ライフサイエンスコーパス: scRNA-seq

1 scRNA-seq on murine hematopoietic stem cells (HSC) and t

2 e high level of technical noise that affects scRNA-seq protocols is vital.

3 We apply our method to mass cytometry and scRNA-seq datasets, and demonstrate that it effectively

4 differential isoform quantification between scRNA-seq data sets.

5 We report the analysis of 1776 cells by scRNA-seq covering distinct human embryonic stem cell-de

6 performed RNA-seq analysis of single cells (scRNA-seq) and single nuclei (snRNA-seq) and found them

7 ovides an integrated framework for comparing scRNA-seq protocols.

8 that SinQC is a useful tool for controlling scRNA-seq data quality.

9 ce of DE are further examined by time course scRNA-seq experiments, employing two new statistical too

10 ts, we find that f-scLVM robustly decomposes scRNA-seq datasets into interpretable components, thereb

11 We apply SinQC to nine different scRNA-seq datasets, and show that SinQC is a useful tool

12 esults, we suggest a framework for effective scRNA-seq studies.

13 m three East-Asian non-diabetic subjects for scRNA-seq.

14 ever, current methods for TCR inference from scRNA-seq are limited in their sensitivity and require l

15 However, scRNA-seq suffers from higher noise and lower coverage t

16 ws users to check for potential artifacts in scRNA-seq data generated by the Fluidigm C1 platform.

17 ftware tool to detect technical artifacts in scRNA-seq samples by integrating both gene expression pa

18 ility makes detecting technical artifacts in scRNA-seq samples particularly challenging.

19 onstructing cell differentiation lineages in scRNA-seq analysis.

20 owever, the sources of experimental noise in scRNA-seq are not yet well understood.

21 ltaneously preserve biological variations in scRNA-seq data, such that existing statistical methods c

22 In applications to multiple scRNA-seq datasets, we find that f-scLVM robustly decomp

23 With the advantages of scRNA-seq come computational challenges that are just be

24 ods available for the design and analysis of scRNA-seq experiments, their advantages and disadvantage

25 orm for researchers to leverage the power of scRNA-seq.

26 used in JingleBells can facilitate reuse of scRNA-seq data by computational biologists.

27 BRIE, therefore, expands the scope of scRNA-seq experiments to probe the stochasticity of RNA

28 resource that will extend the usefulness of scRNA-seq datasets outside the programming aficionado re

29 control, normalization and visualization of scRNA-seq data.

30 we present a generic approach for processing scRNA-seq data and detecting low quality cells, using a

31 l RNA-seq profiling Analysis) for processing scRNA-seq data from a whole organ or sorted cells.

32 act TCR sequence information from short-read scRNA-seq libraries.

33 In simulation and real scRNA-seq data, TASC achieves accurate Type I error cont

34 Using real scRNA-seq data, we compared Linnorm with existing normal

35 ensional reduction methods on several recent scRNA-seq datasets.

36 hough the number of available immune-related scRNA-seq datasets increases rapidly, their large size a

37 he JingleBells repository for immune-related scRNA-seq datasets ready for analysis and visualization

38 aw data of publicly available immune-related scRNA-seq datasets, aligned the reads to the relevant ge

39 an artifact in multiple single-cell RNA-seq (scRNA-seq) datasets generated by the Fluidigm C1 platfor

40 ht chain sequences from single cell RNA-seq (scRNA-seq) in B cells has been largely unstudied.

41 Single-cell RNA-seq (scRNA-seq) is emerging as a promising technology for pro

42 Single-cell RNA-seq (scRNA-seq) of pancreatic islets have reported on alpha-

43 ols for the analysis of single-cell RNA-seq (scRNA-seq) profiles.

44 Single-cell RNA-seq (scRNA-seq) provides a comprehensive measurement of stoch

45 Single cell RNA-seq (scRNA-seq) techniques can reveal valuable insights of ce

46 algorithm that utilizes single-cell RNA-seq (scRNA-seq) to quantitatively measure cellular differenti

47 h as mass cytometry and single-cell RNA-seq (scRNA-seq), are plagued with systematic errors that may

48 Single-cell RNA sequencing (scRNA-seq) can be used to characterize variation in gene

49 the analysis of single cell RNA sequencing (scRNA-seq) data.

50 Single-cell RNA sequencing (scRNA-seq) has become an established and powerful method

51 Single-cell RNA sequencing (scRNA-seq) has broad applications across biomedical rese

52 Single-cell RNA sequencing (scRNA-seq) is increasingly used to study gene expression

53 Current single-cell RNA sequencing (scRNA-seq) protocols are complex and introduce technical

54 Here we use single-cell RNA sequencing (scRNA-seq) to dissect and compare cell composition and p

55 that integrates single-cell RNA sequencing (scRNA-seq) with the shRNA screen to investigate the mech

56 s, especially in single-cell RNA sequencing (scRNA-seq), have already begun to reveal, in a data-driv

57 Single-cell RNA-sequencing (scRNA-seq) allows studying heterogeneity in gene express

58 dy, we performed single-cell RNA-sequencing (scRNA-seq) analysis of mouse nonpeptidergic nociceptors

59 Single cell RNA-sequencing (scRNA-seq) can allow simultaneous measurement of TCR seq

60 Single-cell RNA-sequencing (scRNA-seq) facilitates identification of new cell types

61 Single-cell RNA-sequencing (scRNA-seq) has emerged as a revolutionary tool that allo

62 Here, we used single-cell RNA-sequencing (scRNA-seq) of developing neurons to dissect/identify NPC

63 Here, using single-cell RNA-sequencing (scRNA-seq) of mouse bone marrow progenitors, we reveal I

64 cent advances in single-cell RNA-sequencing (scRNA-seq) technology increase the understanding of immu

65 tochemistry, and single-cell RNA-sequencing (scRNA-seq) to comprehensively profile single neurons fro

66 c progenitors by single-cell RNA-sequencing (scRNA-seq).

67 subjected nearly 200 single human B cells to scRNA-seq, assembled the full-length heavy and the light

68 We then applied SLICE to scRNA-seq of embryonic mouse lung at E16.5 to identify l

69 The uniform scRNA-seq format used in JingleBells can facilitate reus

70 ol, BASIC, which allows investigators to use scRNA-seq for assembling BCR sequences at single-cell re

71 vantage of combining functional screens with scRNA-seq to accelerate the discovery of pathways contro