ライフサイエンスコーパス: scRNA-seq

1 scRNA-seq analysis of the organoids revealed enhanced GA

2 scRNA-seq data have two unique challenges that can affec

3 scRNA-seq may improve our understanding of complicated h

4 scRNA-seq of BAL cells from lung transplant recipients i

5 scRNA-seq of cancer cells illuminated targetable oncogen

6 scRNA-seq revealed that both TREM2 deletion and anti-TRE

7 analyzing a mixture sample sequenced with 13 scRNA-seq protocols.

8 We tested SAME-clustering across 15 scRNA-seq datasets generated by different platforms, wit

9 re, we perform a systematic evaluation of 18 scRNA-seq imputation methods to assess their accuracy an

10 d on canonical correlation analysis of 5,265 scRNA-seq profiles, we identified 18 unique cell populat

11 ticate putative cell types discovered from a scRNA-seq dataset.

12 In a scRNA-seq glioblastoma dataset, we discover a recurrent

13 Here, we present a scRNA-seq platform, named Paired-seq, with high cells/be

14 Additionally, scRNA-seq datasets often contain technical sources of no

15 Although scRNA-seq is now ubiquitously adopted in studies of intr

16 orm and software selection when designing an scRNA-seq study.

17 As a result, analyzing scRNA-seq data requires extensive considerations of prog

18 logy, and function, we used bulk RNA-seq and scRNA-seq to interrogate the developing human intestine,

19 A method for combined lineage tracing and scRNA-seq reveals the interplay between complementary ge

20 Benchmarking using expert-annotated scRNA-seq datasets shows that our method automatically i

21 mal tissues based on bulk RNA-seq as well as scRNA-seq data.

22 Review, we summarize the currently available scRNA-seq technologies and analytical tools and discuss

23 We demonstrate using publicly available scRNA-seq datasets and simulated expression data that ba

24 proposed method on eight publicly available scRNA-seq datasets with known cell types as well as five

25 is corroborated in other publicly available scRNA-seq datasets.

26 We found that, on average, scRNA-seq data from only five genes predicted a cell's p

27 ds for integrating the cell clusters between scRNA-seq and scATAC-seq.

28 When applied to mouse brain scRNA-seq datasets, SCATS identified more differential s

29 Biological features revealed by scRNA-seq were biomarkers of clinical outcomes in indepe

30 curs widely, impacting bulk and single cell (scRNA-seq) data set analysis.

31 ression of key phenotypic features of cells, scRNA-seq platforms are needed that are both high fideli

32 mble clustering is not limited to clustering scRNA-seq data and may be useful to a wide range of clus

33 R-based tool that is scalable to clustering scRNA-seq data with 10 million cells.

34 vised deep embedding algorithm that clusters scRNA-seq data by iteratively optimizing a clustering ob

35 Lastly, we compare scRNA-seq profiles from pediatric Crohn's disease epithe

36 o account for both dropout rates and complex scRNA-seq data distributions in the same model.

37 Current scRNA-seq analysis methods typically overcome dropout by

38 rately or in mixtures, we compared different scRNA-seq platforms and several preprocessing, normaliza

39 contamination levels between four different scRNA-seq protocols.

40 orks equally well on datasets from different scRNA-seq protocols and is scalable to datasets with ove

41 entifies driver regulators from differential scRNA-seq genes and scATAC-seq peaks.

42 aring novel and published data from distinct scRNA-seq modalities that were acquired from the same ti

43 However, effective scRNA-seq quantification tools tailored for TEs are lack

44 Existing scRNA-seq platforms utilize bead-based technologies uniq

45 , including several annotated in an existing scRNA-seq gastrulation atlas, and use this approach to g

46 fore be a useful tool to complement existing scRNA-seq pipelines.

47 ications of BAMM-SC to in-house experimental scRNA-seq datasets using blood, lung and skin cells from

48 For scRNA-seq data lacking UMIs, we propose quasi-UMIs: quan

49 We introduce netNMF-sc, an algorithm for scRNA-seq analysis that leverages information across bot

50 ls SCENIC/AUCell and metaVIPER, designed for scRNA-seq.

51 e generative adversarial networks (GANs) for scRNA-seq imputation (scIGANs), which uses generated cel

52 We propose DENDRO, an analysis method for scRNA-seq data that clusters single cells into genetical

53 hoosing dimensionality reduction methods for scRNA-seq data analysis.

