They can be obtained in HQJHQH format directly from the home page at:

Transcription

1 HQJHQH70 *XLGHG7RXU This document contains a Guided Tour through the HQJHQH platform and it was created for training purposes with respect to the system options and analysis possibilities. It is not intended for training about the biological interpretation of the results. Original data used in this demo correspond to Kobayashi et al. (2001), Comprehensive DNA microarray analysis of Bacillus subtilis two-component regulatory systems. J Bacteriol. 183(24): and it is available in the web at: They can be obtained in HQJHQH format directly from the home page at: 1RWH: this file contains a complete demo. However, it will be modified and extended as new algorithms will be available, and as new experience with the system will be reported. Please, submit any recommendation or suggestions to our webmaster from the HQJHQH-home page, or directly to pascual@cnb.uam.es

2 *XLGHG7RXUZLWK.RED\DVLGDWD Once you have enter your 8VHU,GHQWLILFDWLRQ and your 3DVVZRUG, you are on your home directory. Here, or in a another subdirectory, you have to upload your data file ( NRER\DVLGDW for this demo). Remember that the file format must be the HQJHQH format. To upload the file you must specify the data file name and the complete path in the text box next to the Upload button. Alternatively, you can use the Search button to search for the file. After that, click on the Upload button to start uploading. )LJXUH Current Directory list containing all files generated by the system. The uploading process may last a few minutes. The following message appears when the process is successfully finished: )LJXUH Uploaded message Hit the continue button to return to the home directory. Now the kobayashi.dat file appears in the list:

3 )LJXUH: Current Directory list, with the new uploaded file By clicking on the file name you can see all the processes that you can be executed on a data file, as well as the data file information: )LJXUH : kobayashi.dat, with its overview representation (on the left), available operations (in the upper zone) and related information about this file.

4 Let s now start running the different available processes. 3UH3URFHVVLQJDOJRULWKPGDWDVFDOHGE\/RJ By clicking on the 3UHSURFHVVLQJOption on the Pre-Processing panel (see Figure 4), you will be driven to the PreProcessing Parameters page. )LJXUH Pre-processing Parameters Page (Logarithm base =2) The only required parameter is the output data file name without extension (for example, Kob_Log2). Type in the Logarithm Base text box and then click the 3URFHHG button. If the process is successful, you will be taken back to the directory list where a new entry has been added: the new generated file, whose extension is also GDW ( Kob_log2.dat ) You can click the file name to see the new data information. See how the data visualization has changed (due to the nice properties of log transformation). )LJXUHData Visualization of kobayashi scaled data (logarithm base = 2)

5 3UHSURFHVVLQJDOJRULWKPUHPRYHIODWYHFWRUVZKRVHVWDQGDUGGHYLDWLRQLVOHVVWKDQ In the directory list click kok_log2.dat file, and select the 3UHSURFHVVLQJ option again. Then, on the Preprocessing Parameters Page (Figure 5), type kob_3 as the Output Name, 0.5 as the 6WDQGDUG GHYLDWLRQILOWHUand 3URFHHG. Look at the new file (kob_3.dat) information by clicking on the file name. The new generated file has now 708 vectors (genes) that have passed the standard deviation filter. )LJXUH (on the left) Data Visualization of kob_3 file 9DOXHV+LVWRJUDP Select file Kob_3.dat, and click the 9DOXH +LVWRJUDP Option on the Statistical Analysis panel. Specify the output file name only (Kob_4). Then click the 3URFHHG button. )LJXUH Values Histogram Parameters page. The resulting file will have a.vh extension ( kob_4.vh ). When you see it on the directory list, you can click on it to look at its information.

