If you are looking for software for the following purposes, you can choose any software such as GeneSpring, Partek, Qlucore, R/Bioconductor. You can do it somehow. I want to easily and quickly get the answer. I want to do the same analysis of a textbook or paper. I want to follow tutorials to complete my work.

• Omics Explorer 3.1 For Machine Learning
• Omics Explorer 3.1 For Mac Pro

However, if you think like below, Subio Platform is the solution. I had some experiences in data analysis, but I was not very much satisfied with the results. So I am looking for something else. I want to learn about omics data analysis from experienced analysts, not about the tool operation.

I need to share my data with collaborators to have discussions for biological discovery beyond my perspective. I want to make more use of my data as a long-term asset. You can do almost all analysis tasks on Subio Platform, without plug-ins. This demo shows how to accomplish the entire process with Subio Platform, free web tools (DAVID Functional Annotation) and Excel.  The statistics is only a tool.

0.05 cutoff is not biologically meaningful. Why is 0.49 ok and 0.51 not ok? This is a helpless and pointless argument. Selecting genes with The true omics data analysis is in your enormous trials and errors.

The program Omics Explorer has also been found in our database for this reason. The following tables provide information about the association of the program Omics Explorer with file extensions. If the program Omics Explorer can be used to convert the data, such information will also be provided. May 4, 2009 - In the Qlucore Omics Explorer suite the following versions have been. Qlucore Omics Explorer 3.1 (available both for Mac and Windows).

Subio Platform has all functions to help your doing so. Please take an Online Training for a full instruction of the data analysis. Details of each viewer. Scatter Plot (Measurement) View. Line Graph View is useful to visualizes changes of signals among Sample Groups. It's interactive and you can select genes just by drag and drop on the chart. The selected genes are emphasized in the Annotations table in the lower panel as well.

You can make a Measurement List of the selected genes by clicking on 'Save as Measurement List' button, which is next to the camera icon. Please request a free Online Support, if you don't know how to use it. Genome View is a genome browser which is essential to analyze data related to genomic locations, like ChIP-Seq, Methyl-Seq, tiling array, CGH array, ChIP-chip, methylation array and so on. Even if you analyze gene expression data, you can use this View to see if the up- or down- regulated genes are located closely or not. If you see dense area of such genes, it may indicates they were controlled by changes of chromosomal structure or epigenetic status. Please request a free Online Support, if you don't know how to use it.

Scatter Plot (Samples) View. Scatter Plot (Samples) View is for browsing PCA results, or samples according to your imported profiling.

Dots represent sample groups composed of the selected DataSet. Notice that selecting another DataSet changes plots on the chart. You can drag to select sample groups, and Samples involved in the selected Sample Groups are also superimposed in Sample Info tab in the lower panel, and Tree View.

These views interact each other. Please request a free Online Support, if you don't know how to use this.

Venn Diagram. Many software boasting 'user-friendly' have the automation which applies a routine of normalization and pre-processing on the data. However, the truth is that omics data are not uniform, and such automation does not work very well. The following analysis of the oddly processed data becomes terrible consequently.

The processing with R/Bioconductor has the same problem. We think you should not use automation for normalization at least until it evolves smart enough.

So what should (or can) the analysis software do? We think it is to support analysts' decision makings by the visual aid. So Subio Platform presents the raw data distribution patterns, and how it shows the effect of every step on the data.

Users can go through trials and errors or discuss with somebody to select the right way. We agree that this task is hard for beginners. However, they get to learn the limitation of the data through it, and it leads their correctly interpreting analysis results.

So we decided not to make software having automatic normalization, but to provide service that users can consult to experienced technical support for free. We support researchers who want to understand their data. Learn more on movies. Finding a proper sequence of normalization and pre-processing. Normalization is actually very tricky, because it heavily depends on characteristics of experimental data. That's why we think it can't be automated.

Users must make decision to apply suitable normalization and pre-processing to each series. Subio Platform is designed to help your decision making by visual aid, which you can see what happens on data at each step. Going through trials and errors is the best way to learn about omics data. You can recall pre-set, your saved or currently-applied scenario by one click. So you don't need to be afraid of collapsing data.

Try as much as you can, and we're happy to help you via online support. It makes your understanding the data in depth. Please take an Online Training for a full instruction of the data analysis. How to apply the paired T-test?

If you need 'Fill Missing Value' block, it meas the data needs a bit tricky procedure. RNA-Seq or ChIP-Seq data based on Next-Gen Sequencing technologies or proteomics data from Mass must involves lots of data lacks or '0' signals theoretically. You need to be careful about handling such data, and this demo shows how to make such data more understandable in the biological context with 'Fill Missing Values' block. Please request a free Online Support, if you don't know how to use this tool. What is interesting in omics data is you already measured genes on which you do not pay attention. Biological information or knowledge is tremendously increasing and what you can extract from the data now will not be the same in the future. You cannot thoroughly squeeze at a time.

