--- title: "Introduction to BatchQC" author: - name: W. Evan Johnson affiliation: - Division of Infectious Disease, Department of Medicine, Rutgers University - Director, Center for Data Science, Rutgers University email: wj183@njms.rutgers.edu - name: Jessica McClintock affiliation: - Division of Infectious Disease, Department of Medicine, Rutgers University email: jessica.mcclintock@rutgers.edu date: '`r format(Sys.Date(), "%B %e, %Y")`' package: BatchQC output: BiocStyle::html_document vignette: > %\VignetteIndexEntry{Introdution to BatchQC} %\VignetteEncoding{UTF-8} %\VignetteEngine{knitr::rmarkdown} editor_options: chunk_output_type: console --- # Introduction Sequencing and microarray samples are often collected or processed in multiple batches or at different times. This often produces technical biases that can lead to incorrect results in the downstream analysis. BatchQC is a software tool that streamlines batch preprocessing and evaluation by providing shiny app interactive diagnostics, visualizations, and statistical analyses to explore the extent to which batch variation impacts the data. This is contained in a full R package which allows reproducibility of all shiny evaluations and analyses. BatchQC diagnostics help determine whether batch adjustment needs to be done, and how correction should be applied before proceeding with a downstream analysis. Moreover, BatchQC interactively applies multiple common batch effect approaches to the data, and the user can quickly see the benefits of each method. BatchQC is developed as a Shiny App, but is reproducible at the command line through the functions contained in the R package. The output of the shiny app is organized into multiple tabs, and each tab features an important part of the batch effect analysis and visualization of the data. The BatchQC interface has the following features: 1. Upload Data 2. Batch Correction/Normalization (can also check data distribution) 3. Experimental Design to view summary of data 4. Variation Analysis (including lambda statistic) 5. kBET 6. Numerous Visualization methods including: - Heatmap plot of gene expressions/Median Correlation Plot - Dendrograms - Principal Component Analysis (PCA) - Uniform Manifold Approximation and Projection (UMAP) 7. Differential Expression Analysis and Volcano Plot 8. Data Download to export any corrections or normalizations as an SE object `BatchQC()` is the function that launches the Shiny App in interactive mode. # Installation ## Bioconductor Version To begin, install [Bioconductor](http://www.bioconductor.org/) and then install BatchQC: ```{R, eval = FALSE} if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install("BatchQC") ``` ## Github Version To install the most up-to-date version of BatchQC, please install directly from GitHub. You will need the devtools package. You can install both of these with the following commands: ```{R, eval = FALSE} if (!require("devtools", quietly = TRUE)) { install.packages("devtools") } library(devtools) install_github("wejlab/BatchQC") ``` ## Load BatchQC and Launch Shiny App You should now be able to load BatchQC and launch the shiny app. ```{R load package} library(BatchQC) ``` ```{R Launch Shiny App, eval = FALSE, echo = TRUE} BatchQC() ``` # Example Usage Below is an example of how you may use the shiny app to analyze your data: ## 1. Upload data set Upon launching the shiny app, you will be on the "Upload Data" screen. From here, you may upload the following data: 1. **Your own data w/Count and Metadata file.** Count file should have sample ID as column and item of interest as row. Metadata should have sample ID as row and all meta data labeled in the column. Both files should be uploaded as .csv by selecting "Browse" and selecting the appropriate files from your computer. 2. **Your own data as a Summarized Experiment object.** Upload should be a .RDS file type and can be uploaded by selecting "Browse" and then the appropriate file from your computer 3. **An example data set.** See the examples vignette for additional information on each example data set A preview of the selected data will show up under the "Input Summary" and "Full Metadata" tab. Input summary shows the counts file/assay on the Input summary tab and the metadata on the "Full Metadata" tab. If a preview does not load as expected, please ensure your dataset is structured properly and a valid file type. **You MUST hit "Upload" for your data to be available to interact with the shiny app.