54 on clustering and classification methods for scRNA-seq data, in particular, integrated methods, and p

55 ly used dimensionality reduction methods for scRNA-seq studies.

56 -inflated, as a suitable reference model for scRNA-seq analysis.

57 e step of ranking is intuitively natural for scRNA-seq data and provides a non-parametric method for

58 resent scedar, a scalable Python package for scRNA-seq exploratory data analysis.

59 Many existing pipelines for scRNA-seq data apply pre-processing steps such as normal

60 Ns from bulk samples may not be suitable for scRNA-seq data.

61 implement a heatmap-style visualization for scRNA-seq based on one-dimensional t-SNE for simultaneou

62 ), to impute surface protein abundances from scRNA-seq data by learning from existing single-cell mul

63 r detecting and visualizing dynamic APA from scRNA-seq data.

64 dynamic APA among different cell groups from scRNA-seq data.

65 ecovery of gene expression measurements from scRNA-seq data.

66 ions and inference of clonal membership from scRNA-seq is currently unreliable.

67 vailable for investigating APA profiles from scRNA-seq data.

68 We then present scCC, which map SRTs from scRNA-seq libraries, simultaneously identifying cell typ

69 ) for predicting differentiation states from scRNA-seq data.

70 ypes to human diseases and therapeutics from scRNA-seq profiles are daunting tasks.

71 Furthermore, scRNA-seq reveals the same phenomenon after a short in v

72 ndividual, these results could inform future scRNA-seq studies to ensure the most efficient experimen

73 Although the specific steps of any given scRNA-seq analysis might differ depending on the biologi

74 However, scRNA-seq data has characteristics such as drop-out even

75 we supplement cell type information of human scRNA-seq data, with mouse.

76 icing) for differential splicing analysis in scRNA-seq, which achieves high sensitivity at low covera

77 A spatial metric for individual cells in scRNA-seq data is first established based on a map conne

78 l similarity estimation, which is crucial in scRNA-seq drop-out imputation but has not been appropria

79 By using variants detected in scRNA-seq reads, it is possible to assign cells to their

80 ion and differential expression detection in scRNA-seq data.

81 ata, and sparse variant alleles expressed in scRNA-seq data.

82 h and cell numbers, two important factors in scRNA-seq, has not been determined for a complex cell po

83 e network structure associated with genes in scRNA-seq data.

84 discrete versus continuous heterogeneity in scRNA-seq data and found that SPNs in the striatum can b

85 Despite the existence of zero inflation in scRNA-seq counts, we recommend the generalized linear mo

86 ies argue that there is no zero inflation in scRNA-seq data.

87 , the unique data characteristics present in scRNA-seq such as sparsity and heterogeneity increase th

88 imate low-dimensional nonlinear structure in scRNA-seq data.

89 annotation of hidden sources of variation in scRNA-seq data.

90 recently for studying 'wanted' variation in scRNA-seq data.

91 Developing a novel strategy for integrating scRNA-seq data with GWAS data, we identified 26, exclusi

92 Overall, DecontX can be incorporated into scRNA-seq workflows to improve downstream analyses.

93 ppear to be a key driver of clonal kinetics, scRNA-seq demonstrates that clones that expand after inf

94 However, because of technical limitations, scRNA-seq data often contain zero counts for many transc

95 ancer dataset and to newly generated matched scRNA-seq and exome-seq data from 32 human dermal fibrob

96 SCATS models scRNA-seq data either with or without Unique Molecular I

97 Moreover, scRNA-seq dataset characteristics (for example, sample a

98 NA-seq data, it is urgent to develop the new scRNA-seq clustering methods.

99 cal and biological noise found in normalized scRNA-seq data.

100 The widespread adoption of scRNA-seq has created a need for user-friendly software

101 With the increasing adoption of scRNA-seq, we believe SCATS will be well-suited for vari

102 g exons is essential in splicing analysis of scRNA-seq data as it naturally aggregates spliced reads

103 r dropout events, complicate the analysis of scRNA-seq data using standard methods developed for bulk

104 Pseudotime trajectory analysis of scRNA-seq data was used to predict differentiation of no

105 eukaryotic systems(1-13), the application of scRNA-seq to prokaryotes has been hindered by their extr

106 Our approach for mapping the architecture of scRNA-seq-defined subpopulations can be applied to revea

107 ity and large sample sizes characteristic of scRNA-seq data.

108 ate this by utilizing DR-A for clustering of scRNA-seq data.