6 )LJXUH: Values Histogram file Information. 3UHSURFHVVLQJDOJRULWKPQRUPDOL]LQJWKHGDWD In the directory list, click kok_3.dat file, and select the 3UHSURFHVVLQJoption again. Then, on the Preprocessing Parameters Page (Figure 5), type kob_5 as the Output Name and select the 1RUPDOL]H Option of the &HQWHULQJParameter and then 3URFHHG. )LJXUH: Preprocessing Parameters: Normalize option. After the data normalization process is finished, you will have a new data file with.dat extension ( kob_5.dat ). The file with the same number of vectors (708), but with normalized expression values. 9DOXHV+LVWRJUDPRQ.REBGDW Repeat the process described in step 3 with the normalized data. Name the output file as Kob_6. And see the differences with the Kob_4.vh file

7 )LJXUH: Values Histogram file Information (normalized data) :RUNLQJZLWKQRWQRUPDOL]HGGDWDILOH.REBGDW +LHUDUFKLFDO&OXVWHULQJSURFHGXUH a) Using Simple Average Linkage and Euclidean Distance Click the kob_3.dat file on the directory list, then select the +LHUDUFKLFDO&OXVWHULQJoption on the Clustering panel. You will go to the Hierarchical Clustering Parameters page. )LJXUH: Hierarchical Clustering Parameters page. Type the Output File Name: kob_3_1. Select the Simple $YHUDJH /LQNDJH option in the Agglomerative Method parameter and the (XFOLGHDQoption of the Distance parameter. Then 3URFHHG. The output file will have an.ht extension ( Kob_3_1.ht )

8 )LJXUH: Kob_3_1.ht hierarchical file view. b) Using Simple Average Linkage and Correlation Distance Click on the kob_3.dat file, then select the +LHUDUFKLFDO&OXVWHULQJoption on the Clustering panel. You will go to the Hierarchical Clustering Parameters page. Similar to the previous example, type the Output File Name: kob_3_2, select the Simple $YHUDJH/LQNDJHoption of the Agglomerative Method parameter and the &RUUHODWLRQoption of the Distance parameter. Then 3URFHHG. The output file has an.ht extension ( Kob_3_2.ht ) )LJXUH: Kob_3_2.ht hierarchical file view. You can now appreciate the differences between both trees, due to different distance metric used (Euclidean versus Correlation).

9 620SURFHGXUH a) Using Euclidean Distance Click on the kob_3.dat file in the directory list, then select the 620 option on the Projection Methods panel. You are driven to the Projection Methods Parameters page. )LJXUH: SOM Parameters page. Type the Output File Name: kob_3_3. The rest of the parameters are set by default, including the Euclidean distance. Then 3URFHHG. The output file has a.map extension ( Kob_3_3.map ). By clicking on it you will see the map visualization. For map files, three or four graphical representations are displayed: the code vectors profiles, the Sammon projection of the code vectors in the map, the sorted expression matrix, and, optionally, the Principal Component Projection (you must first create a.pc file, by clicking on the link Create under the message PC not created, and then review the map file). )LJXUH: The Sammon Projection. )LJXUH: The sorted expression matrix.

10 )LJXUH: The code vectors profiles. )LJXUH: The Principal Components Projection. b) Using Correlation Distance This example is similar to the previous one. You will realize a SOM projection on the same input file, but you must select the Correlation Distance parameter; the output file name will be kob_3_4. Then Proceed. The result is a kob_3_4.map. You can see the data graphical representation by clicking the file name..b0hdqvdojrulwkpfoxvwhuv a) Using Euclidean Distance

11 Click on the kob_3.dat file, then select the.0hdqvoption on the Clustering panel. You are driven to the K-Means Parameters page. )LJXUH: K-Means Parameters page. Type the Output File Name: kob_3_5 and the number of clusters: 10 (both parameters are required). The rest of the parameters are left with the default value (Distance = Euclidean). Then 3URFHHG. The output file has a.cb extension ( Kob_3_5.cb ). By clicking on it you will see the data visualization for the clustering results. Three or four graphical representations of data are displayed: the centroids profiles, the Sammon projection of the centroids, the sorted expression matrix, and, optionally, the Principal Component Projection (you must first create a.pc file, by clicking on the link Create under the message PC not created, and then review the clustering file).. )LJXUH: The Sammon Projection (on the left) and )LJXUH: The sorted expression matrix (on the right).

12 )LJXUH: The code vectors (centroids) profiles. )LJXUH: The Principal Components Projection. b) Using Correlation Distance This example is similar to the previous one. You can execute a K-Means Clustering over the same input file, but now selecting the Correlation Distance parameter; the output file name will be kob_3_6. Then 3URFHHG. The result is a kob_3_6.cb. You can now see the data graphical representation by clicking on the generated file name.