** All shiny app functions can also be run from the command line; all command line execution require that your data is in a Summarized Experiment object. You can create a summarized experiment object from a features and metadata matrix. This would be done as follows: ```{r Create Sumarized Experiment Object} # Load package data examples data(signature_data) data(batch_indicator) # Create SE Object features <- signature_data metadata <- batch_indicator se_object <- BatchQC::summarized_experiment(features, metadata) # Name your assay SummarizedExperiment::assayNames(se_object) <- "log_intensity" # Ensure all relevat metadata varaibles are factors se_object$batch <- as.factor(se_object$batch) se_object$condition <- as.factor(se_object$condition) ``` Likewise, you can upload your own data into a summarized experiment object for use with command line functions by providing a data matrix and sample info matrix to the summarized_experiment function. The remainder of this documentation will utilize the TB example data set included in the package. This can be loaded from the command line as a summarized experiment object with the following: ```{R Load TB example data} se_object <- tb_data_upload() ``` For ease of reference, we will also create variables to store the assay we are interested in, the batch variable, and the covariable we are interested in. ```{R TB Variables} assay_of_interest <- "features" batch <- "Experiment" covar <- "TBStatus" ``` ## 2. Normalization/Batch Correction/Data Distribution Check You may choose to use these tools in a different order, although we recommend checking the distribution of your data before deciding the best batch correction method. you may also choose to explore your raw data with the various tools within BatchQC to confirm the presence of a batch effect prior to applying a batch correction method. In this example, we have applied a batch correction method based on the results of the distribution check, but you may choose to do further analysis before selecting (a) (or even comparing) batch correction method(s). ### Data Distribution Check (only available for whole number data, like counts) If you have raw count data, other data that is positive whole numbers, or can provide a backwards compatible positive whole number dataset, you may use the Negative Binomial Check and/or the AIC model to check the distribution of your data to aid in making informed downstream analysis choices that are appropriate for the distribution of your data. These tools are found on the Batch Correction/Normalization tab as we recommend utilizing these tools to inform your choice of batch correction method. ```{R AIC Model Fit} TB_AIC <- compute_aic(se = se_object, assay_of_interest = assay_of_interest, batchind = batch, groupind = covar, maxit = 1000, zero_filt_percent = 15 ) TB_AIC["total_AIC"] TB_AIC["min_AIC"] ``` The lowest AIC score represented by "total_AIC" indicates the model that is likely the best fit. However, the voom/limma model often overfits the data, so we also look at the "min_AIC" parameter. The "min_AIC" parameter is a count of which distribution each individual gene fits best. Therefore, the model that has the highest number is the model that we would recommend utilizing. In this dataset the lowest "total_AIC" is voom, followed by log normal. Since limma often over corrects data, we can then look at "min_AIC" and it seems most genes fit a log normal distribution, so we should consider using ComBat as our correction method. We can further confirm that a negative binomial distribution is not a good fit by running the DESeq2 negative binomial check. ```{R Neg Binomial Model Fit TB Data} # Set a seed if you would like reproducible results set.seed(1) TB_fit <- goodness_of_fit_DESeq2(se = se_object, count_matrix = assay_of_interest, condition = covar, other_variables = batch, num_genes = 3000) TB_fit$res_histogram TB_fit$recommendation ``` The recommendation states that DESeq is not a good option, therefore a negative binomial distribution is not a good fit. ### Apply Normalization methods After you have successfully uploaded your data set of interest, you can apply one of the following Normalization methods if appropriate: 1. **CPM or counts per million.** CPM calculates the counts mapped to a feature relative to the total counts mapped to a sample times one million. CPM may be used to adjust expression count biases introduced by sequencing depth. CPM adjusted values are not recommended for differential expression analysis or within sample comparison. 2. **DESeq.** DESeq calculates the counts mapped to a feature divided by sample- specific size factors. Size factors are determined by the median ratio of gene counts relative to the geometric mean per feature. DESeq may be used to adjust expression count biases introduced by sequencing depth and RNA composition. DESeq adjusted values are not recommended for within sample comparison. 3. **edgeR.** edgeR calculates scale factors using a trimmed mean of M-values between each pair of samples and multiplies the scale factors with the original library size to get the normalized library size. This normalization method should be selected when you plan to use edgeR for differential expression analysis. The original read (raw) counts should be the provided input for this normalization method. 4. **voom.