Tutorials

CALISTA is a numerically efficient and highly scalable toolbox for an end-to-end analysis of single-cell transcriptomic profiles.

CALISTA includes four essential single-cell analyses for cell differentiation studies, including:

1- single-cell clustering;

2- reconstruction of cell lineage specification;

3- transition gene identification;

4- cell pseudotime ordering.

In the following tutorials, we describe the main steps of CALISTA (MATLAB VERSION) applied to in silico and publicly available single-cell gene expression data. For each dataset, ONLY the most important results are reported. Please refer to the file MAIN.m for an example MATLAB script of CALISTA implementation.

Please click here to see examples of CALISTA (R VERSION).

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scRT-qPCR

~2000 cells

Bargaje et al.

ex2.png

scRT-qPCR

~600 cells

Moignard et al.

ex3.png

scRNA-seq

~400 cells

Treutlein et al.

ex4.png

scRNA-seq

~760 cells

Chu et al.

ex5.png

scRT-qPCR

~600 cells

No time info

Moignard et al.

Screenshot 2019-01-20 at 20.27.37.png

scRT-qPCR

~2000 cells

MATLAB GUI

Bargaje et al.

ex7.png

scRNA-seq 

(scDrop-seq)

~38k cells

Farrell et al.

ex8c.png

scRNA-seq

(sn-Drop-seq)

~17k cells

Sathyamuthy et al.

Sankey diagram ZHENG big.png

scRNA-seq

(scDrop-seq)

~68k cells

Zheng et al.

ex5.png

scRT-qPCR

~600 cells

REMOVE UNDESIRED CLUSTERS

Moignard et al.

ex11.png

1800 cells

Simulated data

© 2018 by Rudiyanto Gunawan