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< h1 data-toc-skip > How to conduct principal component analysis (PCA) for AMR< / h1 >
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< small class = "dont-index" > Source: < a href = "https://github.com/msberends/AMR/blob/master/vignettes/PCA.Rmd" > < code > vignettes/PCA.Rmd< / code > < / a > < / small >
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< div class = "hidden name" > < code > PCA.Rmd< / code > < / div >
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< p > < strong > NOTE: This page will be updated soon, as the pca() function is currently being developed.< / strong > < / p >
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< a href = "#introduction" class = "anchor" > < / a > Introduction< / h1 >
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< div id = "transforming" class = "section level1" >
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< a href = "#transforming" class = "anchor" > < / a > Transforming< / h1 >
< p > For PCA, we need to transform our AMR data first. This is what the < code > example_isolates< / code > data set in this package looks like:< / p >
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< div class = "sourceCode" id = "cb1" > < pre class = "downlit sourceCode r" >
< code class = "sourceCode R" > < span class = "kw" > < a href = "https://rdrr.io/r/base/library.html" > library< / a > < / span > < span class = "op" > (< / span > < span class = "va" > < a href = "https://msberends.github.io/AMR/" > AMR< / a > < / span > < span class = "op" > )< / span >
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< span class = "kw" > < a href = "https://rdrr.io/r/base/library.html" > library< / a > < / span > < span class = "op" > (< / span > < span class = "va" > < a href = "https://dplyr.tidyverse.org" > dplyr< / a > < / span > < span class = "op" > )< / span >
< span class = "fu" > < a href = "https://tibble.tidyverse.org/reference/glimpse.html" > glimpse< / a > < / span > < span class = "op" > (< / span > < span class = "va" > example_isolates< / span > < span class = "op" > )< / span >
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< span class = "co" > # Rows: 2,000< / span >
< span class = "co" > # Columns: 49< / span >
< span class = "co" > # $ date < date> 2002-01-02, 2002-01-03, 2002-01-07, 2002-01-07, 2002…< / span >
< span class = "co" > # $ hospital_id < fct> D, D, B, B, B, B, D, D, B, B, D, D, D, D, D, B, B, B,…< / span >
< span class = "co" > # $ ward_icu < lgl> FALSE, FALSE, TRUE, TRUE, TRUE, TRUE, FALSE, FALSE, T…< / span >
< span class = "co" > # $ ward_clinical < lgl> TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, TRUE, TRUE, F…< / span >
< span class = "co" > # $ ward_outpatient < lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALS…< / span >
< span class = "co" > # $ age < dbl> 65, 65, 45, 45, 45, 45, 78, 78, 45, 79, 67, 67, 71, 7…< / span >
< span class = "co" > # $ gender < chr> "F", "F", "F", "F", "F", "F", "M", "M", "F", "F", "M"…< / span >
< span class = "co" > # $ patient_id < chr> "A77334", "A77334", "067927", "067927", "067927", "06…< / span >
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< span class = "co" > # $ mo < mo> "B_ESCHR_COLI", "B_ESCHR_COLI", "B_STPHY_EPDR", "B_STP…< / span >
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< span class = "co" > # $ PEN < rsi> R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R,…< / span >
< span class = "co" > # $ OXA < rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…< / span >
< span class = "co" > # $ FLC < rsi> NA, NA, R, R, R, R, S, S, R, S, S, S, NA, NA, NA, NA,…< / span >
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< span class = "co" > # $ AMX < rsi> NA, NA, NA, NA, NA, NA, R, R, NA, NA, NA, NA, NA, NA,…< / span >
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< span class = "co" > # $ AMC < rsi> I, I, NA, NA, NA, NA, S, S, NA, NA, S, S, I, I, R, I,…< / span >
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< span class = "co" > # $ AMP < rsi> NA, NA, NA, NA, NA, NA, R, R, NA, NA, NA, NA, NA, NA,…< / span >
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< span class = "co" > # $ TZP < rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…< / span >