** Voom calculates logCPM, estimates mean-variance relationship and uses this to compute observation-level weights. It is appropriate for count data that is to be used with limma batch correction. To use one of these two methods, select it from the normalization method drop down, select an assay to perform the normalization on from the drop down, and name the new assay (the prepopulated name can be changed to your preference). You may also choose to log-transform the results of either of these normalization methods or your raw data by selecting the "Log transform" box. Hit "Normalize" to complete the analysis. Your new assay will now be available for further analysis. Alternatively, this can be called directly from the command line on an SE object and will return the same SE object with an additional assay with the name that you provide as the output_assay_name. Our TB data set is raw count data and to utilize ComBat we will need normalized data. We will utilize log_CPM normalized data for ComBat. However, examples are provided below on how you would complete many different normalization methods if needed via the command line: ```{r} # CPM Normalization se_object <- BatchQC::normalize_SE(se = se_object, method = "CPM", log_bool = FALSE, assay_to_normalize = assay_of_interest, output_assay_name = "CPM_normalized_counts") # CPM Normalization w/log se_object <- BatchQC::normalize_SE(se = se_object, method = "CPM", log_bool = TRUE, assay_to_normalize = assay_of_interest, output_assay_name = "CPM_log_normalized_counts") # DESeq Normalization se_object <- BatchQC::normalize_SE(se = se_object, method = "DESeq", log_bool = FALSE, assay_to_normalize = assay_of_interest, output_assay_name = "DESeq_normalized_counts") # DESeq Normalization w/log se_object <- BatchQC::normalize_SE(se = se_object, method = "DESeq", log_bool = TRUE, assay_to_normalize = assay_of_interest, output_assay_name = "DESeq_log_normalized_counts") # log adjust se_object <- BatchQC::normalize_SE(se = se_object, method = "none", log_bool = TRUE, assay_to_normalize = assay_of_interest, output_assay_name = "log_normalized_counts") ``` ### Apply Batch Effect Correction Select the appropriate Batch Effect correction model to create a batch-corrected assay for your data. You may select one of the following: 1. **ComBat-Seq.** ComBat-Seq uses a negative binomial regression to model batch effects. It requires untransformed, raw count data to adjust for batch effect. Only use this model on an untransformed raw counts assay (not normalized assays). 2. **ComBat.** ComBat corrects for Batch effect using a parametric empirical Bayes framework and data should be cleaned and normalized. Select a normalized assay to run this on. 3. **limma.** limma fits a linear model to the data and then removes components due to batch effect. It should be used on log transformed data. 4. **sva** sva identifies and estimates surrogate variables for unknown sources of variation. It is appropriate when you know your experimental variable of interest (like the disease state of samples), but unknown sources of variation. It should be run on cleaned and log transformed data. This can be run with or without additional adjustment variables. 5. **svaseq** svaseq is a variant of sva specifically for sequencing data. Data should still be normalized. To apply your desired batch effect correction model, select the method from the drop down menu, then select the appropriate assay in the second drop down menu, select your batch variable (typically labeled batch in built-in examples), and the covariates that you would like to preserve (not including the batch variable). If desired, revise the name for the corrected assay and then hit "Correct." A progress bar will pop up in the bottom right hand corner and a message will state "Correction Complete" in the bottom right hand corner when complete. You are now ready to analyze your raw and batch effect corrected data sets! The distribution analysis indicated that our TB data follows a log normal distribution, so ComBat is likely an appropriate batch correction tool. We will use the log-CPM assay since ComBat requires normalized data. Therefore, to run from the command line use the following: ```{r} # ComBat correction se_object <- BatchQC::batch_correct(se = se_object, method = "ComBat", assay_to_normalize = "CPM_log_normalized_counts", batch = batch, covar = c(covar), output_assay_name = "ComBat_corrected") ``` ComBat-Seq (or limma or sva or svaseq) can be applied to a data set by providing "ComBat-Seq" (or "limma" or "sva" or "svaseq") to the method parameter in the function call. ## 3. Experimental Design ### Batch Design This tab allows you to view the batch status of each condition. Select the variable that represents your batch variable and then select the variable that represents the condition. A table will then appear listing each condition in the row and how many samples in each condition are in each batch (displayed in the columns). To recreate this table, run the following code, which will produce a tibble as output: ```{R} batch_design_tibble <- BatchQC::batch_design(se = se_object, batch = batch, covariate = covar) batch_design_tibble ``` ### Confounding Statistics This tab displays the Pearson correlation coefficient and Cramer's V estimation based on the selected batch and condition. This table can be recreated with the following code: ```{R} confound_table <- BatchQC::confound_metrics(se = se_object, batch = batch) confound_table ``` #### Pearson Correlation Coefficient Pearson correlation coefficient indicates the strength of the linear relationship between your batch and condition variables. It can range from -1 to 1. The closer the value is to 0, the less likely there is a batch effect. The