; Proserpio, Valentina; Scialdone, Antonio; Theis, Fabian J.; Teichmann, Sarah A.; Marioni, John C.; Stegle, Oliver (1 February 2015).
462:
annotated to a GO term in the input list is normalised against the number of genes annotated to a GO term in the background set of all genes in genome to determine statistical significance.
2247:
Stuart, Tim; Butler, Andrew; Hoffman, Paul; Hafemeister, Christoph; Papalexia, Efthymia; Mauck, William M III; Hao, Yuhan; Marlon, Stoeckius; Smibert, Peter; Satija, Rahul (6 June 2019).
82:, average expression profiles can only order cells by time rather than by developmental stage. Consequently, they cannot show trends in gene expression levels specific to certain stages.
1666:
Treutlein, Barbara; Brownfield, Doug G.; Wu, Angela R.; Neff, Norma F.; Mantalas, Gary L.; Espinoza, F. Hernan; Desai, Tushar J.; Krasnow, Mark A.; Quake, Stephen R. (15 May 2014).
283:
tail in spike-ins and therefore shorter length. Additionally, normalisation using UMIs assumes the cDNA library is sequenced to saturation, which is not always the case.
1395:
Svensson, Valentine; Natarajan, Kedar Nath; Ly, Lam-Ha; Miragaia, Ricardo J.; Labalette, Charlotte; Macaulay, Iain C.; Cvejic, Ana; Teichmann, Sarah A. (6 March 2017).
377:
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Normalisation of RNA-seq data accounts for cell to cell variation in the efficiencies of the cDNA library formation and sequencing. One method relies on the use of
85:
Recent advances in biotechnology allow the measurement of gene expression in hundreds to thousands of individual cells simultaneously. While these breakthroughs in
317:
The techniques outlined have been designed to help visualise and explore patterns in the data in order to facilitate the revelation of these three features.
66:. Higher throughput and speed allow researchers to frequently characterize the expression profiles of populations of thousands of cells. The data from bulk
190:, although the reliability of normalisation through this process is questionable as there is evidence that the level of expression can vary significantly.
279:
and spike-in RNA are the same. Evidence suggests that this is not the case given fundamental differences in size and features, such as the lack of a
2001:
Wei, J.; Hu, X.; Zou, X.; Tian, T. (1 December 2016). "Inference of genetic regulatory network for stem cell using single cells expression data".
38:
cell populations, reconstruct cellular developmental pathways, and model transcriptional dynamics — all previously masked in bulk RNA sequencing.
1448:
Stegle, Oliver; Teichmann, Sarah A.; Marioni, John C. (1 March 2015). "Computational and analytical challenges in single-cell transcriptomics".
2375:
2018:
1226:
Jiang, Lichun; Schlesinger, Felix; Davis, Carrie A.; Zhang, Yu; Li, Renhua; Salit, Marc; Gingeras, Thomas R.; Oliver, Brian (23 March 2017).
2045:"Characterization of transcriptional networks in blood stem and progenitor cells using high-throughput single-cell gene expression analysis"
182:, whose expression should be constant under the conditions, is used for normalisation. The most commonly used house keeping genes include
416:
infer which genes are contributing the most to variance in the population and are involved in differentiating different subpopulations.
198:
to detect the PCR product and monitor the progress of the amplification - the increase in fluorescence intensity is proportional to the
238:
techniques and the reads are mapped back to the reference genome, providing a count of the number of reads associated with each gene.
98:
There is so far no standardized technique to generate single-cell data: all methods must include cell isolation from the population,
718:
1501:"Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells"
2298:"scDREAMER for atlas-level integration of single-cell datasets using deep generative model paired with adversarial classifier"
537:
275:
When using RNA spike-ins for normalisation the assumption is made that the amplification and sequencing efficiencies for the
494:
employ graph partitioning for capturing complex trajectory topologies such as disconnected and multifurcating trajectories.
519:
is used as an alternative. Gene clusters linked in a network signify genes that undergo coordinated changes in expression.
