how to convert EMSet object to scGPS object?

[lixin4306ren]

I did clustering analysis by ascend package and have EMSet object for my data. How can I use it for analysis by scGPS? Thanks.

[quanaibn]

you can extract the expression matrix and cell-gene data from EMSet, and create an scGPS object

[lixin4306ren]

Thank you for your reply. I finished scGPS clustering analysis and compared the results with that from ascend. For the same sample, I found that ascend generated 3 clusters, while scGPS's optimal result generated 10 clusters. How to explain the difference and which result should I use? Thanks.

ascend results:

> nor2@clusterAnalysis$keyStats
   Height Stability RandIndex ConsecutiveRI ClusterCount
1   0.025     0.200 1.0000000     1.0000000            9
2    0.05     0.200 1.0000000     1.0000000            9
3   0.075     0.200 1.0000000     1.0000000            9
4     0.1     0.200 1.0000000     1.0000000            9
5   0.125     0.200 1.0000000     1.0000000            9
6    0.15     0.200 1.0000000     1.0000000            9
7   0.175     0.200 1.0000000     1.0000000            9
8     0.2     0.200 1.0000000     1.0000000            9
9   0.225     0.025 0.8231890     0.8231890            8
10   0.25     0.025 0.8231890     1.0000000            8
11  0.275     0.025 0.8044747     0.9806156            7
12    0.3     0.025 0.8044747     1.0000000            7
13  0.325     0.025 0.5193348     0.6852822            6
14   0.35     0.025 0.5193348     1.0000000            6
15  0.375     0.025 0.5192552     0.9998965            5
16    0.4     0.050 0.5192552     1.0000000            5
17  0.425     0.050 0.5192552     1.0000000            5
18   0.45     0.025 0.3397770     0.7370673            3
19  0.475     0.275 0.3397770     1.0000000            3
20    0.5     0.275 0.3397770     1.0000000            3
21  0.525     0.275 0.3397770     1.0000000            3
22   0.55     0.275 0.3397770     1.0000000            3
23  0.575     0.275 0.3397770     1.0000000            3
24    0.6     0.275 0.3397770     1.0000000            3
25  0.625     0.275 0.3397770     1.0000000            3
26   0.65     0.275 0.3397770     1.0000000            3
27  0.675     0.275 0.3397770     1.0000000            3
28    0.7     0.275 0.3397770     1.0000000            3
29  0.725     0.275 0.3397770     1.0000000            3
30   0.75     0.025 0.3278743     0.9786017            2
31  0.775     0.250 0.3278743     1.0000000            2
32    0.8     0.250 0.3278743     1.0000000            2
33  0.825     0.250 0.3278743     1.0000000            2
34   0.85     0.250 0.3278743     1.0000000            2
35  0.875     0.250 0.3278743     1.0000000            2
36    0.9     0.250 0.3278743     1.0000000            2
37  0.925     0.250 0.3278743     1.0000000            2
38   0.95     0.250 0.3278743     1.0000000            2
39  0.975     0.250 0.3278743     1.0000000            2
40      1     0.250 0.3278743     1.0000000            2

scGPS results

> nor2_scGPS_cluster$optimalClust$KeyStats
   Height Stability RandIndex ConsecutiveRI Cluster_count
1   0.025     0.250 0.9973368     1.0000000            10
2    0.05     0.250 0.9973368     1.0000000            10
3   0.075     0.250 0.9973368     1.0000000            10
4     0.1     0.250 0.9973368     1.0000000            10
5   0.125     0.250 0.9973368     1.0000000            10
6    0.15     0.250 0.9973368     1.0000000            10
7   0.175     0.250 0.9973368     1.0000000            10
8     0.2     0.250 0.9973368     1.0000000            10
9   0.225     0.250 0.9973368     1.0000000            10
10   0.25     0.250 0.9973368     1.0000000            10
11  0.275     0.025 0.9592107     0.9617593             8
12    0.3     0.025 0.9592107     1.0000000             8
13  0.325     0.025 0.6887011     0.7256560             7
14   0.35     0.025 0.6887011     1.0000000             7
15  0.375     0.025 0.6566005     0.9640588             6
16    0.4     0.075 0.6566005     1.0000000             6
17  0.425     0.075 0.6566005     1.0000000             6
18   0.45     0.075 0.6566005     1.0000000             6
19  0.475     0.025 0.6373315     0.9779964             5
20    0.5     0.025 0.6373315     1.0000000             5
21  0.525     0.025 0.4967156     0.8281841             4
22   0.55     0.150 0.4967156     1.0000000             4
23  0.575     0.150 0.4967156     1.0000000             4
24    0.6     0.150 0.4967156     1.0000000             4
25  0.625     0.150 0.4967156     1.0000000             4
26   0.65     0.150 0.4967156     1.0000000             4
27  0.675     0.150 0.4967156     1.0000000             4
28    0.7     0.025 0.4961135     0.9941510             3
29  0.725     0.100 0.4961135     1.0000000             3
30   0.75     0.100 0.4961135     1.0000000             3
31  0.775     0.100 0.4961135     1.0000000             3
32    0.8     0.100 0.4961135     1.0000000             3
33  0.825     0.025 0.3896436     0.8462418             2
34   0.85     0.175 0.3896436     1.0000000             2
35  0.875     0.175 0.3896436     1.0000000             2
36    0.9     0.175 0.3896436     1.0000000             2
37  0.925     0.175 0.3896436     1.0000000             2
38   0.95     0.175 0.3896436     1.0000000             2
39  0.975     0.175 0.3896436     1.0000000             2
40      1     0.175 0.3896436     1.0000000             2

[quanaibn]

We have developed a new version of clustering that is implemented in scGPS, with the new name: SCORE (Stable Clustering at Optimal Resolution). The ascend clustering has been updated as well, so you may want to install the latest version and check the results. Cheers

[lixin4306ren]

I did use the latest version of ascend and scGPS to conduct my analysis.
In ascend, I used runCORE function. In scGPS, I used CORE_scGPS function.

other attached packages:
 [1] scGPS_0.9.9                 dynamicTreeCut_1.63-1
 [3] ascend_0.9.6                SingleCellExperiment_1.4.0