closer the value is to -1 or 1, the more likely there is a batch effect. To calculate only the pearson correlation coefficient, run the following: ```{R} pearson_cor_result <- BatchQC::std_pearson_corr_coef(batch_design_tibble) pearson_cor_result ``` #### Cramer's V This is an additional metric for batch effect and will be between 0 and 1. The closer the value is to 0, the lower the batch effect. The closer the value is to 1, the greater the batch effect To calculate only Cramer's V, run the following: ```{R} cramers_v_result <- BatchQC::cramers_v(batch_design_tibble) cramers_v_result ``` If both the Pearson Correlation Coefficient and the Cramer's V test indicate low batch effect, you likely do not need to adjust your results for batch. However, if one or both metrics indicate some or high batch effect, you should use additional analysis tools (listed below) to explore the need for a batch correction for your results. ## 4. Variation Analysis First, select your unadjusted assay, your batch variable and any covariates of interest. Click "Here we go!" to see the variation analysis results. The "Individual Variable" tab shows the individual, or raw variation, explained by each variable. The "Residual" tab displays the type 2, or residual, variation for each variable. A box plot is displayed along with a table with the variation specific to each gene listed. This table is searchable and can be displayed in ascending or descending order. Notice the amount of variation attributed to batch. In our signature data set, most of the variation is attributed to batch rather than the condition. This indicates a need for a batch correction. The "Individual Variation Variable/Batch Ratio" tab displays the individual, or raw variation, divided by the batch. A ratio greater that 1 indicates that batch has a stronger affect than the variable of interest. And the "Residual Variation Variable/Batch Ratio" displays the residual, or type 2 variation, divided by the batch. A ratio greater that 1 indicates that batch has a stronger affect than the variable of interest. Next, select the batch corrected assay, with the same batch and condition variable and observe the changes in variation. When batch effect is being properly corrected, you should notice that the variation explained by the batch variable has decreased in the batch corrected assay, indicating that the variation in the data is now more likely to be associated with your condition of interest rather than the batch. The ratio statistics should also be less than 1. This is visible in our example data set and confirms the need for batch adjustment. To recreate the variation analysis plot for the TB counts assay, use the following code: ```{r} ex_variation <- batchqc_explained_variation(se = se_object, batch = batch, condition = covar, assay_name = assay_of_interest) EV_boxplot <- BatchQC::EV_plotter(batchqc_ev = ex_variation[[1]]) EV_boxplot EV_boxplot_type_2 <- BatchQC::EV_plotter(batchqc_ev = ex_variation[[2]]) EV_boxplot_type_2 ``` To recreate the variation analysis table for the TB counts assay, use the following code: ```{r} ex_variation <- batchqc_explained_variation(se = se_object, batch = batch, condition = covar, assay_name = assay_of_interest) EV_table <- BatchQC::EV_table(batchqc_ev = ex_variation[[1]]) EV_table EV_table_type_2 <- BatchQC::EV_table(batchqc_ev = ex_variation[[2]]) EV_table_type_2 ``` We can then compare our batch corrected assay, using the following code: ```{r} ex_variation <- batchqc_explained_variation(se = se_object, batch = batch, condition = covar, assay_name = "ComBat_corrected") EV_boxplot <- BatchQC::EV_plotter(batchqc_ev = ex_variation[[1]]) EV_boxplot EV_boxplot_type_2 <- BatchQC::EV_plotter(batchqc_ev = ex_variation[[2]]) EV_boxplot_type_2 EV_table <- BatchQC::EV_table(batchqc_ev = ex_variation[[1]]) EV_table EV_table_type_2 <- BatchQC::EV_table(batchqc_ev = ex_variation[[2]]) EV_table_type_2 ``` ## 5. kBET To confirm the presence of batch effects in the simulated dataset, we use kBET, a method that quantifies batch effects through a chi-square test. kBET works by comparing the batch label distribution in each sample's local neighborhood to the global batch label distribution across the entire dataset. If these distributions differ significantly, it indicates the presence of batch effects. Below is how we would run the kBET analysis on the TB data set. ```{R kBET} TB_kBET <- BatchQC::run_kBET(se = se_object, assay_to_normalize = assay_of_interest, batch = batch) BatchQC::plot_kBET(TB_kBET) ``` The boxplot shows that the observed rejection rate is significantly higher than the expected rejection rate, indicating a strong batch effect in the TB dataset. ## 6. Visualizations BatchQC contains a number of visualization methods that may help you identify a batch effect (or to confirm that a batch effect has been removed). ### Heatmaps The heatmap tab contains options for a sample correlation heatmap or for a more traditional heatmap comparing samples and genes. Select your assay of interest and the batch and condition(s) of interest. We recommend viewing no more than 500 elements at a time (or the max number in your data set). #### Sample Correlations This