Merge pull request #14 from saezlab/revisions2021

Revisions2021

FUNKI/data/examples/carnival_dorothea_contrast.csv

@@ -0,0 +1,2 @@
+"IRF3","MYOD1","NCOA2","NR2F1"
+3.95485866684819,0.98647441291566,1.29967223364204,2.98656807153296

FUNKI/data/examples/carnival_dorothea_example.csv

@@ -0,0 +1,2 @@
+"","FOXM1","MYBL2","OTX2"
+"1",3.42571262040981,0.868284393839793,-1.81747953870413

FUNKI/data/examples/phospho_data.csv

@@ -1,4 +1,4 @@
-"","contrast_1"
+"ID_site","activity"
 "AAK1_S637",1.22263245488
 "AAK1_S678",-0.9508860477
 "ABCF1_S109",0.201773218988

FUNKI/data/models/carnival_PKN_contrast.csv

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+source,interaction,target
+NROb2,1,RXRB
+CSNK2A2,1,CSNK2A1
+RXRB,1,NR2F1
+CSNK2A1,1,AKT
+AKT,1,IKBKB
+IKBKB,1,IRF3
+IRBKB,1,EP300
+EP300,1,MYOD1
+EP300,1,NCOA2
+EP300,1,NR4A1
+NR4A1,1,NR2F1

FUNKI/data/models/carnival_PKN_example.csv

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+source,interaction,target
+LHX1,1,OTX2
+LHX9,-1,LHX1
+FOXA2,-1,OTX2
+AKT1,-1,FOXA2
+TCL1B,1,AKT1
+AKT1,1,CDK2
+CDK2,1,MYBL2
+CDK2,1,FOXM1
+CDK2,-1,PAK1
+PAK1,1,ELF3
+ELF3,1,EHF
+FMR1,1,PAK1

FUNKI/data/reports/carnival_report.Rmd

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+---
+title: "CARNIVAL Analysis Report"
+output: html_document
+params:
+  analysis_expl: ""
+  doro_custom: "**NOT** run."
+  organism: ""
+  minsize: ""
+  confidence_level: ""
+  method: ""
+  pro_custom: "**NOT** included in the analysis."
+  top: ""
+  perm: ""
+  omni: "**NOT** loaded."
+  targets: "**NOT** selected."
+  solver: "**NOT** selected"
+  pea_custom: "the enrichment part has **NOT** been run."
+  pea_explanation: ""
+  pea_expl_barplot: ""
+  pea_expl_volcano: ""
+  carnival_network: NA
+  barplot: NA
+  volcano: NA
+  active_pea: "FALSE"
+---
+
+[CARNIVAL](https://saezlab.github.io/CARNIVAL/) (CAusal Reasoning for Network identification using Integer VALue programming) reconstructs signalling networks from downstream TF activities by finding the upstream regulators ( [Liu et al., 2019](https://www.nature.com/articles/s41540-019-0118-z) ). Given a directed prior-knowledge network (PKN) of protein-protein interactions, which can also be signed, CARNIVAL identifies a subnetwork that explains the activities of transcription factors through potential perturbed intermediate genes.
+
+### The analysis
+
+`r params$analysis_expl`
+
+**DoRotEA** (_measObj_) was `r params$doro_custom`
+
+`r params$organism`
+
+`r params$minsize`
+
+`r params$confidence_level`
+
+`r params$method`
+
+**PROGENy** weights (_weightObj_) was `r params$pro_custom`
+
+`r params$perm`
+`r params$top`
+
+The **network** (_netObj_) was `r params$omni`
+
+The **perturbation nodes** (inputObj) were `r params$targets`
+
+The selected **solver** was `r params$solver`.
+
+#### The code
+
+```{r, eval=FALSE}
+carnival_result = runCARNIVAL( inputObj = iniciators,
+                               measObj = tfList, 
+                               netObj = cNET, 
+                               weightObj = progenylist, 
+                               solverPath = solver$spath, 
+                               solver = solver$solver,
+                               timelimit = 7200,
+                               mipGAP = 0,
+                               poolrelGAP = 0 )
+```
+
+### The results
+
+#### Network visualisation
+
+CARNIVAL produces a weigthed network that can be directly visualised. 
+
+```{r, echo=FALSE, fig.width=10}
+params$carnival_network
+```
+
+#### Enrichment analysis
+
+A pathway/set enrichment analysis can be run over the results.
+
+For this analysis, `r params$pea_custom`
+
+`r params$pea_explanation`
+
+`r params$pea_expl_barplot`
+
+```{r, echo=FALSE, fig.width=10, eval=as.logical(params$active_pea), error=FALSE, message=FALSE, warning=FALSE}
+params$barplot
+```
+
+`r params$pea_expl_volcano`
+
+```{r, echo=FALSE, eval=as.logical(params$active_pea), error=FALSE, message=FALSE, warning=FALSE}
+params$volcano
+```

