diff --git a/vignettes/ConformationalSignatureAnalysis.Rmd b/vignettes/ConformationalSignatureAnalysis.Rmd new file mode 100644 index 0000000..46678bf --- /dev/null +++ b/vignettes/ConformationalSignatureAnalysis.Rmd @@ -0,0 +1,571 @@ +--- +title: "Conformational Signature Analysis" +author: + - name: Oliver Crook + affiliation: + - University of Oxford + email: ococrook@gmail.com +output: + BiocStyle::html_document: + self_contained: yes + toc: true + toc_float: true + toc_depth: 2 + code_folding: show +date: "`r doc_date()`" +package: "`r pkg_ver('RexMS')`" +vignette: > + %\VignetteIndexEntry{Conformational Signature Analysis with RexMS} + %\VignetteEngine{knitr::rmarkdown} + %\VignetteEncoding{UTF-8} +--- + +# Introduction + +This vignette explains how to perform conformational signature analysis. +The essential idea is when you have numerous states in which you performed +HDX-MS and potentially a downstream outcome, you can use the conformational +signature analysis to identify the regions of the protein that are most +likely to be associated for the observed outcome. There are three options + +1) **Unsupervised Conformational Signature Analysis** - This is the most basic +form of the analysis. It will identify the regions of the protein that are most variable +across the states. This is useful for identifying regions that are associated +with the variation in the data. +2) **Supervised Conformational Signature Analysis with discrete outcomes** - This +is a more advanced form of the analysis using a discrete outcome such as "monovalent". +It will identify the regions of the protein that are most variable across +the states and are also associated with the outcome. This is useful +for identifying regions that are associated with the outcome. This is +done by dimensionality reduction that accounts for the outcome. +3) **Supervised Conformational Signature Analysis with continuous outcomes** - +This is a more advanced form of the analysis using a continuous outcome +such as K_d. It will identify the regions of +the protein that are most variable across the states and are also associated +with the outcome. This is useful for identifying regions that are associated +with the outcome. This is done by dimensionality reduction that accounts for +the outcome. + +The trickest part of the analysis is setting up the data correctly. You can do +this in a excel file and load as csv but we will demonstrate using R manually below. + + +# Getting started + +First we will load the library and the data. The data is a list of dataframes +where each dataframe is a state. Each dataframe should have the following columns + +1) Sequence - The peptide sequence +2) Start - The start of the peptide +3) End - The end of the peptide +4) Exposure - The timepoint of the HDX-MS experiment +5) Uptake - The deuteration of the peptide +6) State - The state of the HDX-MS experiment +7) MaxUptake - The maximum deuteration of the peptide +8) replicate - The replicate of the HDX-MS experiment + +This is as in the single protein analysis. We suggest performing that +analysis carefully first before proceeding to the conformational signature analysis. +We recommend storing the reference or apo state in a separate dataframe. +The analysis expects consistency between the experiments (e.g. replicates +and number of timepoints, region of the protein). You will also need to name +the states you are interested in and store them in a vector. + +```{r,} +library(RexMS) + +# for conformation signature analysis organise data into a list + +data("LXRalpha_compounds") # data for different compounds as a list +data("LXRalpha_processed") # data for the reference state + +# usual important terms +numTimepoints <- length(unique(LXRalpha_compounds[[1]]$Exposure)) +Timepoints <- unique(LXRalpha_compounds[[1]]$Exposure) +numPeptides <- length(unique(LXRalpha_compounds[[1]]$Sequence)) +R <- max(LXRalpha_compounds[[1]]$End) + +# names of compounds +states <- names(LXRalpha_compounds) + +# let look at states +states +``` +Our analysis has 18 compounds and a reference state. We will use the reference state +as the reference for the conformational signature analysis. Whichever route you +follow the inital steps are indentical. We will start with the list in an +`lapply` function. To each element in this list (which is an entire HDX dataset) +we apply the `rex` function. This function will perform the usual rex model. +There are only 3 timepoints in this analysis so we will set the `tCoef` to +`c(0, 1, 1)`. We will also set the seed to 1 for reproducibility. We will +use the `SerialParam` for the `BPPARAM` argument. This is because the analysis +is not too