ERROR: VAE Model reconstruct the gene expression data

[greenelab]

ERROR: VAE Model reconstruct the gene expression data

[Sithara85]

I just tries a trick on the data. As I have seen Gregory's dataset had all gene expression distribution ranges from 0-1 so I have multiplied my log2cpm data with 0.01 so all values ranges from 0-1. Now I have better results to visualize, So I am wondering where we are setting the input tensors to range from 0-1.

[Sithara85]

New input_rnaseq_reconstruct:

XXbac-BPG248L24.12	TTN	RP11-290D2.6	JSRP1	RP11-115D19.1	HCG4P5	AC114271.2	RP3-394A18.1	ABALON	KB-1208A12.3	...	LNPK	NBPF15	ATP8B4	AC005522.7	CHID1	ARFRP1	NAPB	CTB-133G6.2	SPATA24	POU2F2
0.114204	0.115338	0.114985	0.106858	0.102436	0.109176	0.109893	0.109055	0.09779	0.107059	...	0.022572	0.020735	0.022362	0.020162	0.021934	0.019642	0.021585	0.020372	0.019506	0.020419
0.106944	0.105709	0.105965	0.104501	0.104928	0.102605	0.097524	0.097808	0.10184	0.098146	...	0.021987	0.020497	0.019832	0.018447	0.018818	0.019570	0.018344	0.020552	0.018087	0.020798

and gene_summary:

gene mean	gene abs(sum)
0.004425	0.027955
0.004736	0.023202
0.004456	0.022163
0.004741	0.020509
0.004347	0.017307

[gwaybio]

Thanks @Sithara85 - a couple things:

after disabling the eager execution to make your program work in Tensorflow 2.

Can you elaborate what your solution was? Perhaps others will see this and will be interested in knowing exactly what you had to change.

But when we use our gene expression data (which is log 2 cpm normalized data), I am getting all the reconstructed values as 1.0

The input data need to be normalized further to be in the range of 0-1. See process-data.ipynb for specific details.

Could you let me know if you can think of any issue with my input data shape.
Dimension: (3045, 10956)

I recommend reducing the number of gene features you're using. In process-data.ipynb, you will also see that we reduced gene dimensions by selecting the top 5,000 most variably expressed genes.

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