The DOI matches its Crossref record; 61 of the 62 references it cites were found in a registry or an index, and 1 are not checked yet; none of the 30 sources its claims cite is checked for support yet.
Comparative Analysis of Single-Cell RNA Sequencing Methods
Open accessPublished version of a preprint
Abstract
Single-cell RNA sequencing (scRNA-seq) offers new possibilities to address biological and medical questions. However, systematic comparisons of the performance of diverse scRNA-seq protocols are lacking. We generated data from 583 mouse embryonic stem cells to evaluate six prominent scRNA-seq methods: CEL-seq2, Drop-seq, MARS-seq, SCRB-seq, Smart-seq, and Smart-seq2. While Smart-seq2 detected the most genes per cell and across cells, CEL-seq2, Drop-seq, MARS-seq, and SCRB-seq quantified mRNA levels with less amplification noise due to the use of unique molecular identifiers (UMIs). Power simulations at different sequencing depths showed that Drop-seq is more cost-efficient for transcriptome quantification of large numbers of cells, while MARS-seq, SCRB-seq, and Smart-seq2 are more efficient when analyzing fewer cells. Our quantitative comparison offers the basis for an informed choice among six prominent scRNA-seq methods, and it provides a framework for benchmarking further improvements of scRNA-seq protocols.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“That amplification noise can be a major factor is seen by the strong increase of extra Poisson variability when ignoring UMIs and considering read counts only (Figure 5B, left; Figure S7A).”
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In the paper
“A 50% detection probability was reached at 7, 11, 14, 16, 17, and 28 ERCC molecules for Smart-seq2, Smart-seq/C1, CEL-seq2/C1, SCRB-seq, Drop-seq, and MARS-seq, respectively (Figure S4C).”
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In the paper
“Hence, among the UMI methods, CEL-seq2/C1 and SCRB-seq libraries had the highest complexity of mRNA molecules”
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In the paper
“As expected, the 3 0 counting methods showed a strong bias of reads mapped to the 3 0 end (Figure S3D).”
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In the paper
“cell (Figure 3C). Smart-seq2 detectedthe highestnumberofgenes per cell with a median of 9,138.”
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In the paper
“While Smartseq2 was the most sensitive method in both cases”
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In the paper
“This was possible for 96.5% (MARS-seq) to 98.9% (Smart-seq2) of all the 13,361 genes.”
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In the paper
“Overall, between 48% (Smart-seq2) and 30% (Smart-seq/C1) of all reads were exonic and, thus, were used to quantify gene expression levels.”
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In the paper
“In particular, the deep sequencing of Smart-seq2 libraries showed clearly that the number of detected genes did not change when increasing the sequencing depth from one million to five million reads per cell (Figure S2B).”
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In the paper
“As expected, accuracy was worse for narrower and especially for lower concentration ranges (Figure S5C).”
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In the paper
“Taking the number of detected genes per cell as a measure of sensitivity, we found that Drop-seq and MARS-seq had the lowest sensitivity, with a median of 4,811 and 4,763 genes detected per cell, respectively”
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In the paper
“A general difference in expression values between 3 0 counting and full-length methods also was reflected in their strong separation by the first principal component, explaining 37% of the total variance”
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In the paper
“while CEL-seq2/C1, SCRB-seq, and Smartseq/C1 detected a median of 7,536, 7,906, and 7,572 genes per”
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In the paper
“While the majority of genes ( 13,000) were detected by all methods, 400 genes were specific to each of the 3 0 counting methods, and 1,000 genes were specific to each of the two full-length methods (Figure S3A).”
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In the paper
“Drop-seq performed better and SCRB-seq and MARS-seq performed worse when using ERCCs.”
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In the paper
“To compare the total number of genes detected across many cells, we pooled the sequence data of 65 cells per method, and we detected 19,000 genes for CEL-Seq2/C1, 17,000 for MARS-seq, 18,000 for Drop-seq and SCRB-Seq, 20,000 for Smart-seq/C1, and 21,000 for Smart-seq2 (Figure 3D).”
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In the paper
“As expected from the number of detected genes per cell (Figure 3C), MARS-seq had the highest median dropout probability (74%) and Smart-seq2 had the lowest (26%) (Figure 5A).”
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In the paper
“When we stratified our analysis (considering UMIs) across five bins of expression levels, the ranking of methods was recapitulated and showed that the lowest expression bin strongly limited the TPR in all methods (Figure S9B).”
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In the paper
“Using minimal cost calculations, we find that Drop-seq is the most cost-effective method, closely followed by SCRB-seq, MARS-seq, and Smart-seq2.”
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In the paper
“While Smart-seq2 and CEL-seq2/C1 needed just 1.3-fold more cells at 0.25 million reads than at one million reads to reach 80% power, SCRB-seq and Drop-seq required 2.6-fold morecells (Table 1).”
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In the paper
“In summary, we find that Drop-seq, SCRB-seq, and MARS-seq are the most cost-effective methods, closely followed by Smart-seq2, if using an in-house-produced transposase.”
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In the paper
“In summary, SCRB-seq is the most powerful method at one million reads and half a million reads, but CELseq2/C1 is the most powerful method at a sequencing depth of 250,000 reads.”
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In the paper
“Calculating minimal costs this way, Drop-seq (690$) is the most cost-effective method when sequencing 254 cells at a depth of 250,000 reads, and SCRB-seq (810$), MARS-seq (820$), and Smart-seq2 (1,090$) are slightly more expensive at the same performance (Table 1).”
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- The authors state this as an assumption: Sequencing costs are the same for all methods.
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In the paper
“The p value of that difference was lowest for SCRB-seq, the most powerful method, and highest for Smartseq/C1, the least powerful method (Figure S10D).”
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In the paper
“Overall, the decrease in power was moderate (Figure 6C; Table 1) and followed the drop in sensitivity at different sequencing depths (Figure 3B).”
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In the paper
“CEL-seq2/C1, Drop-seq, MARS-seq, and Smart-seq2 performed slightly worse, reaching 80% power with 86, 99, 110, and 95 cells per group, respectively, while Smart-seq/C1 needed 150 cells to reach 80% power (Figure 6A).”
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In the paper
“In our comparison, we clearly find that Smart-seq2 is the most sensitive method, closely followed by SCRB-seq, Smart-seq/C1, and CEL-seq2/C1, while Drop-seq and MARSseq detect nearly 50% fewer genes per cell (Figures 3B and 3C).”
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In the paper
“TPRs differed considerably among methods and SCRB-seq performed best, reaching a median TPR of 80% with 64 cells.”
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In the paper
“Our simulations show that, at a sequencing depth of one million reads, SCRB-seq has the highest power, probably due to a good balance of high sensitivity and low amplification noise.”
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In the paper
“We used mESCs cultured under two inhibitor/leukemia inhibitory factor (2i/LIF) conditions to obtain a relatively homogeneous cell population (Gr € un et al., 2014; Kolodziejczyk et al., 2015b), so that biological variation was similar among experiments and, hence, we mainly compared technical variation.”
What the sources say
- Not checked yetcites Gr € un et al., 2014
- Not checked yetcites Kolodziejczyk et al., 2015b
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In the paper
“This extra Poisson variability includes biological variation (assumed to be the same across methods in our data) and technical variation, and the latter includes noise introduced by amplification (Brennecke et al., 2013; Gr € un et al., 2014; Stegle et al., 2015).”
What the sources say
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In the paper
“Indeed, a mixture model including a dropout probability and a negative binomial distribution, modeling the overdispersion in the count data, have been shown to represent scRNA-seq data better than the negative binomial alone (Finak et al., 2015; Kharchenko et al., 2014).”
What the sources say
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In the paper
“Hence, it seems that full-length methods turn a slightly higher fraction of transcripts into sequenceable molecules than 3 0 counting methods and are more sensitive in this respect.”
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In the paper
“Among the 3 0 counting methods, CEL-seq2/C1 and SCRB-seq detect about as many genes per cell as Smart-seq/C1, whereas Drop-seq and MARS-seq detect considerably fewer genes.”
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In the paper
“This suggests that they are unlikely to remain method specific at higher expression levels and that their impact on conclusions drawn from scRNAseq data is rather limited (Lun et al., 2016).”
What the sources say
- Not checked yetcites Lun et al., 2016 source
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In the paper
“Furthermore, the Smart-seq protocol has been optimized for sensitivity, more even full-length coverage, accuracy, and cost (Picelli et al., 2013)”
What the sources say
- Not checked yetcites Picelli et al., 2013 source
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In the paper
“In summary, we find that Smart-seq2 is the most sensitive method, as it detects the highest number of genes per cell and the most genes in total across cells and has the most even coverage across transcripts.”
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In the paper
“Utilization of UMI information improves quantification of mRNA molecules (Gr € un et al., 2014; Islam et al., 2014)”
What the sources say
- Not checked yetcites Gr € un et al., 2014
- Not checked yetcites Islam et al., 2014 source
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In the paper
“Smart-seq/C1 is slightly less sensitive per cell and detects almost the same number of genes across cells with slightly less even coverage.”
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In the paper
“Other protocols have sacrificed full-length coverage in order to sequence part of the primer used for cDNA generation. This enables early barcoding of libraries (i.e., the incorporation of cell-specific barcodes), allowing for multiplexing the cDNA amplification and thereby increasing the throughput of scRNAseq library generation by one to three orders of magnitude (Hashimshony et al., 2012; Jaiti”
What the sources say
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None of the sources cited for this claim have been checked yet.
In the paper
“and this improved Smart-seq2 protocol (Picelli et al., 2014b) has also become widely used (Gokce et al., 2016; Reinius et al., 2016; Tirosh et al., 2016).”
What the sources say
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None of the sources cited for this claim have been checked yet.
In the paper
“For example, it has previously been shown that scRNA-seq conducted in the small volumes available in the automated microfluidic platform from Fluidigm (C1 platform) outperforms CEL-seq2, Smart-seq, or other commercially available kits in microliter volumes (Hashimshony et al., 2016; Wu et al., 2014).”
What the sources say
- Not checked yetcites Hashimshony et al., 2016 source
- Not checked yetcites Wu et al., 2014
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In the paper
“To this end, we generated data for all six compared methods from the same cells, cultured under the same condition in the same laboratory.”
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In the paper
“Hence, our power simulation framework considers the full range of dropout rates and is not biased against more sensitive methods.”
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In the paper
“successful establishment of the method in our lab was confirmed by a species-mixing experiment (Figure S1A).”
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In the paper
“These estimates were very similar among our technical replicates, and, hence, our method comparison is valid with respect to batch variations (Figures S10B-S10D).”
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In the paper
“J1 embryonic stem cells were obtained from E. Li and T. Chen and mycoplasma free determined by a PCR-based test.”
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In the paper
“Furthermore, the presence of batch effects shows that experiments need to be designed in a way that does not confound batches with biological factors (Hicks et al., 2015).”
What the sources say
- Not checked yetcites Hicks et al., 2015
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In summary, we systematically compared six prominent scRNA-seq methods and found that Drop-seq is preferable when quantifying transcriptomes of large numbers of cells with low sequencing depth, SCRB-seq and MARS-seq is preferable when quantifying transcriptomes of fewer cells, and Smart-seq2 is preferable when annotating and/or quantifying transcriptomes of fewer cells as long one can use in-house”
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In the paper
“Cell line authentication was not recently performed.”
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. The quote only partly states this claim.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“We trimmed cDNA reads to the same length and processed raw reads in the same way as data sequenced for this study.”
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What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Similar sequencing qualities were confirmed by FastQC v0.10.1 (Figure S1B).”
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
The paper relies on this without support.
A thorough and systematic comparison of relevant parameters across scRNA-seq methods is still lacking.
“However, a thorough and systematic comparison of relevant parameters across scRNA-seq methods is still lacking.”
“However, systematic comparisons of the performance of diverse scRNA-seq protocols are lacking.”
The authors state this as an assumption.
Differences in total variation among methods are due to differences in their technical variation.
“Hence, we can assume that differences in the total variation among methods are due to differences in their technical variation.”
The paper relies on this without support.
The amount of biological variation is the same in cells analyzed by each of the six methods.
“As we used the same cell type under the same culture conditions for all methods, the amount of biological variation should be the same in the cells analyzed by each of the six methods.”
The authors state this as an assumption.
Sequencing costs are the same for all methods.
“we assume that sequencing costs are the same for all methods.”
The authors state this as an assumption.
Detections of ERCC molecules are independent from each other and can be regarded as independent Bernoulli trials.
“Detections are independent from each other and are thus regarded as independent Bernoulli trials.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Combinatorial indexing and droplet-based methods have higher throughput and lower cost per cell, whereas plate-based methods provide more sensitive gene detection and are more customizable (Ziegenhain et al., 2017).”
From Single-Cell Sequencing of Brain Cell Transcriptomes and Epigenomes (2021) · cites this paper as Ziegenhain et al., 2017
In the citing paper
“In scRNA-seq experiments, individual cells are typically not sequenced to saturation, leading to sparse count matrices, where only highly expressed genes are detected with counts greater than zero.”
From scPower accelerates and optimizes the design of multi-sample single cell transcriptomic studies (2021) · cites this paper as [36]
In the citing paper
“Specifically, we found the optimal read depths to be ~10,000 in most evaluations, which is relatively low compared to previous recommendations34–36,80,81.”
From scPower accelerates and optimizes the design of multi-sample single cell transcriptomic studies (2021) · cites this paper as [36]
In the citing paper
“Furthermore, scRNA‐seq techniques can be divided into full‐length and 3′ enrichment methods (Svensson et al, 2017; Ziegenhain et al, 2017).”
From Current best practices in single-cell RNA-seq analysis: a tutorial (2019) · cites this paper as Ziegenhain et al, 2017
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Commercial Smart-seq2 total conflicts with the stated cost calculation
MajorAt one million reads, 95 cells per group means 190 libraries. At the listed $30 per library and $5 per million reads, the stated calculation gives $6,650, not the table's $10,470; the other two commercial Smart-seq2 totals are also too high under that calculation.
“| Smart-seq2 (commercial) | 0.8 | 5.2 | 95/105/128 | 30 | 10,470/11,040/13,160 |”
“c Assuming $5 per one million reads.”
Citations
Cites
STAR: ultrafast universal RNA-seq aligner
Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., & Gingeras, T. R. (2013) · Bioinformatics · doi:10.1093/bioinformatics/bts635 · Cited by 51,709
limma powers differential expression analyses for RNA-sequencing and microarray studies
Ritchie, M. E., Phipson, B., Wu, D., Hu, Y., Law, C. W., Shi, W., & Smyth, G. K. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv007 · Cited by 40,458
Cutadapt removes adapter sequences from high-throughput sequencing reads
Martin, M. (2011) · EMBnet.journal · doi:10.14806/ej.17.1.200 · Cited by 33,889
An integrated encyclopedia of DNA elements in the human genome
The ENCODE Project Consortium (2012) · Nature · doi:10.1038/nature11247 · Cited by 18,072
Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets.
Macosko, E. Z., Basu, A., Satija, R., Nemesh, J., Shekhar, K., Goldman, M., Tirosh, I., Bialas, A. R., Kamitaki, N., Martersteck, E. M., et al. (2015) · Cell · doi:10.1016/j.cell.2015.05.002 · Cited by 7,110
voom: precision weights unlock linear model analysis tools for RNA-seq read counts
Law, C. W., Chen, Y., Shi, W., & Smyth, G. K. (2014) · Genome Biology · doi:10.1186/gb-2014-15-2-r29 · Cited by 6,155
Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq
Tirosh, I., Izar, B., Prakadan, S. M., Wadsworth, M. H., Treacy, D., Trombetta, J. J., Rotem, A., Rodman, C., Lian, C., Murphy, G., et al. (2016) · Science · doi:10.1126/science.aad0501 · Cited by 4,944
Full-length RNA-seq from single cells using Smart-seq2
Picelli, S., Faridani, O. R., Björklund, Å. K., Winberg, G., Sagasser, S., & Sandberg, R. (2014) · Nature Protocols · doi:10.1038/nprot.2014.006 · Cited by 4,137
Targeted mutation of the DNA methyltransferase gene results in embryonic lethality
Li, E., Bestor, T. H., & Jaenisch, R. (1992) · Cell · doi:10.1016/0092-8674(92)90611-f · Cited by 3,984
Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells
Klein, A. M., Mazutis, L., Akartuna, I., Tallapragada, N., Veres, A., Li, V., Peshkin, L., Weitz, D. A., & Kirschner, M. W. (2015) · Cell · doi:10.1016/j.cell.2015.04.044 · Cited by 3,356
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- Hicks, 2015, On the widespread and critical impact of systematic bias and batch effects in single-cell RNA-Seq data, bioRxiv
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Sorted by citations
Integrating single-cell transcriptomic data across different conditions, technologies, and species
Butler, A., Hoffman, P., Smibert, P., Papalexi, E., & Satija, R. (2018) · Nature Biotechnology · doi:10.1038/nbt.4096 · Cited by 13,174
McGinnis, C. S., Murrow, L. M., & Gartner, Z. J. (2019) · Cell Systems · doi:10.1016/j.cels.2019.03.003 · Cited by 4,521
Determining cell type abundance and expression from bulk tissues with digital cytometry
Newman, A. M., Steen, C. B., Liu, C. L., Gentles, A. J., Chaudhuri, A. A., Scherer, F., Khodadoust, M. S., Esfahani, M. S., Luca, B. A., Steiner, D. F., et al. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0114-2 · Cited by 4,586
Wound Healing: A Cellular Perspective
Rodrigues, M., Kosaric, N., Bonham, C. A., & Gurtner, G. C. (2018) · Physiological Reviews · doi:10.1152/physrev.00067.2017 · Cited by 3,211
Regev, A., Teichmann, S. A., Lander, E. S., Amit, I., Benoist, C., Birney, E., Bodenmiller, B., Campbell, P., Carninci, P., Clatworthy, M., et al. (2017) · eLife · doi:10.7554/elife.27041 · Cited by 2,232
Self-citationCurrent best practices in single-cell RNA-seq analysis: a tutorial
Luecken, M. D., & Theis, F. J. (2019) · Molecular Systems Biology · doi:10.15252/msb.20188746 · Cited by 2,231
RNA sequencing: the teenage years
Stark, R., Grzelak, M., & Hadfield, J. (2019) · Nature Reviews Genetics · doi:10.1038/s41576-019-0150-2 · Cited by 2,041
Mapping the Mouse Cell Atlas by Microwell-Seq
Han, X., Wang, R., Zhou, Y., Fei, L., Sun, H., Lai, S., Saadatpour, A., Zhou, Z., Chen, H., Ye, F., et al. (2018) · Cell · doi:10.1016/j.cell.2018.02.001 · Cited by 1,597
Landscape and Dynamics of Single Immune Cells in Hepatocellular Carcinoma
Zhang, Q., He, Y., Luo, N., Patel, S. J., Han, Y., Gao, R., Modak, M., Carotta, S., Haslinger, C., Kind, D., et al. (2019) · Cell · doi:10.1016/j.cell.2019.10.003 · Cited by 1,624
Single-cell transcriptional diversity is a hallmark of developmental potential
Gulati, G. S., Sikandar, S. S., Wesche, D. J., Manjunath, A., Bharadwaj, A., Berger, M. J., Ilagan, F., Kuo, A. H., Hsieh, R. W., Cai, S., et al. (2020) · Science · doi:10.1126/science.aax0249 · Cited by 1,571
Regev, A., Teichmann, S. A., Lander, E. S., Amit, I., Benoist, C. O., Birney, E., Bodenmiller, B., & al, E. (2017) · Zurich Open Repository and Archive (University of Zurich) · doi:10.5167/uzh-162597 · Cited by 1,457
Multiplexed droplet single-cell RNA-sequencing using natural genetic variation
Kang, H. M., Subramaniam, M., Targ, S., Nguyen, M., Maliskova, L., McCarthy, E., Wan, E., Wong, S., Byrnes, L., Lanata, C. M., et al. (2018) · Nature Biotechnology · doi:10.1038/nbt.4042 · Cited by 1,101
Hu, C., Li, T., Xu, Y., Zhang, X., Li, F., Bai, J., Chen, J., Jiang, W., Yang, K., Ou, Q., et al. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac947 · Cited by 1,373
Single‐cell RNA sequencing technologies and applications: A brief overview
Jovic, D., Liang, X., Zeng, H., Lin, L., Xu, F., & Luo, Y. (2022) · Clinical and Translational Medicine · doi:10.1002/ctm2.694 · Cited by 1,379
Massively parallel single-nucleus RNA-seq with DroNc-seq
Habib, N., Avraham-Davidi, I., Basu, A., Burks, T., Shekhar, K., Hofree, M., Choudhury, S. R., Aguet, F., Gelfand, E., Ardlie, K., et al. (2017) · Nature Methods · doi:10.1038/nmeth.4407 · Cited by 1,069
Li, Q., Cheng, Z., Zhou, L., Darmanis, S., Neff, N. F., Okamoto, J., Gulati, G., Bennett, M. L., Sun, L. O., Clarke, L. E., et al. (2019) · Neuron · doi:10.1016/j.neuron.2018.12.006 · Cited by 1,129
A practical guide to single-cell RNA-sequencing for biomedical research and clinical applications
Haque, A., Engel, J., Teichmann, S. A., & Lönnberg, T. (2017) · Genome Medicine · doi:10.1186/s13073-017-0467-4 · Cited by 1,071
Moncada, R., Barkley, D., Wagner, F., Chiodin, M., Devlin, J. C., Baron, M., Hajdu, C. H., Simeone, D. M., & Yanai, I. (2020) · Nature Biotechnology · doi:10.1038/s41587-019-0392-8 · Cited by 1,045
Systematic comparison of single-cell and single-nucleus RNA-sequencing methods
Ding, J., Adiconis, X., Simmons, S. K., Kowalczyk, M. S., Hession, C. C., Marjanovic, N. D., Hughes, T. K., Wadsworth, M. H., Burks, T., Nguyen, L. T., et al. (2020) · Nature Biotechnology · doi:10.1038/s41587-020-0465-8 · Cited by 871
Guidelines for the use of flow cytometry and cell sorting in immunological studies (second edition)
Cossarizza, A., Chang, H., Radbruch, A., Acs, A., Adam, D., Adam‐Klages, S., Agace, W. W., Aghaeepour, N., Akdiş, M., Allez, M., et al. (2019) · European Journal of Immunology · doi:10.1002/eji.201970107 · Cited by 1,007
Neuronal cell-type classification: challenges, opportunities and the path forward
Zeng, H., & Sanes, J. R. (2017) · Nature Reviews Neuroscience · doi:10.1038/nrn.2017.85 · Cited by 841
Single-Cell RNA-Seq Technologies and Related Computational Data Analysis
Chen, G., Ning, B., & Shi, T. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00317 · Cited by 966
Single-cell transcriptomes from human kidneys reveal the cellular identity of renal tumors
Young, M. D., Mitchell, T. J., Vieira Braga, F. A., Tran, M. G. B., Stewart, B. J., Ferdinand, J. R., Collord, G., Botting, R. A., Popescu, D.-M., Loudon, K. W., et al. (2018) · Science · doi:10.1126/science.aat1699 · Cited by 762
Wu, H., Kirita, Y., Donnelly, E. L., & Humphreys, B. D. (2019) · Journal of the American Society of Nephrology · doi:10.1681/asn.2018090912 · Cited by 752
Comparison and evaluation of statistical error models for scRNA-seq
Choudhary, S., & Satija, R. (2022) · Genome Biology · doi:10.1186/s13059-021-02584-9 · Cited by 634
Single-cell RNA counting at allele and isoform resolution using Smart-seq3
Hagemann-Jensen, M., Ziegenhain, C., Chen, P., Ramsköld, D., Hendriks, G.-J., Larsson, A. J. M., Faridani, O. R., & Sandberg, R. (2020) · Nature Biotechnology · doi:10.1038/s41587-020-0497-0 · Cited by 700
Self-citationAn atlas of the aging lung mapped by single cell transcriptomics and deep tissue proteomics
Angelidis, I., Simon, L. M., Fernandez, I. E., Strunz, M., Mayr, C. H., Greiffo, F. R., Tsitsiridis, G., Ansari, M., Graf, E., Strom, T.-M., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-08831-9 · Cited by 637
Xu, C., Lopez, R., Mehlman, E., Regier, J., Jordan, M. I., & Yosef, N. (2021) · Molecular Systems Biology · doi:10.15252/msb.20209620 · Cited by 691
“A second scenario is that of similar tissue but different technologies, which we expect to be more challenging as each technology comes with its own characteristics and biases (Ziegenhain et al , 2017).”
Decontamination of ambient RNA in single-cell RNA-seq with DecontX
Yang, S., Corbett, S. E., Koga, Y., Wang, Z., Johnson, W. E., Yajima, M., & Campbell, J. D. (2020) · Genome Biology · doi:10.1186/s13059-020-1950-6 · Cited by 635
Missing data and technical variability in single-cell RNA-sequencing experiments
Hicks, S. C., Townes, F. W., Teng, M., & Irizarry, R. A. (2018) · Biostatistics · doi:10.1093/biostatistics/kxx053 · Cited by 635
Applications of single-cell sequencing in cancer research: progress and perspectives
Lei, Y., Tang, R., Xu, J., Wang, W., Zhang, B., Liu, J., Yu, X., & Shi, S. (2021) · Journal of Hematology & Oncology · doi:10.1186/s13045-021-01105-2 · Cited by 610
Power analysis of single-cell RNA-sequencing experiments
Svensson, V., Natarajan, K. N., Ly, L.-H., Miragaia, R. J., Labalette, C., Macaulay, I. C., Cvejic, A., & Teichmann, S. A. (2017) · Nature Methods · doi:10.1038/nmeth.4220 · Cited by 568
Single-Cell RNA Sequencing Resolves Molecular Relationships Among Individual Plant Cells
Ryu, K. H., Huang, L., Kang, H. M., & Schiefelbein, J. (2019) · PLANT PHYSIOLOGY · doi:10.1104/pp.18.01482 · Cited by 529
Lineage Tracing in Humans Enabled by Mitochondrial Mutations and Single-Cell Genomics
Ludwig, L. S., Lareau, C. A., Ulirsch, J. C., Christian, E., Muus, C., Li, L. H., Pelka, K., Ge, W. H., Oren, Y., Brack, A., et al. (2019) · Cell · doi:10.1016/j.cell.2019.01.022 · Cited by 554
Genetic insights into the neurodevelopmental origins of schizophrenia
Birnbaum, R., & Weinberger, D. R. (2017) · Nature reviews. Neuroscience · doi:10.1038/nrn.2017.125 · Cited by 509
Benchmarking single-cell RNA-sequencing protocols for cell atlas projects
Mereu, E., Lafzi, A., Moutinho, C., Ziegenhain, C., McCarthy, D. J., Álvarez-Varela, A., Batlle, E., Sagar, Grün, D., Lau, J. K., et al. (2020) · Nature Biotechnology · doi:10.1038/s41587-020-0469-4 · Cited by 499
Self-citationMyeloid-derived suppressor cells: an emerging target for anticancer immunotherapy
Wu, Y., Yi, M., Niu, M., Mei, Q., & Wu, K. (2022) · Molecular Cancer · doi:10.1186/s12943-022-01657-y · Cited by 475
SABER amplifies FISH: enhanced multiplexed imaging of RNA and DNA in cells and tissues
Kishi, J. Y., Lapan, S. W., Beliveau, B. J., West, E. R., Zhu, A., Sasaki, H. M., Saka, S. K., Wang, Y., Cepko, C. L., & Yin, P. (2019) · Nature Methods · doi:10.1038/s41592-019-0404-0 · Cited by 446
Jew, B., Alvarez, M., Rahmani, E., Miao, Z., Ko, A., Garske, K. M., Sul, J. H., Pietiläinen, K. H., Pajukanta, P., & Halperin, E. (2020) · Nature Communications · doi:10.1038/s41467-020-15816-6 · Cited by 443
The emerging landscape of spatial profiling technologies
Moffitt, J. R., Lundberg, E., & Heyn, H. (2022) · Nature Reviews Genetics · doi:10.1038/s41576-022-00515-3 · Cited by 419
Self-citationByrne, A., Beaudin, A. E., Olsen, H. E., Jain, M., Cole, C., Palmer, T., DuBois, R. M., Forsberg, E. C., Akeson, M., & Vollmers, C. (2017) · Nature Communications · doi:10.1038/ncomms16027 · Cited by 379
Cell-cell communication: New insights and clinical implications
Su, J., Song, Y., Zhu, Z., Huang, X., Fan, J., Qiao, J., & Mao, F. (2024) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-024-01888-z · Cited by 433
MetaCell: analysis of single-cell RNA-seq data using K-nn graph partitions
Baran, Y., Bercovich, A., Sebé-Pedrós, A., Lubling, Y., Giladi, A., Chomsky, E., Meir, Z., Hoichman, M., Lifshitz, A., & Tanay, A. (2019) · Genome biology · doi:10.1186/s13059-019-1812-2 · Cited by 392
Single-cell RNA sequencing: Technical advancements and biological applications
Hedlund, E., & Deng, Q. (2017) · Molecular Aspects of Medicine · doi:10.1016/j.mam.2017.07.003 · Cited by 410
Bernard, V., Semaan, A., Huang, J. H., Lucas, F. A. S., Mulu, F. C., Stephens, B. M., Guerrero, P. A., Huang, Y., Zhao, J., Kamyabi, N., et al. (2018) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-18-1955 · Cited by 387
Comparative Analysis of Droplet-Based Ultra-High-Throughput Single-Cell RNA-Seq Systems
Zhang, X., Li, T., Liu, F., Chen, Y., Yao, J., Li, Z., Huang, Y., & Wang, J. (2018) · Molecular Cell · doi:10.1016/j.molcel.2018.10.020 · Cited by 395
Unravelling biology and shifting paradigms in cancer with single-cell sequencing
Baslan, T., & Hicks, J. B. (2017) · Nature reviews. Cancer · doi:10.1038/nrc.2017.58 · Cited by 382
Sárvári, A. K., Hauwaert, E. L. V., Markussen, L. K., Gammelmark, E., Marcher, A.-B., Ebbesen, M. F., Nielsen, R., Brewer, J. R., Madsen, J. G. S., & Mandrup, S. (2020) · Cell Metabolism · doi:10.1016/j.cmet.2020.12.004 · Cited by 377
zUMIs - A fast and flexible pipeline to process RNA sequencing data with UMIs
Parekh, S., Ziegenhain, C., Vieth, B., Enard, W., & Hellmann, I. (2018) · GigaScience · doi:10.1093/gigascience/giy059 · Cited by 364
Self-citationMeta-Analysis of Leukocyte Diversity in Atherosclerotic Mouse Aortas
Zernecke, A., Winkels, H., Cochain, C., Williams, J. W., Wolf, D., Soehnlein, O., Robbins, C. S., Monaco, C. G., Park, I., McNamara, C. A., et al. (2020) · Circulation Research · doi:10.1161/circresaha.120.316903 · Cited by 387
FateID infers cell fate bias in multipotent progenitors from single-cell RNA-seq data
Herman, J. S., Sagar, & Grün, D. (2018) · Nature Methods · doi:10.1038/nmeth.4662 · Cited by 363
Beyond bulk: a review of single cell transcriptomics methodologies and applications
Kulkarni, A., Anderson, A. G., Merullo, D. P., & Konopka, G. (2019) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2019.03.001 · Cited by 355
Cellular diversity in the Drosophila midbrain revealed by single-cell transcriptomics
Croset, V., Treiber, C. D., & Waddell, S. (2018) · eLife · doi:10.7554/elife.34550 · Cited by 299
Interpretable dimensionality reduction of single cell transcriptome data with deep generative models
Ding, J., Condon, A., & Shah, S. P. (2018) · Nature Communications · doi:10.1038/s41467-018-04368-5 · Cited by 337
Alternative splicing and related RNA binding proteins in human health and disease
Tao, Y., Zhang, Q., Wang, H., Yang, X., & Mu, H. (2024) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-024-01734-2 · Cited by 351
A single-cell analysis of the Arabidopsis vegetative shoot apex
Zhang, T., Chen, Y., & Wang, J. (2021) · Developmental Cell · doi:10.1016/j.devcel.2021.02.021 · Cited by 324
Direct Comparative Analyses of 10X Genomics Chromium and Smart-Seq2
Wang, X., He, Y., Zhang, Q., Ren, X., & Zhang, Z. (2021) · Genomics Proteomics & Bioinformatics · doi:10.1016/j.gpb.2020.02.005 · Cited by 343
Scaling by shrinking: empowering single-cell 'omics' with microfluidic devices
Prakadan, S. M., Shalek, A. K., & Weitz, D. A. (2017) · Nature Reviews Genetics · doi:10.1038/nrg.2017.15 · Cited by 335
The epigenetic basis of cellular heterogeneity
Carter, B. C., & Zhao, K. (2020) · Nature Reviews Genetics · doi:10.1038/s41576-020-00300-0 · Cited by 324
Singh, M., Al-Eryani, G., Carswell, S., Ferguson, J. M., Blackburn, J., Barton, K., Roden, D., Luciani, F., Giang Phan, T., Junankar, S., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-11049-4 · Cited by 308
scds: computational annotation of doublets in single-cell RNA sequencing data
Bais, A. S., & Kostka, D. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btz698 · Cited by 308
Tutorial: guidelines for the computational analysis of single-cell RNA sequencing data
Andrews, T. S., Kiselev, V. Y., McCarthy, D. J., & Hemberg, M. (2020) · Nature Protocols · doi:10.1038/s41596-020-00409-w · Cited by 323
Profiling cell identity and tissue architecture with single-cell and spatial transcriptomics
Gulati, G. S., D'Silva, J. P., Liu, Y., Wang, L., & Newman, A. M. (2024) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-024-00768-2 · Cited by 347
Dobnikar, L., Taylor, A. L., Chappell, J., Oldach, P., Harman, J. J., Oerton, E., Dzierzak, E. A., Bennett, M. R., Spivakov, M., & Jørgensen, H. F. (2018) · Nature Communications · doi:10.1038/s41467-018-06891-x · Cited by 298
Davis, R. T., Blake, K., Ma, D., Gabra, M. B. I., Hernandez, G. A., Phung, A. T., Yang, Y., Maurer, D., Lefebvre, A. E. Y. T., Alshetaiwi, H. S., et al. (2020) · Nature Cell Biology · doi:10.1038/s41556-020-0477-0 · Cited by 301
Prospects and challenges of multi-omics data integration in toxicology
Canzler, S., Schor, J., Busch, W., Schubert, K. A., Rolle-Kampczyk, U. E., Seitz, H., Kamp, H., von Bergen�, M., Buesen, R., & Hackermüller, J. (2020) · Archives of Toxicology · doi:10.1007/s00204-020-02656-y · Cited by 300
Cell type prioritization in single-cell data
Skinnider, M. A., Squair, J. W., Kathe, C., Anderson, M. A., Gautier, M., Matson, K. J. E., Milano, M., Hutson, T. H., Barraud, Q., Phillips, A. A., et al. (2021) · Nature Biotechnology · doi:10.1038/s41587-020-0605-1 · Cited by 274
Saviano, A., Henderson, N. C., & Baumert, T. F. (2020) · Journal of Hepatology · doi:10.1016/j.jhep.2020.06.004 · Cited by 276
Single-Cell Sequencing of Brain Cell Transcriptomes and Epigenomes
Armand, E. J., Li, J., Xie, F., Luo, C., & Mukamel, E. A. (2021) · Neuron · doi:10.1016/j.neuron.2020.12.010 · Cited by 241
A systematic evaluation of single cell RNA-seq analysis pipelines
Vieth, B., Parekh, S., Ziegenhain, C., Enard, W., & Hellmann, I. (2019) · Nature Communications · doi:10.1038/s41467-019-12266-7 · Cited by 265
Self-citationMapping gene regulatory networks from single-cell omics data
Fiers, M. W. E. J., Minnoye, L., Aibar, S., Bravo González-Blas, C., Kalender Atak, Z., & Aerts, S. (2018) · Briefings in Functional Genomics · doi:10.1093/bfgp/elx046 · Cited by 235
Ecker, J. R., Geschwind, D. H., Kriegstein, A. R., Ngai, J., Osten, P., Polioudakis, D., Regev, A., Šestan, N., Wickersham, I. R., & Zeng, H. (2017) · Neuron · doi:10.1016/j.neuron.2017.10.007 · Cited by 258
Tumor Cell Invasion in Glioblastoma
Vollmann‐Zwerenz, A., Leidgens, V., Feliciello, G., Klein, C. A., & Hau, P. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21061932 · Cited by 270
Clonal Decomposition and DNA Replication States Defined by Scaled Single-Cell Genome Sequencing
Laks, E., McPherson, A., Zahn, H., Lai, D., Steif, A., Brimhall, J., Biele, J., Wang, B., Masud, T., Ting, J., et al. (2019) · Cell · doi:10.1016/j.cell.2019.10.026 · Cited by 253
Sensitive and powerful single-cell RNA sequencing using mcSCRB-seq
Bagnoli, J., Ziegenhain, C., Janjic, A., Wange, L. E., Vieth, B., Parekh, S., Geuder, J., Hellmann, I., & Enard, W. (2018) · Nature Communications · doi:10.1038/s41467-018-05347-6 · Cited by 246
Self-citationThe impact of amplification on differential expression analyses by RNA-seq