< span class = "co" > # $ CZO < rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…< / span >
< span class = "co" > # $ FEP < rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…< / span >
< span class = "co" > # $ CXM < rsi> I, I, R, R, R, R, S, S, R, S, S, S, S, S, NA, S, S, R…< / span >
< span class = "co" > # $ FOX < rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…< / span >
< span class = "co" > # $ CTX < rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S,…< / span >
< span class = "co" > # $ CAZ < rsi> NA, NA, R, R, R, R, R, R, R, R, R, R, NA, NA, NA, S, …< / span >
< span class = "co" > # $ CRO < rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S,…< / span >
< span class = "co" > # $ GEN < rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…< / span >
< span class = "co" > # $ TOB < rsi> NA, NA, NA, NA, NA, NA, S, S, NA, NA, NA, NA, S, S, N…< / span >
< span class = "co" > # $ AMK < rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…< / span >
< span class = "co" > # $ KAN < rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…< / span >
< span class = "co" > # $ TMP < rsi> R, R, S, S, R, R, R, R, S, S, NA, NA, S, S, S, S, S, …< / span >
< span class = "co" > # $ SXT < rsi> R, R, S, S, NA, NA, NA, NA, S, S, NA, NA, S, S, S, S,…< / span >
< span class = "co" > # $ NIT < rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…< / span >
< span class = "co" > # $ FOS < rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…< / span >
< span class = "co" > # $ LNZ < rsi> R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R…< / span >
< span class = "co" > # $ CIP < rsi> NA, NA, NA, NA, NA, NA, NA, NA, S, S, NA, NA, NA, NA,…< / span >
< span class = "co" > # $ MFX < rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…< / span >
< span class = "co" > # $ VAN < rsi> R, R, S, S, S, S, S, S, S, S, NA, NA, R, R, R, R, R, …< / span >
< span class = "co" > # $ TEC < rsi> R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R…< / span >
< span class = "co" > # $ TCY < rsi> R, R, S, S, S, S, S, S, S, I, S, S, NA, NA, I, R, R, …< / span >
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< span class = "co" > # $ TGC < rsi> NA, NA, S, S, S, S, S, S, S, NA, S, S, NA, NA, NA, R,…< / span >
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< span class = "co" > # $ DOX < rsi> NA, NA, S, S, S, S, S, S, S, NA, S, S, NA, NA, NA, R,…< / span >
< span class = "co" > # $ ERY < rsi> R, R, R, R, R, R, S, S, R, S, S, S, R, R, R, R, R, R,…< / span >
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< span class = "co" > # $ CLI < rsi> R, R, NA, NA, NA, R, NA, NA, NA, NA, NA, NA, R, R, R,…< / span >
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< span class = "co" > # $ AZM < rsi> R, R, R, R, R, R, S, S, R, S, S, S, R, R, R, R, R, R,…< / span >
< span class = "co" > # $ IPM < rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S,…< / span >
< span class = "co" > # $ MEM < rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…< / span >
< span class = "co" > # $ MTR < rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…< / span >
< span class = "co" > # $ CHL < rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…< / span >
< span class = "co" > # $ COL < rsi> NA, NA, R, R, R, R, R, R, R, R, R, R, NA, NA, NA, R, …< / span >
< span class = "co" > # $ MUP < rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…< / span >
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< span class = "co" > # $ RIF < rsi> R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R…< / span > < / code > < / pre > < / div >
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< p > Now to transform this to a data set with only resistance percentages per taxonomic order and genus:< / p >
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< div class = "sourceCode" id = "cb2" > < pre class = "downlit sourceCode r" >
< code class = "sourceCode R" > < span class = "va" > resistance_data< / span > < span class = "op" > < -< / span > < span class = "va" > example_isolates< / span > < span class = "op" > %> %< / span >