137:
791:
Stegle, O.; Teichmann, S.; Marioni, J. (2015). "Computational and analytical challenges in single-cell transcriptomics".
458:
389:
257:
86:
834:
Kolodziejczyk, Aleksandra A.; Kim, Jong Kyoung; Svensson, Valentine; Marioni, John C.; Teichmann, Sarah A. (May 2015).
349:, has been used to simultaneously cluster by genes and cells to find genes that behave similarly within cell clusters.
106:
and quantification of expression levels. Common techniques for measuring expression are quantitative PCR or RNA-seq.
114:
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Gene regulatory network inference is a technique that aims to construct a network, shown as a graph, in which the
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can be used to simplify data for visualisation and pattern detection by transforming cells from a high to a lower
175:
385:
303:
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A combination of both spike-ins, UMIs and other approaches have been combined for more accurate normalisation.
79:
985:
709:
Szabo, David T. (2014). "Chapter 62 - Transcriptomic biomarkers in safety and risk assessment of chemicals".
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RNA spike-ins (RNA sequences of known sequence and quantity) that are added in equal quantities to each cell
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Haghverdi, Laleh; Büttner, Maren; Wolf, F. Alexander; Buettner, Florian; Theis, Fabian J. (1 October 2016).
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that may bias the findings of statistical methods applied across batches, particularly in the presence of
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and as a result data is usually only obtained for sample sizes of less than 100 genes. The inclusion of
103:
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has made gene expression analysis a routine. RNA analysis was previously limited to tracing individual
1729:"A statistical approach for identifying differential distributions in single-cell RNA-seq experiments"
2151:"Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors"
1884:"Pseudo-temporal ordering of individual cells reveals dynamics and regulators of cell fate decisions"
1679:
1023:
554:
471:
412:
2200:"Integrating single-cell transcriptomic data across different conditions, technologies, and species"
127:
34:(mRNA)) of hundreds to thousands of genes. Single-cell transcriptomics makes it possible to unravel
1548:
Ntranos, Vasilis; Kamath, Govinda M.; Zhang, Jesse M.; Pachter, Lior; Tse, David N. (26 May 2016).
1849:"A comparison of single-cell trajectory inference methods: towards more accurate and robust tools"
132:
2198:
Butler, Andrew; Hoffman, Paul; Smibert, Peter; Papalexi, Efthymia; Satija, Rahul (2 April 2018).
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1550:"Fast and accurate single-cell RNA-seq analysis by clustering of transcript-compatibility counts"
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Korthauer, Keegan D.; Chu, Li-Fang; Newton, Michael A.; Li, Yuan; Thomson, James; Stewart, Ron;
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Wildsmith, S. E.; Archer, G. E.; Winkley, A. J.; Lane, P. W.; Bugelski, P. J. (1 January 2001).
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terms describe gene functions and the relationships between those functions into three classes:
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Hicks, Stephanie C; Townes, William F; Teng, Mingxiang; Irizarry, Rafael A (6 November 2017).
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1944:"Inference of cell state transitions and cell fate plasticity from single-cell with MARGARET"
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in a given population by simultaneously measuring the RNA concentration (conventionally only
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1668:"Reconstructing lineage hierarchies of the distal lung epithelium using single-cell RNA-seq"
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To measure the level of expression of each transcript qPCR can be applied. Gene specific
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PCA example of
Guinean and other African populations Y chromosome haplogroup frequencies
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Identification and characterization of cell types and their spatial organisation in time
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has led to identifying genes differentially expressed in distinct cell populations, and
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1609:"ZIFA: Dimensionality reduction for zero-inflated single-cell gene expression analysis"
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The most frequently used technique is PCA, which identifies the directions of largest
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2149:
Haghverdi, Laleh; Lun, Aaron T L; Morgan, Michael D; Marioni, John C (2 April 2018).
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35:
2102:"Missing data and technical variability in single-cell RNA-sequencing experiments"
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852:
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1847:
Saelens, Wouter; Cannoodt, Robrecht; Todorov, Helena; Saeys, Yvan (2018-03-05).