Why don't you put the data in your toolbox for future use? You cannot make full use of the omics data with R or Excel. You know that new technologies are rising rapidly. One type of omics data tells you only one aspect of the super-complicated living system. So you will need to combine the old and new omics data to guess what is happening in the living system.

Subio Platform is technology-neutral, and it can handle any quantitative data conveying biological information. You can store various types of omics data generated over time and integratively analyze them with Subio Platform. By the way, tons of omics datasets are available via the internet. Subio Platform supports semi-automatic import data from or databases. Someone analyzed the data already though; nobody has extracted all knowledge from the data yet. If you keep such data in your Subio Platform as the reference, your toolbox becomes more powerful.

Data import; Experimental data files Importing An Excel Data. Importing Samples of RNA-Seq Data of Counts/FPKM/RPKM. 0:00 Open a table of RNA-Seq data with gene symbols with Excel. 0:50 Import samples of RNA-Seq data from the table. 1:30 Input sample information to the new samples. Importing Gene Annotations.

1:50 Download files of gene annotations from NCBI FTP site. 2:25 Edit geneinfo file to make it have a title row. 3:20 Import gene annotations from the edited geneinfo file.

4:10 Import Gene Ontology (GO) terms from gene2go file. 4:50 Download a file of genomic location information from UCSC genome browser site. 5:15 Import location information. Once you make such a platform with annotations, you can use it afterward as long as the RNA-Seq on the same gene set. So only you need to do is before 1:50. Please take an Online Training for a full instruction of the data analysis.

Importing Agilent Microarray Data. Importing 1-color (single channel) data files 0:00 Import Agilent 1-color experimental data files. 0:20 Define format for the data files, and save the format. 1:10 Input sample information for the samples. Importing 2-color (dual channel) data files 1:40 Firstly, import cy3/cy5 experiment files. (a part of dye swap design) 2:00 Define format for the cy3/cy5 data files, and save the format. You don't need the second import if you don't apply the dye swap design. 2:45 Secondly, import cy5/cy3 experiment files.

(the other part of dye swap design) 3:05 Load the previously saved format and modify for cy5/cy3 data. 3:45 Input sample information for the samples. Please take an Online Training for a full instruction of the data analysis. Importing Affymetrix GeneChip Data. 00:00 Download Affymetrix Expression Console Software from Affy's web site. 00:25 Launch Affymetrix Expression Console Software, and download the library. 00:40 Load the CEL files to be analyzed, and run an algorithm.

01:25 Export the pivot table of Signal, Detection and Detection P-value. (You can skip if you already have a table of signals.) 01:45 Import the pivot table into a corresponding platform. 01:55 Divide the pivot table into individual samples. 02:05 Define format of the samples and finish the process of importing samples. 02:25 Edit sample information to give proper sample names. 03:20 It's ready for creating a series to analyze.

Please take an Online Training for a full instruction of the data analysis. Data import: Public datasets Importing and analyzing public expression data sets from GEO. Subio Platform v1.20.5019 supports importing miRNA-Seq data of TCGA or TARGET projects from GDC site. It automatically import not only signal values, but also sample annotation.

So you can easily start analyzing or exploring the large omics data sets of a variety of cancers. You can combine with TCGA RNA-Seq gene expression data to find potential targets of miRNAs. Please take an Online Training for a full instruction of the data analysis. Importing and analyzing TCGA methylation data.

Let's think you ask somebody to analyze data. You know that the real biological experiment is far from the perfect.

So you, who know very well about the experimental design and assumptions, are needed to be involved in the decision makings during the data analysis. However, you are not often invited to the discussion and receive Excel and PDF files as a result.

The reality is that analysis workflow is a black box, and you don't know how they made decisions and if they are right. Even if you have raw data, it is not likely to happen that other analysts analyze the data to generate second opinions.

If so, it never works as a quality-assurance of the data analysis. So we developed implemented a new data sharing format named SSA containing all information about the dataset such as raw data files, gene annotation and other biological information, parameters and experimental information, final and intermediate results, data of associated experiments, reference papers. You can easily import the SSA file into Subio Platform by drag & drop.

It allows you and other researchers checking the quality of data itself and analysis steps. Additionally, you and collaborators can take over the analysis with Subio Platform of their own. Many people's eyes with different knowledge, skills or backgrounds drastically increase the chance of discovery than an individual perspective. Letting all members be involved in analyzing and discussing is the best education in the omics. It will nurture the significant strength of the team in the future.

