differential_signaling
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differential_signaling [2020/02/05 10:30] krian [Pathways] |
differential_signaling [2020/04/03 20:18] (current) |
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| In the input data panel, we must introduce the expression data. | In the input data panel, we must introduce the expression data. | ||
| {{ :differential_signaling_input.png?nolink |}} | {{ :differential_signaling_input.png?nolink |}} | ||
| - | The **[[https://en.wikipedia.org/wiki/Gene_expression_profiling|expression data]]** is a gene expression matrix provided by ourselves (see how to upload files in [[upload_your_data|Upload your data]]). | + | The **[[https://en.wikipedia.org/wiki/Gene_expression_profiling|expression data]]** is a gene expression matrix provided by ourselves (see how to upload files in [[upload_your_data|Upload your data]]).\\ When we select a gene expression file, the number of samples of this matrix will appear under the "file browser" button as shown below. |
| + | {{ ::diffnumbersamples.png?nolink |}} | ||
| ==== Design data panel ==== | ==== Design data panel ==== | ||
| The design data panel allows you to choose the kind of experiment you want to perform. You can choose between two kinds of experimental design: | The design data panel allows you to choose the kind of experiment you want to perform. You can choose between two kinds of experimental design: | ||
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| ==== Study information ==== | ==== Study information ==== | ||
| This panel includes some parameters in order to identify and save our study. | This panel includes some parameters in order to identify and save our study. | ||
| - | * **Output folder**: If we want to reorganize our studies we can select the folder in which we want save our report. By default the study will be saved in the home in a folder named "Differential_signaling_example-N", N is an integer number. | + | * **Output folder**: If we want to reorganize our studies we can select the folder in which we want save our report. By default the study will be saved in the home in a folder named "Differential_signaling_study-N", N is an integer number. |
| - | * **Study name**: We can give a name to our study. This is very useful to later identify it among the other studies listed in the //My studies// list.\\ The default study name is "Differential_signaling_example-N", N is an integer number. | + | * **Study name**: We can give a name to our study. This is very useful to later identify it among the other studies listed in the //My studies// list.\\ The default study name is "Differential_signaling_study-N", N is an integer number. |
| * **Description**: We can give a description to our study. | * **Description**: We can give a description to our study. | ||
| {{ :studyinfo.png |}} | {{ :studyinfo.png |}} | ||
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| {{ ::pathwaysreport1.png?nolink |}} | {{ ::pathwaysreport1.png?nolink |}} | ||
| - In the upper-right part of the visualization tool, all selected pathways from the differential signaling form are shown along with one or two arrows. These arrows indicate whether in one of the "Effector circuit" within each pathway a differential activation pattern between the two compared groups have been found.\\ The arrows will be colored if the differential activation is significant after the p-value adjustment (or unadjusted p-value, if the "Unadjusted" parameter has been selected) and depicted in grey if it isn't.\\ The arrow will point up or down if an up-regulation or down-regulation of the signal occurs between the circuits within that pathway.\\ The meaning of this up or down-regulation depends on the comparison performed, that normally would be case vs. control (comparison between selected class from the design file : condition 1 Vs condition 2).\\ Only one arrow is shown if all the effector circuits are whether up or down-regulated.{{ ::pathwaysreport2.png?nolink |}} | - In the upper-right part of the visualization tool, all selected pathways from the differential signaling form are shown along with one or two arrows. These arrows indicate whether in one of the "Effector circuit" within each pathway a differential activation pattern between the two compared groups have been found.\\ The arrows will be colored if the differential activation is significant after the p-value adjustment (or unadjusted p-value, if the "Unadjusted" parameter has been selected) and depicted in grey if it isn't.\\ The arrow will point up or down if an up-regulation or down-regulation of the signal occurs between the circuits within that pathway.\\ The meaning of this up or down-regulation depends on the comparison performed, that normally would be case vs. control (comparison between selected class from the design file : condition 1 Vs condition 2).\\ Only one arrow is shown if all the effector circuits are whether up or down-regulated.{{ ::pathwaysreport2.png?nolink |}} | ||
| - | - In the lower-right part of the tool will appear all the circuits in which a pathway (previously selected on the upper part -1-) can be decomposed. Once any of the circuit is clicked the nodes and interactions (edges) that form part of this circuit are highlighted in the pathway viewer.\\ In the visualization tool there are two types of objects, the nodes and the edges. | + | - In the lower-right part of the tool will appear all the circuits in which a pathway (previously selected on the upper part -1-) can be decomposed. Once any of the circuit is clicked the nodes and interactions (edges) that form part of this circuit are highlighted in the pathway viewer (), One example might be the red-highlighted circuit on the figure below.{{ ::effectorcircuithl.png?nolink |}} |
| + | - In the visualization part there are two types of objects, the nodes and the edges.{{ ::pathwaysreport3.png?nolink |}} | ||
| * The nodes represent the different proteins or metabolites that are responsible on passing the signal from the previous node to the next one. As commented before, the nodes can be plain or complexes. In the former it is only necessary the presence of one protein (although that same function can be done by several proteins), while in the complex ones it is necessary the presence of two or more proteins to pass the signal through.\\ The color on the nodes are the result of a differential expression analysis on the gene/s involved on performing that node's function. | * The nodes represent the different proteins or metabolites that are responsible on passing the signal from the previous node to the next one. As commented before, the nodes can be plain or complexes. In the former it is only necessary the presence of one protein (although that same function can be done by several proteins), while in the complex ones it is necessary the presence of two or more proteins to pass the signal through.\\ The color on the nodes are the result of a differential expression analysis on the gene/s involved on performing that node's function. | ||
