changes and characterizing heterogeneity in
single-cell RNA sequencing data. Genome
Biol 16.https://doi.org/10.1186/S13059-
015-0844-5
- Delmans M, Hemberg M (2016) Discrete
distributional differential expression ((DE)-
E-3) – a tool for gene expression analysis of
single-cell RNA-seq data. BMC Bioinformat-
ics 17. https://doi.org/10.1186/S12859-
016-0944-6
- Korthauer KD, Chu LF, Newton MA, Li Y,
Thomson J, Stewart R, Kendziorski C (2016)
A statistical approach for identifying differen-
tial distributions in single-cell RNA-seq
experiments. Genome Biol 17(1):222.
https://doi.org/10.1186/s13059-016-
1077-y
- Korthauer KD, Chu L-F, Newton MA, Li Y,
Thomson J, Stewart R, Kendziorski C (2016)
scDD: a statistical approach for identifying
differential distributions in single-cell RNA-
seq experiments. bioRxiv 2016:035501
- Jia C, Kelly D, Kim J, Li M, Zhang N (2017)
Accounting for technical noise in single-cell
RNA sequencing analysis. bioRxiv
2017:116939
- Svensson V, Teichmann SA, Stegle O (2017)
SpatialDE-identification of spatially variable
genes. bioRxiv 2017:143321
- Grun D, Lyubimova A, Kester L, Wiebrands
K, Basak O, Sasaki N, Clevers H, van Oude-
naarden A (2015) Single-cell messenger RNA
sequencing reveals rare intestinal cell types.
Nature 525(7568):251.https://doi.org/10.
1038/nature14966
- Zeisel A, Munoz-Manchado AB, Codeluppi
S, Lonnerberg P, La Manno G, Jureus A,
Marques S, Munguba H, He LQ, Betsholtz
C, Rolny C, Castelo-Branco G, Hjerling-Lef-
fler J, Linnarsson S (2015) Cell types in the
mouse cortex and hippocampus revealed by
single-cell RNA-seq. Science 347
(6226):1138–1142. https://doi.org/10.
1126/science.aaa1934
- Pierson E, Yau C (2015) ZIFA: dimensional-
ity reduction for zero-inflated single-cell gene
expression analysis. Genome Biol 16.https://
doi.org/10.1186/S13059-015-0805-Z
- Angerer P, Haghverdi L, Buttner M, Theis FJ,
Marr C, Buettner F (2016) destiny: diffusion
maps for large-scale single cell data in R. Bio-
informatics 32(8):1241–1243. https://doi.
org/10.1093/bioinformatics/btv715
- Xu C, Su ZC (2015) Identification of cell
types from single-cell transcriptomes using a
novel clustering method. Bioinformatics 31
(12):1974–1980.https://doi.org/10.1093/
bioinformatics/btv088
- Marco E, Karp RL, Guo GJ, Robson P, Hart
AH, Trippa L, Yuan GC (2014) Bifurcation
analysis of single-cell gene expression data
reveals epigenetic landscape. Proc Natl Acad
Sci U S A 111(52):E5643–E5650.https://
doi.org/10.1073/pnas.1408993111
- Leng N, Chu LF, Barry C, Li Y, Choi J, Li
XM, Jiang P, Stewart RM, Thomson JA,
Kendziorski C (2015) Oscope identifies oscil-
latory genes in unsynchronized single-cell
RNA-seq experiments. Nat Methods 12
(10):947–950. https://doi.org/10.1038/
Nmeth.3549
- Ji ZC, Ji HK (2016) TSCAN: pseudo-time
reconstruction and evaluation in single-cell
RNA-seq analysis. Nucleic Acids Res 44(13).
https://doi.org/10.1093/nar/gkw430
- Specht AT, Li J (2017) LEAP: constructing
gene co-expression networks for single-cell
RNA-sequencing data using pseudotime
ordering. Bioinformatics 33(5):764–766.
https://doi.org/10.1093/bioinformatics/
btw729
- Welch JD, Hartemink AJ, Prins JF (2016)
SLICER: inferring branched, nonlinear cellu-
lar trajectories from single cell RNA-seq data.
Genome Biol 17.https://doi.org/10.1186/
S13059-016-0975-3
- duVerle D, Yotsukura S, Nomura S, Aburatani
H, Tsuda K (2016) CellTree: an R/biocon-
ductor package to infer the hierarchical struc-
ture of cell populations from single-cell RNA-
seq data. BMC Bioinformatics 17.https://
doi.org/10.1186/S12859-016-1175-6
- Rashid S, Kotton DN, Bar-Joseph Z (2017)
TASIC: determining branching models from
time series single cell data. Bioinformatics.
https://doi.org/10.1093/bioinformatics/
btx173
- Lo ̈nnberg T, Svensson V, James KR, Fernan-
dez-Ruiz D, Sebina I, Montandon R, Soon
MS, Fogg LG, Nair AS, Liligeto U (2017)
Single-cell RNA-seq and computational anal-
ysis using temporal mixture modelling
resolves Th1/Tfh fate bifurcation in malaria.
Sci Immunol 2(9):eaa12192
- Matsumoto H, Kiryu H (2016) SCOUP: a
probabilistic model based on the Ornstein-
Uhlenbeck process to analyze single-cell
expression data during differentiation. BMC
Bioinformatics 17. https://doi.org/10.
1186/S12859-016-1109-3
- McCarthy DJ, Campbell KR, Lun ATL, Wills
QF (2017) Scater: pre-processing, quality
control, normalization and visualization of
single-cell RNA-seq data in R. Bioinformatics
33(8):1179–1186. https://doi.org/10.
1093/bioinformatics/btw777
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