109 lations are then determined by clustering of scRNA-seq data.

110 Through a combination of scRNA-seq, ATAC-seq and genome-scale CRISPR-Cas9 screeni

111 In order to reduce the complexity of scRNA-seq data analysis, we present scedar, a scalable P

112 rtitioning and is an important confounder of scRNA-seq analysis.

113 , and provide a comprehensive description of scRNA-seq data and download URLs.

114 uction and feature gene extraction (EDGE) of scRNA-seq data.

115 An increase in the capture efficiency of scRNA-seq would also be beneficial.

116 lexibility to account for common features of scRNA-seq: high proportions of zero values, increased ce

117 yesian approach for scaling and inference of scRNA-seq counts.

118 We found that the majority of scRNA-seq imputation methods outperformed no imputation

119 or pipeline steps, illustrate the options of scRNA-seq platforms, summarize new knowledge gained from

120 scruff streamlines the preprocessing of scRNA-seq data in a few simple R commands.

121 l transport to recover spatial properties of scRNA-seq data by utilizing spatial measurements of a re

122 Analyzing the high-content readout of scRNA-seq reveals both shared and context-specific trans

123 c learns a low-dimensional representation of scRNA-seq transcript counts using network-regularized no

124 sion reduction tools across a diverse set of scRNA-seq data sets to highlight our model's ability to

125 ing observations and batch effect typical of scRNA-seq datasets make this task particularly challengi

126 RNA sequencing (scRNA-seq), large volumes of scRNA-seq data are being obtained for various processes.

127 of most optimal marker selection methods on scRNA-seq data.

128 d for bulk transcriptomics perform poorly on scRNA-seq data and progress on single cell-specific tech

129 ond-Strand Synthesis"), a massively parallel scRNA-seq protocol that uses a randomly primed second-st

130 We performed scRNA-seq analyses on immune and stromal populations fro

131 ning human retinal development, we performed scRNA-seq analysis on 16 time points from developing ret

132 We performed scRNA-seq on skin and blood from a patient with refracto

133 By performing scRNA-seq on peripheral blood mononuclear cells from fou

134 e analysis between Toxoplasma and Plasmodium scRNA-seq results reveals concerted expression of gene s

135 pciSeq), an approach that leverages previous scRNA-seq classification to identify cell types using mu

136 stering or classification methods to process scRNA-seq data is generally difficult.

137 omputational workflow involved in processing scRNA-seq data.

138 seq)-a low-cost, high-throughput prokaryotic scRNA-seq pipeline that overcomes these technical obstac

139 omprehensive benchmarking tests on 17 public scRNA-seq data sets show that SHARP outperforms existing

140 Using a recently published scRNA-seq study of tissue T cells as an example, we intr

141 -fold more podocytes compared with published scRNA-seq datasets (2.4% versus 0.12%, respectively).

142 As demonstrated using simulated and real scRNA-seq data, the VAM method provides superior classif

143 ene covariance using both simulated and real scRNA-seq data, with increasing advantages at higher dro

144 form benchmark studies on simulated and real scRNA-seq data.

145 f SCATS, we analyzed both simulated and real scRNA-seq datasets and compared with existing methods in

146 ons based on a variety of simulated and real scRNA-seq datasets show that scIGANs is effective for dr

147 ng cell types in multiple simulated and real scRNA-seq datasets.

148 of scDoc using both simulated data and real scRNA-seq studies.

149 roduce different statistics observed in real scRNA-seq data.

150 We complement the simulated data with real scRNA-seq data upon CRISPR-mediated knock-out.

151 inflation in multiple biologically realistic scRNA-seq datasets.

152 omic profiles, and clustering on large-scale scRNA-seq datasets.

153 define cell types from single-cell RNA-seq (scRNA-seq) and single-nucleus ATAC-seq (snATAC-seq) data

154 analysis of RNA-seq and single-cell RNA-seq (scRNA-seq) data from the same lung cell populations indi

155 step in the analysis of single cell RNA-seq (scRNA-seq) data to shed light on tissue complexity inclu

156 In single-cell RNA-seq (scRNA-seq) experiments, the number of individual cells h

157 linking microscopy and single-cell RNA-seq (scRNA-seq) have limited scalability.