| - | * The edges represent how the interactions between the different nodes are.\\ If the edge is an arrow then the previous node will be activating the next one, while if it ends with a vertical bar is the former node will inhibit the functionality of the following node. This interactions may be depicted in red or blue depending on the circuit they form part of (whether they are up-regulated or down-regulated). It may occur, that two or even three colors for a same edge are shown, but this only happens when a circuit is not yet selected on the lower-right part of the tool.\\ Once an certain circuit is selected all its edges will be colored in the same color depending on the result of the differential signaling activation analysis. | + | * The edges represent how the interactions between the different nodes are.\\ If the edge is an arrow then the previous node will be activating the next one, while if it ends with a vertical bar is the former node will inhibit the functionality of the following node. This interactions may be depicted in red or blue depending on the circuit they form part of (whether they are up-regulated or down-regulated). It may occur, that two or even three colors for a same edge are shown, but this only happens when a circuit is not yet selected on the lower-right part of the tool.\\ Once an certain circuit is selected all its edges will be colored in the same color depending on the result of the differential signaling activation analysis.{{ ::pathwaysreport4.png?nolink |}} |
| - | - blbl | + | - The top part contains the title of selected pathway,by clicking on this button {{::keggsource.png?nolink|}} you can see the original source of this pathway.\\ also you can find other button in the right side: |
| - | know more about [[pathway_viewer|Pathway viewer]] | + | * {{::search.png?nolink|}}Allow to search specific genes, proteins or functions. |
| - | * [[circuit values|circuit values]] | + | {{ ::searchgif.gif?nolink |}} |
| - | * [[Function based analysis|Function based analysis]] (If chosen) | + | * {{::export.png?nolink|}}Export a SVG image of viewed objects (the whole pathway or just the selected effector circuit) |
| + | * {{::center.png?nolink|}}Center the selected pathway | ||
| + | * {{::width.png?nolink|}}Width adjust | ||
| + | * {{::height.png?nolink|}}Height adjust | ||
| + | ==== Circuit values ==== | ||
| + | Here you can find additional tables and plots: | ||
| + | * The first section below there is a link to download the calculated circuit activity values. This matrix file indicates for each effector circuit the level of activation calculated using Hipathia method for each sample.{{ ::circuitvalue.png?nolink |}} | ||
| + | * The Heatmap plot represented on the hipathia results page is a heatmap of the activation values from the most differentiated effector circuits (rows) between groups along with a clustering of the samples (columns). This plot allows to observe if its possible to differentiate the groups that are compared according to its effector circuit activation values.\\ The colors depicted here indicates the level of activation for the different circuit in each sample, being the bluish ones those with lower activation levels and the reddish the highest ones (e.g. if a given cell of the heatmap is blue would indicate that this particular effector circuit is poorly activated in that determined sample) {{ ::heatmap.png?nolink |}} | ||
| + | * The next plot on the Hipathia report corresponds to a Principal Component Analysis plot. This figure is useful to determine if the activation levels of the pathways calculated with Hipathia are able to differentiate between the two groups that are being compared. {{ ::pca.png?nolink |}} | ||
| + | * In the section below there is a table (that can be downloaded) with the results of the differential activation analysis. This table indicates for each effector circuit whether or not there is a different level of activation depending on the group the samples belong. {{ ::circuitsig.png?nolink |}} | ||
| + | * **circuit/term**: Code identifying the circuit consisting in the name of the pathway to which it belongs and the effector (last) node of the Circuit (//pathway Name : Effector//). | ||
| + | * **UP/DOWN**: Up when the circuit is up-regulated in the disease case, DOWN otherwise. | ||
| + | * **statistic**: Statistic of the Wilcoxon test performed between conditions. | ||
| + | * **p.value**: P-value of the statistic test. | ||
| + | * **FDRp.value**: Adjusted p-value of the statistic test using the FDR method. | ||
| + | * **Fold Change**: is a measure describing how much the ** circuit activity** changes between conditions.[[https://en.wikipedia.org/wiki/Fold_change|reed more about Fold Change]] | ||
| + | * **logFC**: The logarithm to base 2 of calculated Fold change. | ||
| + | ==== Function based analysis ==== | ||
| + | For each of the functional analysis selected to be performed, the same descriptive statistics as in the Path values case are shown: | ||
| + | * **GO terms/Uniprot keywords values**: You can download the matrix of function values by clicking on //GO terms values// or //Uniprot keywords values//. | ||
| + | * **Heatmap**: A heatmap is a graphical representation of the values of a matrix. A not-supervised clustering is performed on the data per columns (samples). A good separation between the two colors of the upper bar of the heatmap indicates that the differences between the two groups are big enough to discriminate between them. | ||
| + | * **PCA**: A Principal Components Analysis is performed in order to see if the matrix contains information enough to separate between the two groups. | ||
| + | * **GO terms/Uniprot keywords significance**: Ordered table of the functions significance. More significant terms are displayed in the upper part of the table. For each functional term, the following information is shown: | ||
| + | * **path/term**: Name of the function. | ||
| + | * **UP/DOWN**: Up when the function is up-regulated in the disease case, DOWN otherwise. | ||
| + | * **statistic**: Statistic of the of the statistic test. | ||
| + | * **p.value**: P-value of the statistic test. | ||
| + | * **FDRp.value**: Adjusted p-value of the statistic test using the FDR method. | ||
| + | * **Fold Change**: is a measure describing how much the ** circuit activity** changes between conditions.[[https://en.wikipedia.org/wiki/Fold_change|reed more about Fold Change]] | ||
| + | * **logFC**: The logarithm to base 2 of calculated Fold change. | ||
differential_signaling.1580898647.txt.gz · Last modified: 2020/04/03 20:18 (external edit)
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