heat map shows how similar each sample is to each other sample in your data (samples are plotted on both the x and y axis, so are identical to their self). The samples are clustered together based on similarity. Ideally, you would expect your samples to cluster and be more similar to samples from the same condition rather than from the sample batch. If there is clear visual clustering of the batch in your raw data, you should apply a batch correction method. Your batch corrected assay should have stronger clustering by condition. Using the TB dataset, we initially see clustering based on batch, but after apply ComBat, our adjusted data set clusters more based on condition. Use the following code to recreate the sample correlation heatmap: ```{r} heatmaps <- BatchQC::heatmap_plotter(se = se_object, assay = assay_of_interest, nfeature = 38, annotation_column = c(batch, covar), log_option = "FALSE") correlation_heatmap <- heatmaps$correlation_heatmap correlation_heatmap ``` #### Heatmap The heatmap shoes the sample clustering on the x axis and the y axis is each gene. The values on the heatmap represent gene expression. The dendrogram on the x axis is the same clustering arrangement as was seen in the sample correlations. This heatmap allows the viewer to see patterns of gene expression across the samples based on the selected variables. Remember that if batch is clustering together on your raw data, you should perform a batch effect correction to adjust your data. Use the following code to recreate the heatmap: ```{r} heatmaps <- BatchQC::heatmap_plotter(se = se_object, assay = assay_of_interest, nfeature = 38, annotation_column = c(batch, covar), log_option = FALSE) heatmap <- heatmaps$topn_heatmap heatmap ``` ### Dendrograms Within the dendrogram tab you can view either a traditional dendrogram or a circular dendrogram. #### Dendrogram This tab shows a more detailed dendrogram than seen in the heatmap. When samples cluster together by batch, you are likely to have a strong batch effect in your data and you should consider a batch correction method for your data. Samples are clustered based on similarity of gene expression and other metadata using an Euclidian distance metric. Labels will include the batch variable and one covariate of interest. Use the following code to recreate the dendrogram for the TB data set: ```{r} dendrogram_plot <- BatchQC::dendrogram_plotter(se = se_object, assay = assay_of_interest, batch_var = batch, category_var = covar) dendrogram_plot$dendrogram ``` #### Circular Dendrogram This circularizes the previous dendrogram to better show relatedness of all branches on the dendrogram without the appearance of great distance of the samples at the top and the bottom of the chart. Use the following code to recreate the circular dendrogram: ```{r} circular_dendrogram_plot <- BatchQC::dendrogram_plotter( se = se_object, assay = assay_of_interest, batch_var = batch, category_var = covar) circular_dendrogram_plot$circular_dendrogram ``` ### PCA Analysis You may select multiple assays to plot PCA analysis side by side. Therefore, select your raw data assay and batch corrected assay, the batch variable as shape, condition as color, and the number of top variable features you are interested in. Review the clustering pattern. In data sets where a strong batch effect is seen, similar shapes (batch) will all cluster together. Whereas if a batch effect is not seen, shapes (batch) should be dispersed throughout and you should see clustering by condition (color). Use the following code to recreate the PCA plot and associated table that lists the explained variation of each PC for our TB data set: ```{r} pca_plot <- BatchQC::PCA_plotter(se = se_object, nfeature = 20, color = batch, shape = covar, batch = batch, assays = c(assay_of_interest, "ComBat_corrected"), xaxisPC = 1, yaxisPC = 2, log_option = FALSE) pca_plot$plot pca_plot$var_explained ``` ### UMAP Analysis You may plot your data on a UMAP by selecting the assay, the batch variable, and selecting a value for nearest neighbors and minimum distance for the points to plot. The default for neighbors is 15 and minimum distance default is 0.1. We expect each batch to cluster together when there is a batch effect present. Otherwise, having dispersed points in each batch indicates there is not a strong batch effect. You can adjust the nearest neighbor parameter, a lower value will prioritize local structure and a higher value will represent bigger picture, but lose finer details. A higher minimum distance value will also put less emphasis on the global structure. Use the following code to recreate the UMAP for the TB data set: ```{r UMAP} umap_plot <- BatchQC::umap(se = se_object, assay_of_interest = assay_of_interest, batch = batch, covar = covar, neighbors = 15, min_distance = 0.1, spread = 1) umap_plot ``` This code creates a UMAP for the batch corrected assay: ```{r UMAP batch corrected} umap_plot_batch_corrected <- BatchQC::umap(se = se_object, assay_of_interest = "ComBat_corrected", batch = batch, covar = covar, neighbors = 15, min_distance = 0.1, spread = 1) umap_plot_batch_corrected ``` ## 7. Differential Expression Analysis To explore which features are