5 Longitudinal Data Analysis
Another feature of metavizr is to visualize data using a line plot. We detail the steps to perform this analysis and create a line plot using Metaviz such as those used in Paulson et al. when analyzing this time series data with a smoothing-spline [2] and [3].
First, import the etec16s dataset, select sample data from the first 9 days, and choose the feature annotations of interest.
library(etec16s)
data(etec16s)
etec16s <- etec16s[,-which(pData(etec16s)$Day>9)]Next, use metagenomeSeq to fit a smoothing-spline to the time series data.
featureData(etec16s)$Kingdom <- "Bacteria"
feature_order <- c("Kingdom", "Phylum", "Class", "Order", "Family", "Genus", "Species", "OTU.ID")
timeSeriesFits <- fitMultipleTimeSeries(obj=etec16s,
formula = abundance~id + time*class + AntiGiven,
class="AnyDayDiarrhea",
id="SubjectID",
time="Day",
lvl="Family",
featureOrder = feature_order,
C=0.3,
B=1,
seed=1234)## Loading required namespace: gss## Warning in mspreg1(s, r, id.basis, y, wt, method, alpha, varht, random, :
## gss warning in ssanova: iteration for model selection fails to convergeFor plotting the data using Metaviz, we set the fit values as y-coordinates and timepoints as x-coordinates. We need to call ts2MRexperiment with arguments for the sample and feature data, in this case timepoints and annotations, respectively.
feature_order2 <- c("Kingdom", "Phylum", "Class", "Order", "Family")
splinesMRexp <- ts2MRexperiment(timeSeriesFits, featureData = featureData(aggregateByTaxonomy(etec16s, lvl="Family", featureOrder = feature_order2)), sampleNames = timeSeriesFits[[2]]$fit$timePoints)Finally, we add the MRexperiment as a measurement using the add_measurements method and then visualize those measurements as a LinePlot.
splines_to_plot <- sapply(1:nrow(MRcounts(splinesMRexp)), function(i) {max(abs(MRcounts(splinesMRexp[i,]))) >= 2})
splines_to_plot_indices <- which(splines_to_plot == TRUE)
ic_plot <- app$plot(splinesMRexp[splines_to_plot_indices,], datasource_name = "etec16_base21", control=metavizControl(norm = FALSE, aggregateAtDepth = 4), feature_order = feature_order2)## MRExperiment Object validated... PASS## creating hierarchy_tree## creating node_ids_table## creating nodes_table## creating leaf_of_table## creating leaf_sample_count_tablesplineObj <- app$data_mgr$add_measurements(splinesMRexp[splines_to_plot_indices,], datasource_name = "splines2", control = metavizControl(norm=FALSE, aggregateAtDepth = 4), feature_order = feature_order2)## MRExperiment Object validated... PASS## creating hierarchy_tree## creating node_ids_table## creating nodes_table## creating leaf_of_table## creating leaf_sample_count_tablesplineMeasurements <- splineObj$get_measurements()
splineChart <- app$chart_mgr$visualize("LinePlot", splineMeasurements)We can update the colors and settings on the spline chart. For example, lets limit the y axis to be between -10 and 10. To do so we use the set_chart_settings method. We can list existing settings for a chart using the list_chart_settings function.
## type js_class
## 1 HeatmapPlot epiviz.plugins.charts.HeatmapPlot
## 2 LinePlot epiviz.plugins.charts.LinePlot
## 3 StackedLinePlot epiviz.plugins.charts.StackedLinePlot
## 4 epiviz.ui.charts.tree.Icicle epiviz.ui.charts.tree.Icicle
## num_settings
## 1 17
## 2 16
## 3 14
## 4 5
## settings
## 1 title,marginTop,marginBottom,marginLeft,marginRight,measurementGroupsAggregator,...
## 2 title,marginTop,marginBottom,marginLeft,marginRight,measurementGroupsAggregator,...
## 3 title,marginTop,marginBottom,marginLeft,marginRight,measurementGroupsAggregator,...
## 4 title,marginTop,marginBottom,marginLeft,marginRight
## num_colors
## 1 8
## 2 20
## 3 20
## 4 10## id label
## 1 title Title
## 2 marginTop Top margin
## 3 marginBottom Bottom margin
## 4 marginLeft Left margin
## 5 marginRight Right margin
## 6 measurementGroupsAggregator Aggregator for measurement groups
## 7 colLabel Columns labels
## 8 rowLabel Row labels
## 9 showPoints Show points
## 10 showLines Show lines
## 11 showErrorBars Show error bars
## 12 pointRadius Point radius
## 13 lineThickness Line thickness
## 14 yMin Min Y
## 15 yMax Max Y
## 16 interpolation Interpolation
## default_value
## 1
## 2 30
## 3 50
## 4 30
## 5 15
## 6 mean-stdev
## 7 colLabel
## 8 name
## 9 FALSE
## 10 TRUE
## 11 TRUE
## 12 4
## 13 3
## 14 default
## 15 default
## 16 basis
## possible_values
## 1
## 2
## 3
## 4
## 5
## 6 mean-stdev,quartiles,count,min,max,sum
## 7
## 8
## 9
## 10
## 11
## 12
## 13
## 14
## 15
## 16 linear,step-before,step-after,basis,basis-open,basis-closed,bundle,cardinal,cardinal-open,monotone
## type
## 1 string
## 2 number
## 3 number
## 4 number
## 5 number
## 6 categorical
## 7 measurementsMetadata
## 8 measurementsAnnotation
## 9 boolean
## 10 boolean
## 11 boolean
## 12 number
## 13 number
## 14 number
## 15 number
## 16 categorical
5.0.1 Close Metavizr and end session
To close the connection with metaviz and the R session, we use the stop_app function.
