packages related to scRNAseq:

Single cell transcriptomes identify human islet cell signatures and reveal cell-type-specific expression changes in type 2 diabetes

http://genome.cshlp.org/content/early/2016/11/18/gr.212720.116.abstract

SCODE: An efficient regulatory network inference algorithm from single-cell RNA-Seq during differentiation

http://biorxiv.org/content/early/2016/11/21/088856

SCOUP is a probabilistic model to analyze single-cell expression data during differentiation

https://github.com/hmatsu1226/SCOUP

scLVM is a modelling framework for single-cell RNA-seq data

https://github.com/PMBio/scLVM

Selective Locally linear Inference of Cellular Expression Relationships (SLICER) algorithm for inferring cell trajectories

https://github.com/jw156605/SLICER

SinQC: A Method and Tool to Control Single-cell RNA-seq Data Quality

http://www.morgridge.net/SinQC.html

TSCAN: Pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis

https://github.com/zji90/TSCAN

Visualization and cellular hierarchy inference of single-cell data using SPADE

http://www.nature.com/nprot/journal/v11/n7/full/nprot.2016.066.html

OEFinder: Identify ordering effect genes in single cell RNA-seq data

https://github.com/lengning/OEFinder

SCell: single-cell RNA-seq analysis software

https://github.com/diazlab/SCell

Beta-Poisson model for single-cell RNA-seq data analyses

https://github.com/nghiavtr/BPSC

Sincera: A Computational Pipeline for Single Cell RNA-Seq Profiling Analysis

https://research.cchmc.org/pbge/sincera.html

SC3 - consensus clustering of single-cell RNA-Seq data

http://biorxiv.org/content/early/2016/09/02/036558

Citrus: A toolkit for single cell sequencing analysis

http://biorxiv.org/content/early/2016/09/14/045070

Single-Cell Resolution of Temporal Gene Expression during Heart Development

http://www.cell.com/developmental-cell/fulltext/S1534-5807(16)30682-7

Scalable latent-factor models applied to single-cell RNA-seq data separate biological drivers from confounding effects

http://biorxiv.org/content/early/2016/11/15/087775

CellView – Interactive Exploration Of High Dimensional Single Cell RNA-Seq Data

https://mbolisetty.shinyapps.io/CellView/

SCANPY: large-scale single-cell gene expression data analysis

https://github.com/theislab/Scanpy

A step-by-step workflow for low-level analysis of single-cell RNA-seq data

http://f1000research.com/articles/5-2122/v1

https://f1000research.com/articles/5-2122/v2

https://www.bioconductor.org/help/workflows/simpleSingleCell/

Power Analysis of Single Cell RNA‐Sequencing Experiments

http://biorxiv.org/content/early/2016/09/08/073692

Excellent Single Cell RNAseq data analysis Tutorial available in pdf format. (thanks vladimir.yu.kiselev and Geparada for sharing)

http://hemberg-lab.github.io/scRNA.seq.course/scRNA-seq-course.pdf

C: Analysis of single-cell RNA-seq data

My feeling is that depending on the type of technology that was used for the generation of the single cell data (e.g., plate-sorted vs. droplet-based), the data may come with very differently magnified problems.

A good overview is given here, the image below is a great visualization of the variety of methods that generically pass as "single-cell transcriptomics". Does anyone know of an insightful discussion of the different (scales of) problems (and solutions)?

In addition, I guess it'd be good to know which packages were developed with what kind of data in mind. E.g., Seurat was developed in a lab that pushed Drop-seq technology. I think it'd at least be useful piece of information to have as it may explain why some specific methods/models may work better for some types of data than for others.

scRNA notes and Reviews:

Single-cell mRNA quantification and differential analysis with Census

http://www.nature.com/nmeth/journal/vaop/ncurrent/full/nmeth.4150.html

Comparison of bulk and scRNA-seq analytical strategies

http://www.nature.com/nrg/journal/v16/n3/fig_tab/nrg3833_F1.html

Single-cell RNA-seq to decipher tumour architecture

http://www.nature.com/nrg/journal/vaop/ncurrent/full/nrg.2016.151.html

Single-Cell Transcriptomics Bioinformatics and Computational Challenges

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5030210/

Vedio tutorial: Analysis of single cell RNA-seq data - Lecture 1

Analysing Single-Cell RNA-Seq data with SeqMonk

single nucleus RNA-seq