computationally intensive. We will store the results in a list. +For brevity we will run the analysis for 100 iterations but we recommend much +more (typically 5000) for final analysis. The following function take a while +to run so we will not run them here but pre-load the results from the package +the code is placed for completeness. + +```{r, eval=FALSE} +set.seed(1) +rex_lxr <- lapply(LXRalpha_compounds, function(xx) { + + rex(HdxData = DataFrame(xx), + numIter = 100, + R = R, + numtimepoints = numTimepoints, + timepoints = Timepoints, + seed = 1L, + tCoef = c(0, rep(1, 2)), + BPPARAM = SerialParam())}) + +``` + +The next step is to apply `RexProcess` to the output of the `rex` function. +Remember there are 18 compounds so we will use `lapply` again. We will set the +range to 50:100. This is mcmc iteration we are interested in. We will store the +results in a list. Again, we do not actually run the code here but pre-load the +results from the package. + +```{r, eval=FALSE} + +rex_lxr <- lapply(seq_along(rex_lxr), + function(i) + RexProcess(HdxData = DataFrame(LXRalpha_compounds[[i]]), + params = rex_lxr[[i]], + range = 50:100)) +``` + +We will now load the results from the package. The results are stored in the +`rex_lxr` object. + +```{r} +data("rex_lxr") +``` + +In the next step we would perform the same differential analysis as in the +differential analysis but 18 times because there are 18 compounds. We will +use the same reference state for each compound. We will store the results in +a list. Again, we do not actually run the code here but pre-load the results +from the package. Remember `params` are the various states and the +`HdxData` is the reference state HDX data. The `params` is the output of the +`rex` function, which has been processed. The `whichChain` is the chain of the +analysis you are interested in using to generate the results. We will use chain 1, +but recommend assessing the quality of multiple chains. + +```{r, eval=FALSE} +out_lxr_compound_proccessed <- lapply(rex_lxr, function(xx) + processDifferential(HdxData = DataFrame(LXRalpha_processed), + params = xx, + whichChain = 1)) +``` + +We will now load the results from the package. The results are stored in the +`out_lxr_compound_proccessed` object. + +```{r} +data("out_lxr_compound_proccessed") +``` + + +# Unsupervised Conformational Signature Analysis + +To perform the unsupervised conformational signature analysis, we will use the +`unsupervisedCSA` function. This function will take the output of the +`processDifferential` function. Whilst for unsupervised analysis you do not need +to specify the outcome, we will generate the labels results anayway to highlight +the details of setting up that analysis. + +For our protein there are 18 compounds and a reference state. Each of the compounds +induce one outcome in two scenarios or the outcome is unknown (which is allowed in +our analysis). We will generate the labels for the outcomes. We will store +the results in a dataframe with the states as the rownames. For proteins where +the outcome is unknown, we will set the outcome to "Unknown". For the two scenarios, +the first is ABCA1 induction and the second is lipogencity. We will set the +outcome to "low" or "high" for ABCA1 induction. We will set the outcome to +"Non-Lipogenic" or "Lipogenic" for lipogenicity. Of course, you may adapt these +outcomes to your own analysis. We will then convert the outcomes to factors +with the levels in the order we are interested in. You can of course +load this dataframe from something that was made in excel and covert to a +dataframe. + +```{r,} +# First set up the dataframe with unknowns and correct rownames +labels <- data.frame(ABCA1 = rep("Unknown", length(states)), + lipogenic = rep("Unknown", length(states))) +rownames(labels) <- states + +# Now set the outcomes with ABCA1 induction +labels$ABCA1[rownames(labels) %in% c("LXR.623", "AZ9", "AZ8", "AZ5")] <- "low" +labels$ABCA1[rownames(labels) %in% c("Az1", "AZ2", "AZ3", "AZ4", "AZ6", + "AZ7", "AZ876", "T0.901317", "WAY.254011", + "F1", "GW3965", "BMS.852927")] <- "high" + +# Now set the outcomes with lipogenicity +labels$lipogenic[rownames(labels) %in% c("AZ6", "AZ7", "AZ9", + "AZ8", "GW3965", "BMS.852927", + "LXR.623")] <- "Non-Lipogenic" + +labels$lipogenic[rownames(labels) %in% c("AZ876", "AZ1", + "T0.901317", "F1", "WAY.254011")] <- "Lipogenic" + +# Now convert to factors with the correct levels +labels$ABCA1 <- factor(labels$ABCA1, + levels = c("low", "high", "Unknown")) +labels$lipogenic <- factor(labels$lipogenic, + levels = c("Non-Lipogenic", "Lipogenic", "Unknown")) +``` + +We will now run the unsupervised conformational