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< span class = "fu" > < a href = "https://dplyr.tidyverse.org/reference/group_by.html" > group_by< / a > < / span > < span class = "op" > (< / span > order < span class = "op" > =< / span > < span class = "fu" > < a href = "../reference/mo_property.html" > mo_order< / a > < / span > < span class = "op" > (< / span > < span class = "va" > mo< / span > < span class = "op" > )< / span > , < span class = "co" > # group on anything, like order< / span >
genus < span class = "op" > =< / span > < span class = "fu" > < a href = "../reference/mo_property.html" > mo_genus< / a > < / span > < span class = "op" > (< / span > < span class = "va" > mo< / span > < span class = "op" > )< / span > < span class = "op" > )< / span > < span class = "op" > %> %< / span > < span class = "co" > # and genus as we do here< / span >
< span class = "fu" > < a href = "https://dplyr.tidyverse.org/reference/summarise_all.html" > summarise_if< / a > < / span > < span class = "op" > (< / span > < span class = "va" > is.rsi< / span > , < span class = "va" > resistance< / span > < span class = "op" > )< / span > < span class = "op" > %> %< / span > < span class = "co" > # then get resistance of all drugs< / span >
< span class = "fu" > < a href = "https://dplyr.tidyverse.org/reference/select.html" > select< / a > < / span > < span class = "op" > (< / span > < span class = "va" > order< / span > , < span class = "va" > genus< / span > , < span class = "va" > AMC< / span > , < span class = "va" > CXM< / span > , < span class = "va" > CTX< / span > ,
< span class = "va" > CAZ< / span > , < span class = "va" > GEN< / span > , < span class = "va" > TOB< / span > , < span class = "va" > TMP< / span > , < span class = "va" > SXT< / span > < span class = "op" > )< / span > < span class = "co" > # and select only relevant columns< / span >
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< span class = "fu" > < a href = "https://rdrr.io/r/utils/head.html" > head< / a > < / span > < span class = "op" > (< / span > < span class = "va" > resistance_data< / span > < span class = "op" > )< / span >
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< span class = "co" > # # A tibble: 6 x 10< / span >
< span class = "co" > # # Groups: order [2]< / span >
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< span class = "co" > # order genus AMC CXM CTX CAZ GEN TOB TMP SXT< / span >
< span class = "co" > # < chr> < chr> < dbl> < dbl> < dbl> < dbl> < dbl> < dbl> < dbl> < dbl> < / span >
< span class = "co" > # 1 (unknown order) (unknown genu… NA NA NA NA NA NA NA NA< / span >
< span class = "co" > # 2 Actinomycetales Corynebacteri… NA NA NA NA NA NA NA NA< / span >
< span class = "co" > # 3 Actinomycetales Cutibacterium NA NA NA NA NA NA NA NA< / span >
< span class = "co" > # 4 Actinomycetales Dermabacter NA NA NA NA NA NA NA NA< / span >
< span class = "co" > # 5 Actinomycetales Micrococcus NA NA NA NA NA NA NA NA< / span >
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< span class = "co" > # 6 Actinomycetales Rothia NA NA NA NA NA NA NA NA< / span > < / code > < / pre > < / div >
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< / div >
< div id = "perform-principal-component-analysis" class = "section level1" >
< h1 class = "hasAnchor" >
< a href = "#perform-principal-component-analysis" class = "anchor" > < / a > Perform principal component analysis< / h1 >
< p > The new < code > < a href = "../reference/pca.html" > pca()< / a > < / code > function will automatically filter on rows that contain numeric values in all selected variables, so we now only need to do:< / p >
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< div class = "sourceCode" id = "cb3" > < pre class = "downlit sourceCode r" >
< code class = "sourceCode R" > < span class = "va" > pca_result< / span > < span class = "op" > < -< / span > < span class = "fu" > < a href = "../reference/pca.html" > pca< / a > < / span > < span class = "op" > (< / span > < span class = "va" > resistance_data< / span > < span class = "op" > )< / span >
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< span class = "co" > # NOTE: Columns selected for PCA: AMC CXM CTX CAZ GEN TOB TMP SXT. Total< / span >
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< span class = "co" > # observations available: 7.< / span > < / code > < / pre > < / div >
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< p > The result can be reviewed with the good old < code > < a href = "https://rdrr.io/r/base/summary.html" > summary()< / a > < / code > function:< / p >
