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Table 1 List of compared dimensionality reduction methods. We list standard modeling properties for each of compared dimensionality reduction methods

From: Accuracy, robustness and scalability of dimensionality reduction methods for single-cell RNA-seq analysis

No.

Methods

Modeling counts

Modeling zero inflation

Non-linear projection

Computation efficiency

Implementation language

Year of publication

Reference

1

PCA

No

No

No

Yes

R

1901

[42]

2

ICA

No

No

No

No

R

1994

[43]

3

FA

No

No

No

Yes

R

1952

[44]

4

NMF

No

No

No

Yes

R

1999

[45]

5

Poisson NMF

Yes

No

No

Yes

R

1999

[45]

6

Diffusion Map

No

No

Yes

Yes

R

2005

[46]

7

ZIFA

No

Yes

No

No

Python

2016

[30]

8

ZINB-WaVE

Yes

Yes

No

No

R

2018

[32]

9

GLMPCA

Yes

No

No

No

R

2019

[47]

10

pCMF

Yes

Yes

No

No

R

2019

[31]

11

scScope

No

Yes

Yes

Yes

Python

2019

[37]

12

DCA

Yes

Yes

Yes

Yes

Python

2018

[40]

13

tSNE

No

No

Yes

No

R

2008

[48]

14

MDS

No

No

No

Yes

R

1958

[49]

15

LLE

No

No

Yes

Yes

R

2000

[50]

16

LTSA

No

No

Yes

No

R

2004

[51]

17

Isomap

No

No

Yes

Yes

R

2000

[11]

18

UMAP

No

No

Yes

Yes

Python

2019

[52]

  1. These properties include whether it models count data (3rd column), whether it accounts for zero inflation (4th column), whether it is a linear dimensionality reduction method (5th column), its computation efficiency (6th column), implementation language (7th column), year of publication (8th column), and reference (9th column). FA factor analysis, PCA principal component analysis, ICA independent component analysis, NMF nonnegative matrix factorization, Poisson NMF Kullback-Leibler divergence-based NMF, ZIFA zero-inflated factor analysis, ZINB-WaVE zero-inflated negative binomial-based wanted variation extraction, pCMF probabilistic count matrix factorization, DCA deep count autoencoder network, scScope scalable deep-learning-based approach, GLMPCA generalized linear model principal component analysis, Diffusion Map, MDS multidimensional scaling, LLE locally linear embedding, LTSA local tangent space alignment, Isomap; UMAP uniform manifold approximation and projection, tSNE t-distributed stochastic neighbor embedding