Parekh, S., Ziegenhain, C., Vieth, B., Enard, W., & Hellmann, I. (2016) · Scientific Reports · doi:10.1038/srep25533 · Cited by 266
Self-citationSCDC: bulk gene expression deconvolution by multiple single-cell RNA sequencing references
Dong, M., Thennavan, A., Urrutia, E., Li, Y., Perou, C. M., Zou, F., & Jiang, Y. (2019) · Briefings in Bioinformatics · doi:10.1093/bib/bbz166 · Cited by 250
Gene expression variability across cells and species shapes innate immunity
Hagai, T., Chen, X., Miragaia, R. J., Rostom, R., Gomes, T., Kunowska, N., Henriksson, J., Park, J.-E., Proserpio, V., Donati, G., et al. (2018) · Nature · doi:10.1038/s41586-018-0657-2 · Cited by 220
Single-Cell RNA Sequencing Analysis: A Step-by-Step Overview
Slovin, S., Carissimo, A., Panariello, F., Grimaldi, A., Bouchè, V., Gambardella, G., & Cacchiarelli, D. (2021) · Methods in molecular biology · doi:10.1007/978-1-0716-1307-8_19 · Cited by 264
Identifying cell populations with scRNASeq
Andrews, T. S., & Hemberg, M. (2017) · Molecular Aspects of Medicine · doi:10.1016/j.mam.2017.07.002 · Cited by 247
Alpern, D., Gardeux, V., Russeil, J., Mangeat, B., Meireles-Filho, A. C. A., Breysse, R., Hacker, D. L., & Deplancke, B. (2019) · Genome biology · doi:10.1186/s13059-019-1671-x · Cited by 254
Live-seq enables temporal transcriptomic recording of single cells
Chen, W., Guillaume-Gentil, O. J., Rainer, P. Y., Gäbelein, C. G., Saelens, W., Gardeux, V., Klaeger, A., Dainese, R., Zachara, M., Zambelli, T., et al. (2022) · Nature · doi:10.1038/s41586-022-05046-9 · Cited by 272
Macrophage functional diversity in NAFLD — more than inflammation
Barreby, E., Chen, P., & Aouadi, M. (2022) · Nature Reviews Endocrinology · doi:10.1038/s41574-022-00675-6 · Cited by 251
Identification, discrimination and heterogeneity of fibroblasts
Lendahl, U., Muhl, L., & Betsholtz, C. (2022) · Nature Communications · doi:10.1038/s41467-022-30633-9 · Cited by 256
Revolutionizing immunology with single-cell RNA sequencing
Chen, H., Ye, F., & Guo, G. (2019) · Cellular and Molecular Immunology · doi:10.1038/s41423-019-0214-4 · Cited by 244
Multiplexed single-cell proteomics using SCoPE2
Petelski, A. A., Emmott, E., Leduc, A., Huffman, R. G., Specht, H., Perlman, D. H., & Slavov, N. (2021) · Nature Protocols · doi:10.1038/s41596-021-00616-z · Cited by 231
Observation weights unlock bulk RNA-seq tools for zero inflation and single-cell applications
Van den Berge, K., Perraudeau, F., Soneson, C., Love, M. I., Risso, D., Vert, J.-P., Robinson, M. D., Dudoit, S., & Clement, L. (2018) · Genome Biology · doi:10.1186/s13059-018-1406-4 · Cited by 228
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Self-citationGuo, M., Du, Y., Gokey, J. J., Ray, S., Bell, S. M., Adam, M., Sudha, P., Perl, A. T., Deshmukh, H. S., Potter, S. S., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-07770-1 · Cited by 209
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Metadata
- Authors
- Ziegenhain, C., Vieth, B., Parekh, S., Reinius, B., Guillaumet-Adkins, A., Smets, M., Leonhardt, H., Heyn, H., Hellmann, I., & Enard, W.
- Affiliations
- Ludwig-Maximilians-Universität München (DE)
- Ludwig Cancer Research (BE)
- Universitat Pompeu Fabra (ES)
- Center for Integrated Protein Science Munich (DE)
- Corresponding author
- Enard, Wolfgang
- ORCID
- Venue
- Molecular Cell
- Publisher
- Elsevier BV
- Published
- 2017-02
- Volume
- 65
- Issue
- 4
- Pages
- 631-643.e4
- ISSN
- 1097-2765 (ISSN-L)
- OpenAlex
- W2887910031
- Cited by
- 1,494
- References
- 62
- Citations by year
- 2026: 86 · 2025: 156 · 2024: 158 · 2023: 152 · 2022: 160 · 2021: 219 · 2020: 197 · 2019: 174 · 2018: 125 · 2017: 60
- FWCI
- 63.41
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Molecular Cell (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Repositori digital de la UPF (Universitat Pompeu Fabra) (publishedVersion, other-oa)
- RECERCAT (Consorci de Serveis Universitaris de Catalunya) (submittedVersion, other-oa)
- Annals of Hematology
- Funders
- Deutsche Forschungsgemeinschaft (DFG) (SFB1243, A01/A14/A15)
- Boehringer Ingelheim Fonds
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- single-cell RNA sequencing, mouse embryonic stem cells, Drop-seq, SmartSeq2, SMART-seq, unique molecular identifiers, sequencing depth, power simulation, cost efficiency, transcriptome quantification
- MeSH terms
- Animals, Base Sequence, Cell Line, Computer Simulation, Cost-Benefit Analysis, RNA, Sequence Analysis, RNA, Models, Economic, Mice, Embryonic Stem Cells, Single-Cell Analysis, High-Throughput Nucleotide Sequencing
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