1790:
652:"Single-cell transcriptome sequencing: recent advances and remaining challenges"
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51:
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facilitate a complete and detailed study of single-cell expression profiles.
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2003:
2016 IEEE International
Conference on Bioinformatics and Biomedicine (BIBM)
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and used to normalise read count by the number of reads mapped to spike-in
1960:
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Kharchenko, Peter V.; Silberstein, Lev; Scadden, David T. (1 July 2014).
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by single-cell RNA sequencing service provider Single Cell
Discoveries.
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1300:
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covarying genes. A combination of both clustering approaches, known as
227:
2296:
Shree, Ajita; Pavan, Musale
Krushna; Zafar, Hamim (27 November 2023).
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1279:
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1121:
1012:"Massively parallel digital transcriptional profiling of single cells"
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molecules in the cell lysate that are detected is often only 10-20%.
246:
99:
67:
2060:
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889:
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1340:"Bayesian approach to single-cell differential expression analysis"
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Iris dendrogram produced using a
Hierarchical clustering algorithm
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183:
231:
23:
2350:
1791:"Diffusion pseudotime robustly reconstructs lineage branching"
1169:
Wang, Zhong; Gerstein, Mark; Snyder, Michael (23 March 2017).
234:
fragments. These fragments are sequenced by high-throughput
174:
are used to amplify the corresponding gene as with regular
1397:"Power analysis of single-cell RNA-sequencing experiments"
836:"The Technology and Biology of Single-Cell RNA Sequencing"
736:"Defining cell types and states with single-cell genomics"
930:"A Single-Cell Transcriptome Atlas of the Human Pancreas"
230:
technique converts a population of RNAs to a library of
593:"Single cell transcriptomics: methods and applications"
1228:"Synthetic spike-in standards for RNA-seq experiments"
1122:"Maximization of signal derived from cDNA microarrays"
1171:"RNA-Seq: a revolutionary tool for transcriptomics"
1079:
1607:Pierson, Emma; Yau, Christopher (1 January 2015).
118:Fluorescence Assisted Cell Sorting workflow (FACS)
586:
584:
2249:"Comprehensive Integration of Single-Cell Data"
2355:The ultimate single-cell RNA sequencing guide
8:
1403:. advance online publication (4): 381–387.
306:and their strength across individual cells
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511:. However, correlation fails to identify
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1942:Pandey, Kushagra; Zafar, Hamim (2022).
1937:
1935:
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591:Kanter, Itamar; Kalisky, Tomer (2015).
580:
713:. Academic Press. pp. 1033–1038.
7:
650:Liu, Serena; Trapnell, Cole (2016).
149:Fluorescence activated cell sorting
46:The development of high-throughput
459:Gene Ontology (GO) term enrichment
352:Clustering methods applied can be
14:
369:be identified using this method.
207:) and is measured for each gene.
102:formation, amplification through
480:Graph with minimal spanning tree
734:Trapnell, Cole (October 2015).
26:expression level of individual
538:canonical correlation analysis
295:Three main insights provided:
1:
669:10.12688/f1000research.7223.1
360:, forming nested partitions.
356:, forming disjoint groups or
138:Laser capture microdissection
2376:Molecular biology techniques
2118:10.1093/biostatistics/kxx053
853:10.1016/j.molcel.2015.04.005
404:Principal component analysis
390:Principal component analysis
258:unique molecular identifiers
87:transcriptomics technologies
20:Single-cell transcriptomics
16:Analysis technique of genes
2397:
2315:10.1038/s41467-023-43590-8
2265:10.1016/j.cell.2019.05.031
1091:10.1016/j.bbrc.2003.11.177
946:10.1016/j.cels.2016.09.002
469:
313:component of transcription
236:next generation sequencing
2011:10.1109/BIBM.2016.7822521
1746:10.1186/s13059-016-1077-y
1626:10.1186/s13059-015-0805-z
1567:10.1186/s13059-016-0970-8
662:: F1000 Faculty Rev–182.