app$stop_app()5.0.2 SessionInfo
sessionInfo()## R version 3.4.1 (2017-06-30)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 16.04.2 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.5-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.5-bioc/R/lib/libRlapack.so
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=C
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] parallel stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] etec16s_1.4.0 msd16s_0.110.0 metavizr_1.0.2
## [4] digest_0.6.12 data.table_1.10.4 metagenomeSeq_1.18.0
## [7] RColorBrewer_1.1-2 glmnet_2.0-10 foreach_1.4.3
## [10] Matrix_1.2-10 limma_3.32.3 Biobase_2.36.2
## [13] BiocGenerics_0.22.0 BiocStyle_2.4.0
##
## loaded via a namespace (and not attached):
## [1] nlme_3.1-131 ProtGenerics_1.8.0
## [3] bitops_1.0-6 matrixStats_0.52.2
## [5] phyloseq_1.20.0 bit64_0.9-7
## [7] httr_1.2.1 rprojroot_1.2
## [9] GenomeInfoDb_1.12.2 tools_3.4.1
## [11] backports_1.1.0 epivizr_2.6.0
## [13] vegan_2.4-3 R6_2.2.2
## [15] KernSmooth_2.23-15 mgcv_1.8-17
## [17] colorspace_1.3-2 DBI_0.7
## [19] lazyeval_0.2.0 permute_0.9-4
## [21] ade4_1.7-6 bit_1.1-12
## [23] curl_2.7 compiler_3.4.1
## [25] git2r_0.18.0 graph_1.54.0
## [27] DelayedArray_0.2.7 rtracklayer_1.36.4
## [29] scales_0.4.1 caTools_1.17.1
## [31] RBGL_1.52.0 stringr_1.2.0
## [33] Rsamtools_1.28.0 epivizrStandalone_1.4.0
## [35] rmarkdown_1.6 XVector_0.16.0
## [37] pkgconfig_2.0.1 epivizrServer_1.4.0
## [39] htmltools_0.3.6 ensembldb_2.0.3
## [41] rlang_0.1.1 RSQLite_2.0
## [43] BiocInstaller_1.26.0 shiny_1.0.3
## [45] jsonlite_1.5 BiocParallel_1.10.1
## [47] gtools_3.5.0 RCurl_1.95-4.8
## [49] magrittr_1.5 GenomeInfoDbData_0.99.0
## [51] biomformat_1.4.0 munsell_0.4.3
## [53] Rcpp_0.12.12 S4Vectors_0.14.3
## [55] ape_4.1 stringi_1.1.5
## [57] yaml_2.1.14 MASS_7.3-47
## [59] SummarizedExperiment_1.6.3 zlibbioc_1.22.0
## [61] rhdf5_2.20.0 gplots_3.0.1
## [63] plyr_1.8.4 AnnotationHub_2.8.2
## [65] grid_3.4.1 blob_1.1.0
## [67] gdata_2.18.0 lattice_0.20-35
## [69] splines_3.4.1 Biostrings_2.44.1
## [71] multtest_2.32.0 GenomicFeatures_1.28.4
## [73] knitr_1.16 igraph_1.1.1
## [75] GenomicRanges_1.28.4 rjson_0.2.15
## [77] reshape2_1.4.2 codetools_0.2-15
## [79] biomaRt_2.32.1 stats4_3.4.1
## [81] XML_3.98-1.9 evaluate_0.10.1
## [83] httpuv_1.3.5 gtable_0.2.0
## [85] ggplot2_2.2.1 mime_0.5
## [87] xtable_1.8-2 AnnotationFilter_1.0.0
## [89] survival_2.41-3 tibble_1.3.3
## [91] OrganismDbi_1.18.0 iterators_1.0.8
## [93] epivizrData_1.4.0 GenomicAlignments_1.12.1
## [95] AnnotationDbi_1.38.1 memoise_1.1.0
## [97] IRanges_2.10.2 cluster_2.0.6
## [99] interactiveDisplayBase_1.14.0 gss_2.1-7References:
[1] Pop, M., Walker, A.W., Paulson, J., Lindsay, B., Antonio, M., Hossain, M.A., Oundo, J., Tamboura, B., Mai, V., Astrovskaya, I. and Bravo, H.C., 2014. Diarrhea in young children from low-income countries leads to large-scale alterations in intestinal microbiota composition. Genome biology, 15(6), p.1.
[2] Pop, M., Paulson, J.N., Chakraborty, S., Astrovskaya, I., Lindsay, B.R., Li, S., Bravo, H.C., Harro, C., Parkhill, J., Walker, A.W. and Walker, R.I., 2016. Individual-specific changes in the human gut microbiota after challenge with enterotoxigenic Escherichia coli and subsequent ciprofloxacin treatment. BMC genomics, 17(1), p.1.
[3] Paulson J.N., Talukder H., and Bravo H.C, Longitudinal differential abundance analysis of microbial marker-gene surveys using smoothing splines. In Submission.