158 Single cell RNA-seq (scRNA-seq) is a powerful tool to discover cellular heter

159 approach that utilizes single-cell RNA-seq (scRNA-seq) or single-nucleus RNA-seq (snRNA-seq) data to

160 Single-cell RNA-seq (scRNA-seq) profiles gene expression of individual cells.

161 Recent advances in single-cell RNA-seq (scRNA-seq) revolutionized cell type-specific gene expres

162 Early single-cell RNA-seq (scRNA-seq) studies suggested that it was unusual to see

163 The emergence of single-cell RNA-seq (scRNA-seq) technology has made it possible to measure ge

164 However, compared to bulk RNA-seq, scRNA-seq data are much noisier due to high technical va

165 before Lgr5, and single-cell RNA sequencing (scRNA-seq) analyses reveal transcriptional paths underly

166 Our single-cell RNA sequencing (scRNA-seq) analysis reveals novel mesenchymal and transi

167 adult SPNs using single-cell RNA sequencing (scRNA-seq) and quantitative RNA in situ hybridization (I

168 Here we applied single-cell RNA sequencing (scRNA-seq) and single-cell assay for transposase-accessi

169 a droplet-based single-cell RNA sequencing (scRNA-seq) approach to systematically classify molecular

170 all intestine by single-cell RNA sequencing (scRNA-seq) at steady state and after induction of a type

171 Single-cell RNA sequencing (scRNA-seq) can be used to explore cell types, states, an

172 Single-cell RNA sequencing (scRNA-seq) can characterize cell types and states throug

173 dic protocols in single cell RNA sequencing (scRNA-seq) collect mRNA counts from up to one million in

174 Integrating single-cell RNA sequencing (scRNA-seq) data with genotypes obtained from DNA sequenc

175 cell types from single-cell RNA sequencing (scRNA-seq) data.

176 blicly available single-cell RNA sequencing (scRNA-seq) dataset of melanoma samples of patients subje

177 omparing diverse single-cell RNA sequencing (scRNA-seq) datasets generated by different technologies

178 Single-cell RNA sequencing (scRNA-seq) deconvolves cell types from gene expression,

179 Single-cell RNA sequencing (scRNA-seq) enables the systematic identification of cell

180 is, we performed single-cell RNA sequencing (scRNA-seq) from a pool of 2,045 parasites collected from

181 e development of single-cell RNA sequencing (scRNA-seq) has allowed high-resolution analysis of cell-

182 Single-cell RNA sequencing (scRNA-seq) has become an essential tool for characterizi

183 Single-cell RNA sequencing (scRNA-seq) has enabled the simultaneous transcriptomic p

184 cerebral cortex, single-cell RNA sequencing (scRNA-seq) has revealed the genome-wide expression patte

185 Although single-cell RNA sequencing (scRNA-seq) has revolutionized studies of transcriptional

186 Using single-cell RNA sequencing (scRNA-seq) in Arabidopsis thaliana tetraploid lines and

187 Single-cell RNA sequencing (scRNA-seq) is a popular and powerful technology that all

188 Single-cell RNA sequencing (scRNA-seq) is a powerful approach for reconstructing cel

189 Single-cell RNA sequencing (scRNA-seq) is a powerful tool for defining cellular dive

190 Single-cell RNA sequencing (scRNA-seq) is a technology to measure gene expression in

191 Single-cell RNA sequencing (scRNA-seq) is an emerging technology that can assess the

192 While single cell RNA sequencing (scRNA-seq) is invaluable for studying cell populations,

193 Single-cell RNA sequencing (scRNA-seq) is the leading technique for characterizing t

194 re we report the single-cell RNA sequencing (scRNA-seq) of ALK3(bright+)-sorted ductal cells, a fract

195 ecipient DNA and single-cell RNA sequencing (scRNA-seq) of five human kidney transplant biopsy cores

196 Single-cell RNA sequencing (scRNA-seq) of metastatic lung cancer was performed using

197 here we perform single-cell RNA sequencing (scRNA-seq) of peripheral blood mononuclear cells from op

198 Single-cell RNA sequencing (scRNA-seq) of the tumors identified hypoxic cancer cells

199 ere we performed single-cell RNA sequencing (scRNA-seq) of total 125,674 cells from seven stage-I/II

200 rent methods for single-cell RNA sequencing (scRNA-seq) of yeast cells do not match the throughput an

201 on, we performed single-cell RNA sequencing (scRNA-seq) on biopsies from a patient with cCD and analy

202 Progress in single-cell RNA sequencing (scRNA-seq) provides an opportunity to dissect human dise

203 Single-cell RNA sequencing (scRNA-seq) provides details for individual cells; howeve

204 Single-cell RNA sequencing (scRNA-seq) resolves heterogenous cell populations in tis

205 Single-cell RNA sequencing (scRNA-seq) reveals that ~15% of lung ECs are transcripti

206 monly applied in single cell RNA sequencing (scRNA-seq) studies.