1175:Nature Reviews. Genetics
711:Biomarkers in Toxicology
437:Gene ontology enrichment
386:Dimensionality reduction
373:Dimensionality reduction
304:gene regulatory networks
1450:Nature Reviews Genetics
990:Single Cell Discoveries
793:Nature Reviews Genetics
610:10.3389/fonc.2015.00053
466:Pseudotemporal ordering
420:Differential expression
358:Hierarchical clustering
166:Quantitative PCR (qPCR)
1948:Nucleic Acids Research
1725:Kendziorski, Christina
560:Single-cell sequencing
489:of the data, builds a
481:
382:
338:
330:
309:Classification of the
220:
133:Cytoplasmic aspiration
119:
110:Isolating single cells
2302:Nature Communications
1244:10.1101/gr.121095.111
1016:Nature Communications
752:10.1101/gr.190595.115
597:Frontiers in Oncology
491:minimal spanning tree
479:
380:
336:
329:K-Means-Gaussian-data
328:
256:Another control uses
218:
117:
104:reverse transcription
2204:Nature Biotechnology
2155:Nature Biotechnology
2005:. pp. 217–222.
1888:Nature Biotechnology
1505:Nature Biotechnology
555:Single-cell analysis
472:Trajectory inference
413:principal components
2049:Nature Cell Biology
1961:10.1093/nar/gkac412
1692:10.1038/nature13173
1684:2014Natur.509..371T
1132:(1): 202–206, 208.
1037:10.1038/ncomms14049
1028:2017NatCo...814049Z
986:"SORT-seq Archives"
878:Nature Neuroscience
388:algorithms such as
211:Single-cell RNA-seq
1810:10.1038/nmeth.3971
1727:(1 January 2016).
1409:10.1038/nmeth.4220
1356:10.1038/nmeth.2967
1301:10.1038/nmeth.2772
517:mutual information
515:relationships and
482:
454:Biological process
451:Cellular component
448:Molecular function
383:
354:K-means clustering
339:
331:
221:
219:RNA Seq Experiment
196:reporter molecules
180:housekeeping genes
120:
94:Experimental steps
2020:978-1-5090-1611-2
1678:(7500): 371–375.
1139:10.2144/01301dd04
940:(4): 385–394.e3.
746:(10): 1491–1498.
498:Network inference
398:dimensional space
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2259:(7): 1888–1902.
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2216:10.1038/nbt.4096
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2167:10.1038/nbt.4091
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1900:10.1038/nbt.2859
1879:
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1518:10.1038/nbt.3102
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192:Fluorescent dyes
64:quantitative PCR
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2395:
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1941:
1940:
1933:
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1876:
1867:
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1665:
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1660:
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1462:10.1038/nrg3833
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1225:
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1167:
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740:Genome Research
733:
732:
728:
721:
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570:Transcriptomics
546:
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439:
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375:
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80:differentiation
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2373:
2371:DNA sequencing
2363:
2362:
2359:
2358:
2352:
2345:
2344:External links
2342:
2340:
2339:
2288:
2239:
2210:(5): 421–427.
2190:
2161:(5): 421–427.
2141:
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2092:
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2034:
2019:
1993:
1931:
1894:(4): 381–386.
1874:
1862:10.1101/276907
1839:
1798:Nature Methods
1778:
1733:Genome Biology
1715:
1658:
1613:Genome Biology
1599:
1554:Genome Biology
1540:
1511:(2): 155–160.
1491:
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1440:
1401:Nature Methods
1387:
1350:(7): 740–742.
1344:Nature Methods
1330:
1295:(2): 163–166.
1289:Nature Methods
1275:
1218:
1161:
1112:
1085:(4): 856–862.