FUNKI/data/reports/cosmos_report.Rmd

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+---
+title: "COSMOS Analysis Report"
+output: html_document
+params:
+  analysis_expl: ""
+  network: "**NOT** selected/loaded."
+  solver: "**NOT** selected."
+  layer1: "**NOT** selected."
+  layer2: "**NOT** selected."
+  cosmos_network: NA
+---
+
+COSMOS is a tool to integrate multiomic data with a prior knowledge network spanning signaling, gene regulation and metabolism ( [Dugourd et al. 2021](http://europepmc.org/abstract/MED/33502086) ). It uses the ILP formulation of CARNIVAL to connect two sets of upstream and downstream molecular features (e.i. kinase activities, TF activities, deregulated metabolites, enzyme fluxes, genetic or drug perturbations, etc…) with a signed directed transomic network. 
+
+This resulting network is essentially a set of coherent mechanistic hypotheses that can explain how the measured deregulation may explain each other. Subsets of this network centered on user-defined nodes can be viewed in the shiny app. The network can also be downloaded as a pair of sif and attribute csv files. These files can be imported in tools such as cytoscape to visualise the full network.
+
+### The analysis
+
+`r params$analysis_expl`
+
+The **layer 1** was `r params$layer1`
+
+The **layer 2** was `r params$layer2`
+
+The **network** (_netObj_) was `r params$network`
+
+The **solver** was `r params$solver`
+
+#### The code
+
+```{r, eval=FALSE}
+  cosmos <- run_COSMOS(layer_1 = layer_1, 
+                       layer_2 = layer_2, 
+                       RNA_data = data_vec, 
+                       PKN = PKN, 
+                       solver = input$solver_cosmos, 
+                       solver_path = solverpath_cosmos,
+                       runtime = c(200,1000,200,1000))
+```
+
+### The results: Network visualisation
+
+COSMOS produces a set of networks that can be directly visualised. 
+
+```{r, echo=FALSE, fig.width=10}
+params$cosmos_network
+```