207 Single-cell RNA sequencing (scRNA-seq) technologies allow researchers to uncover the

208 Single-cell RNA sequencing (scRNA-seq) technologies enable the study of transcriptio

209 cent progress in single-cell RNA sequencing (scRNA-seq) technologies enables identification of cell-t

210 However, these single-cell RNA sequencing (scRNA-seq) technologies generate an extraordinary amount

211 Advances in single-cell RNA sequencing (scRNA-seq) technologies in the past 10 years have had a

212 We used single-cell RNA sequencing (scRNA-seq) to achieve an unbiased characterization of th

213 ere, we employed single-cell RNA sequencing (scRNA-seq) to examine the immature postnatal thymocyte p

214 te analysis, and single-cell RNA sequencing (scRNA-seq) to profile CD8(+) CAR-T cells from infusion p

215 D-19, we applied single-cell RNA sequencing (scRNA-seq) to profile peripheral blood mononuclear cells

216 al organoids and single-cell RNA sequencing (scRNA-seq) to study the effects of prenatal METH exposur

217 Single-cell RNA sequencing (scRNA-seq) was performed on airway inflammatory cells is

218 ach by combining single-cell RNA sequencing (scRNA-seq) with Raman optical tweezers (ROT), a label-fr

219 We performed single-cell RNA sequencing (scRNA-seq) with the clinically relevant unilateral ische

220 th the advent of single-cell RNA sequencing (scRNA-seq), it is in principle possible to do such an an

221 ical advances in single-cell RNA sequencing (scRNA-seq), large volumes of scRNA-seq data are being ob

222 ologies, such as single-cell RNA sequencing (scRNA-seq), there is a need for developing novel network

223 the single cell level, using RNA sequencing (scRNA-seq), there is the potential to identify specific

224 Using single-cell RNA sequencing (scRNA-seq), we have identified a population of endotheli

225 e major goals in single cell RNA sequencing (scRNA-seq).

226 e reliability of single cell RNA sequencing (scRNA-seq).

227 as determined by single-cell RNA sequencing (scRNA-seq).

228 used to perform single-cell RNA sequencing (scRNA-seq).

229 Single-cell RNA-sequencing (scRNA-seq) allows us to dissect transcriptional heteroge

230 Single-cell RNA-sequencing (scRNA-seq) analysis demonstrated considerable heterogene

231 Using single-cell RNA-sequencing (scRNA-seq) and genetic reporter mice, we identified disc

232 ources involving single-cell RNA-sequencing (scRNA-seq) data are expanding rapidly.

233 ds to deconvolve single-cell RNA-sequencing (scRNA-seq) data are necessary for samples containing a m

234 milarity between single-cell RNA-sequencing (scRNA-seq) data are ubiquitous in bioinformatics, but us

235 cess large-scale single-cell RNA-sequencing (scRNA-seq) data effectively without excessive distortion

236 cently published single-cell RNA-sequencing (scRNA-seq) data from 727 peripheral and nervous system c

237 Normalization of single-cell RNA-sequencing (scRNA-seq) data is a prerequisite to their interpretatio

238 ical analysis of single cell RNA-sequencing (scRNA-seq) data is hindered by high levels of technical

239 iously generated single-cell RNA-sequencing (scRNA-seq) data of gastric corpus epithelium to define t

240 for visualizing single-cell RNA-sequencing (scRNA-seq) data, but it scales poorly to large datasets.

241 as dropouts) in single-cell RNA-sequencing (scRNA-seq) data.