The active data sharing is the long-term strategy. Learn more on movies. The deep sharing of omics data by SSA file. After you analyze omics data with wide variety of biological information, export all data (e.g. Gene annotations, sample information, experimental data, result of statistical analysis, PDFs and so on) into one archive (ssa) file. Your collaborators can completely reconstruct what you saw on their PCs just by drag-and-drop the ssa.

This is the easiest way of omics data sharing at the lowest cost. Please request a free Online Support, if you don't know how to do this. You cannot launch or log in to commercial data analysis software like GeneSpring after a license expires.

You have to keep paying to access to your own data. It can be an obstacle for turning data to long-term assets, sharing data and fueling open discussions. We sell plug-in licenses, and you can buy them for specific computers and periods. The big difference from other commercial tools is you still can see all the data even after a license expires, including analysis results created with plug-in tools. Moreover, we show above that you can continue intrinsically analyzing data without plug-ins. So do members who are received SSA files.

Not all members must buy plug-in licenses. Most of them can join the analysis and discussions without plug-ins.

It will bring significant changes to your team. Biologists do not leave the data analysis tasks to bioinformaticians but work with them. Bioinformaticians do not use jargons or formula but try to explain visually.

Many people join the discussion, and new ideas continuously emerge. We think such an in-depth co-working environment is the fundament of new life science which is one of the most challenging frontiers for human beings. Learn more on movies. Almost no researchers know Subio Platform, comparing to Excel, R, and GeneSpring. You might worry about using such unknown software. Subio set the policy that we do not spend money on sales or marketing, but on the development and user support. We do not hire salespersons, appear at trade shows or on media.

By avoiding spending our time and money on such things, we have been focusing on the development and improvement of Subio Platform. We have listened to users' feedbacks and implemented suggested idea. We are proud of the achievement of excellent usability as a result. Even though Subio Platform is not very popular, you can use the output for publications. If you get comments from referees, our technical support helps your reasonable justifying or logical modifications. Many users use it for ten years, and there are. Fewer Statistical Functions The design policy of Subio Platform is that wet biologists can use it easily.

So we select statistical functions very carefully. Although 'Highly sophisticated' statistical methods have strict restrictions on applicable conditions or assumptions, users tend to ignore them and wrongly use them. You know how much p-values are abused in the biological society. So we selected only general methods which can relatively widely be applicable. We also eliminated methods which became nonsense in the history of the microarray data analysis.

For example, z-score normalization used to be necessary due to the terrible quality of spotted dual-channel microarrays. However, it lost the value as microarray technologies mature. It became rather harmful because it cancels the difference of variance which is one of the relevant biological information.

Any methods have advantages and disadvantages, and the balance changes according to data characteristics. So it is dangerous to mimic a way of a textbook or paper. What we think most important is that users can choose the right methods to the data case by case. So we carefully eliminated means even if they often use in papers or textbook, rather than putting methods as many as possible.

However, we know sometimes you need those functions which we eliminated from Subio Platform. So you can work with both Subio Platform and R/Bioconductor together.

Subio Platform with interactive viewers and R with a comprehensive collection of statistical methods are the most potent combination. If you cannot make R scripts, please consider our. By the way, you might use commercial data analysis software having tools which Subio Platform does not have. And you might think you cannot move to Subio Platform because you need the tools. If so, please consider our.

Although we charge on the development, it can be reasonable if you take into account license fees of the following years. Learn more on movies.

Working with R/Bioconductor for further statistical analysis. Subio Platform is a free omics data browser, and you can add analysis tools by activating plug-ins. If you can use R and Bioconductor, you can use Subio Platform with such external tools, too. Even if you can't write code by yourself, maybe you can copy&paste to do it. Although this demo is in Japanese, you can get to know how to do it. If you compare analysis software, you may focus on statistical features.

However, they are not everything to make your work easy and efficient. Please consider the following points. Items Subio Platform or other commercial software + Excel + PDF Visual aid with interactive operations.

Omics Explorer 3.1 For Machine Learning

Statistical analysis tools, automation. To allow trials and errors, or exploratory data mining. Data management. Data sharing, co-working. Cost of using by team. Technical support, training. Publicity, familiarity.

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Omics Explorer 3.1 For Mac Pro

Contents This document describes how to use Web sites based on the Pathway Tools software from SRI International. Since multiple Web sites such as BioCyc, YeastCyc, AraCyc, and MouseCyc are all based on the same underlying software, the same usage instructions apply to all. (Note that differences in configuration and in software version may introduce some variability among sites). Please note that the desktop version of Pathway Tools that you can install locally provides some additional operations compared to the Web capabilities described here.