In Vivianstats/scImpute: Accurate and robust imputation of single-cell RNA sequencing data

Updates

2018/08/15:

• Version 0.0.9 is released!
• More robust implementation of dimension reduction.
• Faster calculation of cell similarity.

2018/06/27:

• Version 0.0.8 is released!
• Faster implementation of dimension reduction.

2018/06/08:

• Version 0.0.7 is released!
• New option for application on TPM values.

2018/03/16:

• Version 0.0.6 is released!
• The scImpute method is published at Nature Communications.
• scImpute now supports input and output in the format of R objects (.rds).

2018/01/12:

• Version 0.0.5 is released!
• It is now possible to apply scImpute on just one cell population by setting Kcluster = 1.

2017/10/27:

• Version 0.0.4 is released!
• scImpute now supports multi-code parallelism.

2017/10/22:

• Version 0.0.3 is released!
• Estimation of dropout probabilities is more accurate.
• Imputation step is more robust.
• scimpute() incorporates a new parameter Kcluster to specify the number of cell subpopulations.
• scImpute is now able to detect outlier cells.

2017/07/01:

• Version 0.0.2 is released!
• This version speeds up the first step in scImpute and program now completes in a few seconds when applied to a dataset with 10,000 genes and 100 cells (using single core).

Vivianstats/scImpute documentation built on Aug. 21, 2019, 1:49 p.m.