242 imate, and mouse single-cell RNA-sequencing (scRNA-seq) datasets across health and disease to uncover

243 Single-cell RNA-sequencing (scRNA-seq) enables high-throughput measurement of RNA ex

244 Single-cell RNA-sequencing (scRNA-seq) enables the characterization of transcriptomi

245 cent advances in single-cell RNA-sequencing (scRNA-seq) facilitate detailed characterization of gene

246 Single-cell RNA-sequencing (scRNA-seq) has become an important tool to unravel cellu

247 VD by performing single-cell RNA-sequencing (scRNA-seq) in primary AF and NP cells isolated from non-

248 High-throughput single-cell RNA-sequencing (scRNA-seq) methodologies enable characterization of comp

249 Here, we applied single-cell RNA-sequencing (scRNA-seq) on >5,400 Toxoplasma in both tachyzoite and b

250 Single-cell RNA-sequencing (scRNA-seq) represents a powerful tool for dissecting com

251 d development of single-cell RNA-sequencing (scRNA-seq) technologies has led to the emergence of many

252 Single-cell RNA-sequencing (scRNA-seq) technology enables studying gene expression p

253 iseases, we used single-cell RNA-sequencing (scRNA-seq) to analyze the transcriptomes of ~85,000 cell

254 Here we use single-cell RNA-sequencing (scRNA-seq) to build a comprehensive cell atlas of the ad

255 -based single cell transcriptome sequencing (scRNA-seq) technology, largely represented by the 10x Ge

256 el DGRN inference method using "time-series" scRNA-seq data.

257 e over several existing methods on simulated scRNA-seq datasets by finding more differentially expres

258 Specifically, scRNA-seq has facilitated the identification of novel or

259 The scRNA-seq, comprehensive, cell-specific profiles provide

260 ited for unsupervised learning tasks for the scRNA-seq data, where labels for cell types are costly a

261 esents a competing imputation method for the scRNA-seq data.

262 However, the scRNA-seq data are challenging for traditional methods d

263 Analysis of expressed SNVs in the scRNA-seq data set distinguished recipient versus donor

264 curate low dimensional representation of the scRNA-seq data.

265 ial first step in downstream analysis of the scRNA-seq data.

266 We hypothesized that the scRNA-seq data of mouse brain tissue can be used to comp

267 ing the genetic variants detected within the scRNA-seq reads.

268 These scRNA-seq resources are free of cross-contamination and

269 This scRNA-seq atlas will be a valuable resource for dissecti

270 We also apply iDEA to analyze three scRNA-seq data sets, where iDEA achieves up to five-fold

271 The proposed methods are applied on three scRNA-seq datasets.

272 pipelines when dealing with high throughput scRNA-seq data.

273 ligation transcriptomics), a high-throughput scRNA-seq method for Gram-negative and Gram-positive bac

274 Thus, scRNA-seq analyses guided successful therapeutic interve

275 We further expanded our analysis to scRNA-seq data from early stages of mouse embryogenesis.

276 urated footprint gene sets can be applied to scRNA-seq data, partially outperforming dedicated single

277 lished for bulk sequencing can be applied to scRNA-seq in a meaningful way.

278 We further applied our approach to scRNA-seq transformed by kNN smoothing and found that ou

279 However, one major challenge to scRNA-seq research is the presence of 'drop-out' events,

280 ically for UMI data to be applied to non-UMI scRNA-seq datasets.

281 multicenter study comparing 13 commonly used scRNA-seq and single-nucleus RNA-seq protocols applied t

282 library construction, the most commonly used scRNA-seq protocol-10x Genomics enables us to improve th

283 To fill this knowledge gap, we used scRNA-seq to profile the placental villous tree, basal p

284 ntal and computational methodology that uses scRNA-seq to characterize dynamic cellular programs and

285 Using scRNA-seq profiling and genetic lineage tracing, we show

286 Using scRNA-seq to probe unsorted cells from regenerating, sca

287 o resolve isoforms in individual cells using scRNA-seq.

288 tools and discuss the latest findings using scRNA-seq that have substantially improved our knowledge

289 oenvironment at single-cell resolution using scRNA-seq of 59,915 tumor and non-neoplastic cells from

290 possible to study alternative splicing using scRNA-seq.

291 hods by applying it on datasets from various scRNA-seq protocols.

292 developed to process, analyse and visualize scRNA-seq datasets.

293 Whole genome-wide scRNA-seq, ATAC-seq, and ChIP-seq analyses reveal that A

294 We additionally integrate our analysis with scRNA-seq data to identify orthogonal evidence for trans

295 ly process specific problems associated with scRNA-seq data.

296 at the high rate of dropouts associated with scRNA-seq is a major obstacle to studying alternative sp

297 soform choice and the errors associated with scRNA-seq is required.

298 ntification pipeline that is compatible with scRNA-seq data generated across multiple technology plat

299 Here, we combined fluorescence imaging with scRNA-seq to measure cell cycle phase and gene expressio

300 ures, patient samples, and mouse models with scRNA-seq to elucidate early events occurring with oncog