signature analysis. We will +set the `quantity` to "TRE" as this is the quantity we use to generate the signature. +We will set the `states` to the states we are interested in. We will set the +`whichTimepoint` to 600. This is the timepoint used to generate the conformational +signature. We will store the results in the `ucsa` object. Since this is the +unsupervised analysis we do not need to specify the `labels` argument. + + +```{r,} +ucsa <- UnsupervisedCSA(RexDifferentialList = out_lxr_compound_proccessed, + quantity = "TRE", + states = states, + whichTimepoint = 600) +``` + +We will now look at the results. The results are stored in a list with the +first entry `pca_states` being the Principal Component Analysis of the analysis. +The rotation matrix can be used as the conformational signature. This contains +all the results from a PCA analysis. The second entry `states_wide` is a dataframe +with the states as the columns and the residue as the rows. The values are the +TRE values that were used to generate the conformational signature. + +You can perform any downstream analysis you would like with the results. For +example, you could plot the PCA results. We will plot the first two components +of the PCA analysis. The labels are not used unless you pass them to the +`pca_params` argument even if you supply a `labels` argument. + +Note that the results look strange because we have not run the algorith for +enough iterations. + + +```{r,} +library(ggfortify) +# doesn't use the labels even though they are passed as an argument +plotUCSA(pca_states = ucsa$pca_states, + states_wide = ucsa$states_wide, + labels = labels) + +# uses labels to colour the points +plotUCSA(pca_states = ucsa$pca_states, + states_wide = ucsa$states_wide, + labels = labels, + pca_params = list(x = 1, # pca component 1 on x-axis + y = 2, # pca component 2 on y-axis + whichlabel = "ABCA1")) # colour by ABCA1 + + +``` + +# Supervised Conformational Signature Analysis with discrete outcomes + +Until now we have not used the labels as part of the analysis only to interpret +the results of the unsupervised analysis. Supervised conformational signature +analysis is more advanced and uses the labels to generate the conformational +signature. The idea is to identify the regions of the protein that are most +variable across the states and are also associated with the outcome. + +To perform the supervised conformational signature analysis with discrete outcomes, +we will use the `supervisedCSA` function. This function will take the output of the +`processDifferential` function. We will generate the labels for the outcomes as +before. We will store the results in a dataframe with the states as the rownames. +For proteins where the outcome is unknown, we will set the outcome to "Unknown". +For the two scenarios, the first is ABCA1 induction and the second is lipogencity. +We will set the outcome to "low" or "high" for ABCA1 induction. We will set the +outcome to "Non-Lipogenic" or "Lipogenic" for lipogenicity. Of course, you may adapt +these outcomes to your own analysis. We will then convert the outcomes to factors +with the levels in the order we are interested in. You can of course load this +dataframe from something that was made in excel and covert to a dataframe. + +We will now run the supervised conformational signature analysis with discrete +outcomes. We will set the `quantity` to "TRE" as this is the quantity we use to +generate the signature. We will set the `states` to the states we are interested in. +We will set the `whichTimepoint` to 600. This is the timepoint used to generate the +conformational signature. We will store the results in the `scsa` object. We +will set the `whichlabel` to "ABCA1" as this is the outcome we are interested in. +We will also set `whichlabel` to "lipogenic" to generate the conformational signature +for lipogenicity. Since this is the supervised analysis we need to specify the +`labels` argument from the dataframe we generated earlier. We will set the `orthoI` +to 1. This is the number of orthogonal components to use in the analysis. We will +set this to 1 for simplicity. We will store the results in the `scsa` object. + +The analysis used the labels to generate the conformational signature by using +orthogonal partial least squares discrimant analysis (OPLS-DA). The results return +a formal `opls` object. This object contains the results of the analysis. + +Note that the results look strange because we have not run the algorith for +enough iterations earlier. The results are allowed to contain "Unknown" outcomes. + +```{r,} +scsa <- supervisedCSA(RexDifferentialList = out_lxr_compound_proccessed, + quantity = "TRE", + states = states, + labels = labels, + whichlabel = "ABCA1", + whichTimepoint = 600, + orthoI = 1) + +scsa2 <- supervisedCSA(RexDifferentialList = out_lxr_compound_proccessed, + quantity = "TRE", + states = states, + labels = labels, + whichlabel = "lipogenic", + whichTimepoint = 600, + orthoI = 1) + +``` + + +We can now look at the results of the analysis. The results are stored in the +`opls` object. For each analysis, we can plot the loading of the conformational +signature. This is the regions of the protein that are most variable across the +states and are also associated with the outcome. We will plot the loadings of the +outcomes. The predictive component is the component that is associated with the +outcome. The orthogonal component is the component that is not associated with the +outcome. We will plot the loadings for ABCA1 and lipogenicity for both +predictive and orthogonal components. + +Note again the results look strange because we have not run the algorith for +enough iterations earlier. + +```{r,} +plotLoadingSCSA(states.plsda = scsa, + labels = labels, + whichLoading = "predictive", + whichlabel = "ABCA1") + +plotLoadingSCSA(states.plsda = scsa, + labels = labels, + whichLoading = "orthogonal", + whichlabel = "ABCA1") + +plotLoadingSCSA(states.plsda = scsa2, + labels = labels, + whichLoading = "predictive", + whichlabel = "lipogenic") + +plotLoadingSCSA(states.plsda = scsa2, + labels = labels, + whichLoading = "orthogonal", + whichlabel = "lipogenic") +``` + +You can also plot the scores of the conformational signature. This is the +scores of the samples on the predictive and orthogonal components. We will +plot the scores for ABCA1 and lipogenicity for both predictive and orthogonal +components. + +```{r,} +plotSCSA(states.plsda = scsa2, + labels = labels, + whichlabel = "lipogenic") +``` + + + +# Supervised Conformational Signature Analysis with continuous outcomes + +We can also perform the supervised conformational signature analysis with continuous +outcomes. This is similar to the discrete outcomes but the outcome is continuous. +To specify the labels we will generate the labels for the outcomes as before. We +will store the results in a dataframe with the states as the rownames. For proteins +where the outcome is unknown, we will set the outcome to `NA`. In this case, +we will use the `ED50` as the outcome. We will log the `ED50` values. You can of +course load this dataframe from something that was made in excel and covert to a +dataframe. We keep these values as numeric rather than factors. + +```{r,} +labels$ED50 <- NA +labels[, "ED50"] <- log(c(4.11, NA, NA, 0.956, NA, 9.64, 1.49, + 5.65, 0.969, 2.10, 11.3, NA, 31.5, 341, 32.2, 17.2, NA)) +``` + +We will now run the supervised conformational signature analysis with continuous +outcomes. We will set the `quantity` to "TRE" as this is the quantity we use to +generate the signature. We will set the `states` to the states we are interested in. +We will set the `whichTimepoint` to 600. This is the timepoint used to generate the +conformational signature. + +To perform the supervised conformational signature analysis with continuous outcomes, +we will use the `supervisedCSA` function. In this case, we will set the `type` to +"continuous" so that it does not perform a discrete analysis. We will set the +`whichlabel` to "ED50" as this is the outcome we are interested in. + +```{r,} +scsa3 <- supervisedCSA(RexDifferentialList = out_lxr_compound_proccessed, + quantity = "TRE", + states = states, + labels = labels, + whichlabel = "ED50", + whichTimepoint = 600, + orthoI = 1, + type = "continuous") +``` + +We can now look at the results of the analysis. The results are stored in the +`opls` object. We can plot the loading of the conformational signature. This is +the regions of the protein that are most variable across the states and are also +associated with the outcome. We will also plot the scores of the conformational +signature. This is the scores of the samples on the predictive and orthogonal +components. We will plot the loadings and scores for the `ED50` outcome. + +```{r,} + +plotSCSA(states.plsda = scsa3, + labels = labels, + whichlabel = "ED50", + type = "continuous") + labs(col = "log ED50") + +plotLoadingSCSA(states.plsda = scsa3, + labels = labels, + whichLoading = "predictive", + whichlabel = "ED50") + +plotLoadingSCSA(states.plsda = scsa3, + labels = labels, + whichLoading = "orthogonal", + whichlabel = "ED50") +``` + +# Advanced analysis + +Until now we havent used the uncertainty in the analysis. We can use the +uncertainty in the analysis to generate the conformational signature. We sample +from the distribution of the parameters to generate the conformational signature. +We will use the `sampleTREuncertainty` function. This function will take the outputs +of the analysis and sample from the distribution of the parameters to +quantify the uncertainty in the conformational signature. The important +argument below is the `num_montecarlo` which is the number of samples to take +from the distribution. We will set this to 5000. The ``whichSamples`` argument +is the samples to use from the orginal analysis. We will use the first 50 samples +- note that since the results have been processed the samples are actually the +first 50 samples of the processed results. + +We will not run the code here but pre-load the results from the package. + +```{r, eval=FALSE} +TRE_dist <- sampleTREuncertainty(HdxData = LXRalpha_processed, + RexParamsList = rex_lxr, + quantity = "TRE", + states = states, + whichChain = 1, + whichSamples = seq.int(1, 50), + whichTimepoint = 600, + num_montecarlo = 5000) + +``` + +We will now load the results from the package. The results are stored in the +`TRE_dist` object. + +```{r} +data("TRE_dist") +``` + +To plot the results we will use the `plotTREuncertaintyLoadings` function. This function +will take the output of the `sampleTREuncertainty` function and plot the results. +We also need to pass the `pca_states` object from the unsupervised analysis. We will +plot the results. This analysis reports the distribution of the loadings. + +```{r,} +gg <- plotTREuncertaintyLoadings(df_all = TRE_dist, + pca_states = ucsa$pca_states, + states = states) +gg + +``` +To visualise the results we can use the `plotTREuncertainty` function. This function +will take the output of the `sampleTREuncertainty` function and plot the results. +We also need to pass the `pca_states` object from the unsupervised analysis. +This plots the unsupervised analysis with the uncertainty as contours in the pca +coordinates. + +Again the results suffer because of the lack of iterations in the analysis. + + +```{r,} +plotTREuncertainty(df_all = TRE_dist, + pca_states = ucsa$pca_states, + states = states) + +``` + + +You may be interested in plotting the results of the analys on the structure. +This is the same set-up as before you simply need to make sure that you align +the residue numbers correctly + +```{r,} +# Using the predictive component of the supervised analysis as an example +library(NGLVieweR) +library(dplyr) + +v <- matrix(t(scsa@loadingMN), nrow = 1) + +# set colnames carefully to residue number by first remove the x from the rownames +# and then coverting to numeric +colnames(v) <- as.numeric(gsub("x", "", rownames(scsa@loadingMN))) +pdb_filepath <- system.file("extdata", "2acl.pdb", mustWork = TRUE, + package = "RexMS") + +mycolor_parameters <- hdx_to_pdb_colours(dataset = v, + pdb = pdb_filepath, + cmap_name = "CSA", scale_limits = c(-0.2,0)) + +graphics <- NGLVieweR(pdb_filepath) %>% + stageParameters(backgroundColor = "white", zoomSpeed = 1) %>% + addRepresentation("ribbon") %>% setQuality("high") %>% + addRepresentation("ribbon", param = list(color=mycolor_parameters, + backgroundColor="white")) + +graphics + + + +``` + + + +To extract the information from the uncertainty quantification analysis: + + +```{r,} +# Using the lower quantile as an example +loadingquants <- gg$data + +v <- matrix(as.numeric(loadingquants[loadingquants$level == 0.025, + "quantile", drop = FALSE][[1]]), nrow = 1) +colnames(v) <- seq.int(ncol(v)) +pdb_filepath <- system.file("extdata", "2acl.pdb", mustWork = TRUE, + package = "RexMS") + + +mycolor_parameters <- hdx_to_pdb_colours(dataset = v, + pdb = pdb_filepath, + cmap_name = "CSA") + +graphics <- NGLVieweR(pdb_filepath) %>% + stageParameters(backgroundColor = "white", zoomSpeed = 1) %>% + addRepresentation("ribbon") %>% setQuality("high") %>% + addRepresentation("ribbon", param = list(color=mycolor_parameters, + backgroundColor="white")) + +graphics + +``` + + + + + + + + + diff --git a/vignettes/DifferentialRexMS_cache/html/__packages b/vignettes/DifferentialRexMS_cache/html/__packages deleted file mode 100644 index c861f31..0000000 --- a/vignettes/DifferentialRexMS_cache/html/__packages +++ /dev/null @@ -1,14 +0,0 @@ -base -methods -datasets -utils -grDevices -graphics -stats -BiocStyle -RefManageR -stats4 -BiocGenerics -S4Vectors -RexMS -dplyr diff --git a/vignettes/DifferentialRexMS_cache/html/unnamed-chunk-1_ff2bae1f1a5676c79a43b61c74c52676.RData b/vignettes/DifferentialRexMS_cache/html/unnamed-chunk-1_ff2bae1f1a5676c79a43b61c74c52676.RData deleted file mode 100644 index 0ea4669..0000000 Binary files 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