1069:
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840:Molecular Cell
826:
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268:
267:Considerations
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128:Micropipetting
111:
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60:Northern blots
50:(RNA-seq) and
48:RNA sequencing
43:
40:
15:
13:
10:
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6:
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2:
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2381:Biotechnology
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2106:Biostatistics
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2066:
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656:F1000Research
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565:Transcriptome
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530:batch effects
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302:Inference of
301:
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287:Data analysis
286:
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33:
32:messenger RNA
29:
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1852:
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989:
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934:Cell Systems
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364:Biclustering
351:
347:biclustering
340:
316:
294:
290:
274:
270:
262:
255:
240:
222:
194:are used as
169:
160:
155:Microfluidic
143:
121:
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18:
2308:(1): 7781.
1954:(15): e86.
534:confounding
523:Integration
509:correlation
225:single-cell
74:discovery.
56:transcripts
52:microarrays
2365:Categories
1868:2018-03-12
1855:: 276907.
1739:(1): 222.
1560:(1): 112.
995:2022-11-15
576:References
513:non-linear
392:(PCA) and
342:Clustering
321:Clustering
311:stochastic
277:endogenous
42:Background
2069:1465-7392
1970:0305-1048
1908:1087-0156
1818:1548-7091
1755:1474-760X
1635:1474-760X
1576:1474-7596
1527:1087-0156
1486:205486032
1470:1471-0056
1417:1548-7105
1364:1548-7091
1309:1548-7091
1252:1088-9051
1195:1471-0056
1148:0736-6205
1099:0006-291X
1046:2041-1723
954:2405-4712
898:1097-6256
821:205486032
760:1549-5469
678:2046-1402
619:2234-943X
243:extrinsic
72:biomarker
2334:38012145
2325:10682386
2283:31178118
2234:29608179
2185:29608177
2136:29121214
2087:23524953
2029:27737735
1988:35639499
1926:24658644
1826:27571553
1773:27782827
1710:24739965
1653:26527291
1594:27230763
1535:25599176
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1435:28263961
1382:24836921
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1270:21816910
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1107:14706621
1064:28091601
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914:14461377
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862:26000846
813:25628217
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696:26949524
637:25806353
544:See also
431:unimodal
410:variance
200:amplicon
2274:6687398
2225:6700744
2176:6152897
2127:6215955
2078:3796878
1979:9410915
1917:4122333
1853:bioRxiv
1834:3594049
1764:5080738
1701:4145853
1680:Bibcode
1644:4630968
1619:: 241.
1585:4881296
1426:5376499
1373:4112276
1325:6765530
1261:3166838
1204:2949280
1055:5241818
1024:Bibcode
963:5092539
769:4579334
687:4758375
628:4354386
550:RNA-Seq
427:Bimodal
228:RNA-seq
172:primers
157:devices
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247:lysate
186:and α-
151:(FACS)
100:lysate
68:assays
2025:S2CID
1830:S2CID
1794:(PDF)
1482:S2CID
1321:S2CID
910:S2CID
817:S2CID
504:nodes
394:t-SNE
188:actin
184:GAPDH
28:cells
2330:PMID
2279:PMID
2253:Cell
2230:PMID
2181:PMID
2132:PMID
2083:PMID
2065:ISSN
2015:ISBN
1984:PMID
1966:ISSN
1922:PMID
1904:ISSN
1822:PMID
1814:ISSN
1769:PMID
1751:ISSN
1706:PMID
1649:PMID
1631:ISSN
1590:PMID
1572:ISSN
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1523:ISSN
1474:PMID
1466:ISSN
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1413:ISSN
1378:PMID
1360:ISSN
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1266:PMID
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1191:ISSN
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1103:PMID
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1060:PMID
1042:ISSN
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950:ISSN
902:PMID
894:ISSN
858:PMID
809:PMID
774:PMID
756:ISSN
715:ISBN
692:PMID
674:ISSN
633:PMID
615:ISSN
429:vs.
251:mRNA
232:cDNA
223:The
24:gene
2320:PMC
2310:doi
2269:PMC
2261:doi
2257:177
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2212:doi
2171:PMC
2163:doi
2122:PMC
2114:doi
2073:PMC
2057:doi
2007:doi
1974:PMC
1956:doi
1912:PMC
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1759:PMC
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