FUNKI/data/reports/dorothea_report.Rmd

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+---
+title: "DoRothEA Analysis Report"
+output: html_document
+params:
+  minsize: NA
+  method: NA
+  selected_conf_level: NA
+  organism: NA
+  selected_tf: NA
+  selected_sample: NA
+  numberTFs: NA
+  sample_plot: NA
+  tf_plot: NA
+  network_plot: NA
+  heatmap_plot: NA
+---
+
+[DoRothEA](https://saezlab.github.io/DoRothEA/) (Discriminant Regulon Expression Analysis) is a resource that links transcription factors (TFs) with their downstream targets ([Garcia-Alonso et al., 2018, 2019](http://europepmc.org/abstract/MED/31340985)). 
+The unity of a TF and its targets is called regulon. The regulons are built from four different strategies:
+
+(i) manually curated interaction repositories
+(ii) interactions derived from ChIP-seq binding data
+(iii) in silico predictions of TF binding on gene promoters
+(iv) reverse-engineered regulons from gene expression datasets. 
+
+The TFs activities are computed from gene expression by performing an enrichment analysis ([Alvarez et al., 2016](https://www.nature.com/articles/ng.3593)), where the regulons are the underlying gene-sets.
+
+### The analysis
+
+The selected parameters:
+
+- organism: `r params$organism`
+- min size: `r params$minsize`
+- method: `r params$method`
+- confidence level: `r paste(params$selected_conf_level, collapse = ",")`
+
+The code:
+
+```{r, eval=FALSE}
+
+regulons <- dorothea_regulon %>%
+      dplyr::filter(confidence %in% params$selected_conf_level)
+
+dorothea::run_viper(dorothea_matrix, 
+                    regulons,
+                    options =  list(minsize = minsize,
+                                    method = method,
+                                    eset.filter = FALSE,
+                                    cores = 1,
+                                    verbose = FALSE,
+                                    nes = TRUE))
+```
+
+### The results
+
+The computation yields a matrix with the normalised enrichment scores for each TF across all samples.
+
+The heatmap provides a comprehensive overview of all contrasts/samples.
+This heatmap includes the top `r params$numberTFs` selected TFs for the sample **`r params$selected_sample`** compared with the rest of the samples.
+
+```{r, echo=FALSE, fig.width=10}
+params$heatmap_plot
+```
+
+The bar plots show the activity of **`r params$selected_tf`** for all given contrasts/samples;
+and the top `r params$numberTFs` regulated TFs for sample **`r params$selected_sample`**.
+
+```{r, echo=FALSE}
+params$tf_plot
+
+params$sample_plot
+```
+
+The interactions of `r params$selected_tf` with its targets is shown in a network plot for sample `r params$selected_sample`.
+
+Blue nodes indicate that the target is over-expressed and red nodes indicate that the target is down-regulated. The color of the edges represent the effect of the TF on its target (either activation or repression). The number of shown nodes can be changed by the corresponding widget.
+
+```{r, echo=FALSE}
+params$network_plot
+```
+

FUNKI/data/reports/kinact_report.Rmd

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+---
+title: "KinAct Analysis Report"
+output: html_document
+params:
+  minsize: NA
+  method: NA
+  organism: NA
+  selected_kinase: NA
+  selected_sample: NA
+  numberKinases: NA
+  sample_plot: NA
+  tf_plot: NA
+  network_plot: NA
+  heatmap_plot: NA
+---
+
+[KinAct](http://saezlab.github.io/kinact/) is a resource linking kinases to phosphorylation sites ( [Wirbel et al., 2018](https://link.springer.com/protocol/10.1007%2F978-1-4939-7493-1_6 ). 
+It is fully integrated into Omnipath due to the addition of kinase-substrate interaction resources ( [Türei et al., 2021, 2016](http://europepmc.org/abstract/MED/27898060) ). 
+Kinase activity estimation is performed using the same algorithm as DoRothEA to estimate activity scores ( [Alvarez et al., 2016](https://www.nature.com/articles/ng.3593) ). Instead of TF-target interactions, KinAct uses collections of kinase-substrate interactions and phosphoproteomic data instead of transcriptomic data.
+
+### The analysis
+
+The selected parameters:
+
+- organism: `r params$organism`
+- min size: `r params$minsize`
+- method: `r params$method`
+
+The code:
+
+```{r, eval=FALSE}
+dorothea::run_viper(kinase_matrix, 
+                    kinact_regulon_human,
+                    options =  list(minsize = minsize,
+                                    method = method,
+                                    eset.filter = FALSE,
+                                    cores = 1,
+                                    verbose = FALSE,
+                                    nes = TRUE))
+```
+
+### The results
+
+The computation yields a matrix with the normalised enrichment scores for each Kinase across all samples.
+
+The heatmap provides a comprehensive overview of all contrasts/samples.
+This heatmap includes the top `r params$numberKinases` selected TFs for the sample **`r params$selected_sample`** compared with the rest of the samples.
+
+```{r, echo=FALSE, fig.width=10}
+params$heatmap_plot
+```
+
+The bar plots show the activity of **`r params$selected_kinase`** for all given contrasts/samples;
+and the top `r params$numberKinases` regulated TFs for sample **`r params$selected_sample`**.
+
+```{r, echo=FALSE}
+params$tf_plot
+
+params$sample_plot
+```
+
+The interactions of `r params$selected_kinase` with its targets is shown in a network plot for sample `r params$selected_sample`.
+
+Blue nodes indicate that the target is over-expressed and red nodes indicate that the target is down-regulated. The color of the edges represent the effect of the TF on its target (either activation or repression). The number of shown nodes can be changed by the corresponding widget.
+
+```{r, echo=FALSE}
+params$network_plot
+```