differentially expressed between conditions, use the differential expression tab. DE_Seq2 should be used for count data (non-negative, integer values) with a negative binomial distribution, edgeR can be used for edgeR-normalized data, ANOVA can be used for normal-distributed data, Kruskal-Wallis test can be used as a nonparametric equivalent of one-way ANOVA, and limma can be used for all other data. edgeR implemented in BatchQC uses glm quasi-likelihood F-tests to test for differential expression. Our TB data is count data with a log normal distribution, so we will use limma. We will select the assay we are interested in, select the batch variable and all covariates that we would like to include in the analysis, and select a method to adjust p-values. The "Results Table" return a table for each condition within each variable. The results table displays the log2 fold change, p-value and adjusted p-value for each feature. The "P-Value Analysis" tab displays a box plot of p-values for each analysis. In both the "Results Table" and "P-Value Analysis" tabs, the tables can be sorted by ascending or descending order of a column of your choice. They are also searchable with the "Search" bar (which can be helpful if you are interested in a specific feature or p-value threshold). To run the limma differential expression analysis from the command line, run the following code (which can be modified to run DESeq2 when appropriate by changing the method argument to "DESeq2"): ```{r} differential_expression <- BatchQC::DE_analyze(se = se_object, method = "limma", batch = batch, conditions = c(covar), assay_to_analyze = assay_of_interest, padj_method = "BH") ``` After running the differential expression analysis, you can see all the completed analysis by running this code: ```{r} names(differential_expression) ``` You can then view the log2 fold change, p-value and adjusted p-value table for a specific analysis of interest, by running the following: ```{r} head(differential_expression[["TBStatusPTB"]]) ``` After running a differential expression analysis, you may replicate the p-value box plot and table from the command line by running the following code: ```{r} pval_plotter(differential_expression) head(pval_summary(differential_expression)) ``` ### Volcano Plot The "Volcano Plot" is based on the limma or DESeq2 analysis results and must be run after completing the differential expression analysis. You should select the analysis result that you are interested in, specify the threshold for p-value cut off (the default is 0.05), and the magnitude of expression change (the shiny default is the average of the absolute value of the minimum and maximum value in the data set). After clinking "Run," a volcano plot will appear. All values that are below the p-value threshold and exceed the set expression change threshold will appear in red, indicating that these may be potential features that differ between the conditions in that variable. In a data set with a batch effect, you will likely see many significant values in both your p-value analysis and in the volcano plot. When you apply a batch correction analysis, you should see more significant values in your covariate conditions than in your batch conditions and fewer significant values that meet the volcano plot thresholds. The volcano plot is interactive and you can select specific points to view the feature information, log2fold change, and -log10(p-value). To recreate the volcano plot for the "TBStatusPTB" assay (which you may change by using any of the named results assays) differential expression result from our uncorrected TB data , run the following code: ```{r} value <- round((max(abs( differential_expression[[length(differential_expression)]][, 1])) + min( abs(differential_expression[[length(differential_expression)]][, 1]))) / 2) volcano_plot(DE_results = differential_expression[["TBStatusPTB"]], pslider = 0.05, fcslider = value) ``` ## 8. Data Download You may download the data set that you have been working with int he shiny app, including the assays you added in the "Normalization" or "Batch Effect Correction" steps when you uploaded your data. A preview is provided that shows the Summarized Experiment (SE) object, including the dimensions, metadata table, assays, rownames and colNames. Click the "Download" button and you can save this SE object to your computer for additional analysis outside of the BatchQC shiny app. The object is saved as a .RDS Summarized Experiment object in the folder of your choice. You should give it a descriptive name (default is "se.RDS"). If you run code from the command line according to the above instructions, all assays will be saved to your original SE object and available for your use in other applications. You may save it as an SE object by running the following: ```{r, eval = FALSE, echo = TRUE} file_location <- "location/to/save/object" saveRDS(object = se_object, file = file_location) ``` # Conclusion Many of these analyses can be run again on the batch corrected assay to see improvements in groupings based on your biological variable rather than the batch effect. # Session info {.unnumbered} ```{r sessionInfo, echo=FALSE} sessionInfo() ```