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< div class = "sourceCode" id = "cb4" > < pre class = "downlit sourceCode r" >
< code class = "sourceCode R" > < span class = "fu" > < a href = "https://rdrr.io/r/base/summary.html" > summary< / a > < / span > < span class = "op" > (< / span > < span class = "va" > pca_result< / span > < span class = "op" > )< / span >
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< span class = "co" > # Importance of components:< / span >
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< span class = "co" > # PC1 PC2 PC3 PC4 PC5 PC6 PC7< / span >
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< span class = "co" > # Standard deviation 2.154 1.6807 0.61365 0.33902 0.20757 0.03136 1.733e-16< / span >
< span class = "co" > # Proportion of Variance 0.580 0.3531 0.04707 0.01437 0.00539 0.00012 0.000e+00< / span >
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< span class = "co" > # Cumulative Proportion 0.580 0.9331 0.98012 0.99449 0.99988 1.00000 1.000e+00< / span > < / code > < / pre > < / div >
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< p > Good news. The first two components explain a total of 93.3% of the variance (see the PC1 and PC2 values of the < em > Proportion of Variance< / em > . We can create a so-called biplot with the base R < code > < a href = "https://rdrr.io/r/stats/biplot.html" > biplot()< / a > < / code > function, to see which antimicrobial resistance per drug explain the difference per microorganism.< / p >
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< / div >
< div id = "plotting-the-results" class = "section level1" >
< h1 class = "hasAnchor" >
< a href = "#plotting-the-results" class = "anchor" > < / a > Plotting the results< / h1 >
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< div class = "sourceCode" id = "cb5" > < pre class = "downlit sourceCode r" >
< code class = "sourceCode R" > < span class = "fu" > < a href = "https://rdrr.io/r/stats/biplot.html" > biplot< / a > < / span > < span class = "op" > (< / span > < span class = "va" > pca_result< / span > < span class = "op" > )< / span > < / code > < / pre > < / div >
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< p > < img src = "PCA_files/figure-html/unnamed-chunk-5-1.png" width = "750" > < / p >
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< p > But we can’ < code > < a href = "../reference/ggplot_pca.html" > ggplot_pca()< / a > < / code > function, that automatically adds the right labels and even groups:< / p >
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< div class = "sourceCode" id = "cb6" > < pre class = "downlit sourceCode r" >
< code class = "sourceCode R" > < span class = "fu" > < a href = "../reference/ggplot_pca.html" > ggplot_pca< / a > < / span > < span class = "op" > (< / span > < span class = "va" > pca_result< / span > < span class = "op" > )< / span > < / code > < / pre > < / div >
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< p > < img src = "PCA_files/figure-html/unnamed-chunk-6-1.png" width = "750" > < / p >
< p > You can also print an ellipse per group, and edit the appearance:< / p >
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< div class = "sourceCode" id = "cb7" > < pre class = "downlit sourceCode r" >
< code class = "sourceCode R" > < span class = "fu" > < a href = "../reference/ggplot_pca.html" > ggplot_pca< / a > < / span > < span class = "op" > (< / span > < span class = "va" > pca_result< / span > , ellipse < span class = "op" > =< / span > < span class = "cn" > TRUE< / span > < span class = "op" > )< / span > < span class = "op" > +< / span >
< span class = "fu" > ggplot2< / span > < span class = "fu" > ::< / span > < span class = "fu" > < a href = "https://ggplot2.tidyverse.org/reference/labs.html" > labs< / a > < / span > < span class = "op" > (< / span > title < span class = "op" > =< / span > < span class = "st" > "An AMR/PCA biplot!"< / span > < span class = "op" > )< / span > < / code > < / pre > < / div >
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< p > < img src = "PCA_files/figure-html/unnamed-chunk-7-1.png" width = "750" > < / p >
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< p > Developed by < a href = "https://www.rug.nl/staff/m.s.berends/" > