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Subio Platform runs slow. Is there a workaround?

The performance of using Subio Platform largely depends on memory and data access. So the high-speed memory (RAM) and M.2 SSD is the solution. Please read this for required memory size. CPU is not so important for this work. Intel Core i5-7400 or AMD Ryzen 5 1400 are enough. The M.2 SSD drive must to be a sufficient size to install both OS and Subio Platform. 

If you cannot take such options, please try followings.

Close Other Software:

If the computer runs many software, try to close them as much as possible. If many software run, Subio Platform can't get enough memory space.

Disable Views and Tabs:

You can disable views and tabs from View menu to omit drawing them.

Turning Views and Tabs On/Off

Subio Platform slows down if you load a very large data sets like million measurements or thousands of samples.

You can improve responsiveness by turning Views and tabs off. Subio Platform works like operation console without viewers.

After you complete anaylsis taks, you can turn viewrs and tabs on.

Drawing Mode: 

Preferences windows is accessible from Platform or Plug-in menu. "Quality" mode draws beautifully but slowly. You can switch to "Speed" mode. It draws rough but fast.

Preference - Graphic Mode

Fixing Scales of Chart:

The default setting is "max" and "min," which make Subio Platform search max and min number, and it consumes resources. You can set actual number for max and min to avoid it.

Reducing Number of Measurements:

And maybe it's better to reduce number of measurements. You can see several idea in this movie, it's quite tricky though. Please contact us before you actually try it by yourself.

TCGA PRAD, RNA-Seq & DNA Methylation Integration (Part 2)

TCGA's DNA Methyaltion data of 301 Prostate Adenocalcinoma patients, based on Illumina's HumanMethylation450 bead chip. A PC with 64GB RAM is not powerful enough to visualize 480k probes x 301 samles. So I'm going to show how to divide data to make it handleable. I hope it gives an idea to whom think they can't work with a big data set on their computers.

0:00 Getting clinical and DNA methylation array data from TCGA Data Portal.

0:50 Getting the annotation table of the Illumina methylation array from GEO site.

1:00 Editing the annotation table to separate into 3 to make it small.

3:05 Importing the separated annotation tables to create sub-platforms.

3:25 Importing experimental data of DNA methylation study.

4:10 Creating a Series and editing parameter to extract TCGA sample ID.

4:40 Filtering to remove probes which methylation status are unchanging.

5:45 Exporting the data of changing probes only.

6:15 Merging the data of changing probes from the separated 3 Series on Excel.

7:00 Importing the changing data into the platform of whole probes.

7:45 Creating a Series and restoring the sample IDs.

8:20 Covert data into Region Lists to make them available to tools in Advanced Plug-in.

8:40 Loading a genome of hg19 RefSeq genes.

9:20 Creating intervals to merge probes which are closely located and sharing similar values.

10:20 Exporting the intervals as BED files, and import them again as creating a platform of intervals.

11:00 Editing sample information to fill clinical information.

12:50 Creating a Series and editing parameters.

13:55 Editing DataSets as defining groups of samples.

15:10 Editing normalization scenario as pre-processing.

16:00 PCA on DNA methylation status.

17:00 Filtering to extract intervals which methylation status change between normal and tumor.

18:55 Applying Genomic Location Filter to extract genes having methylated or unmethylated intervals.

Please take an Online Training for a full instruction of the data analysis.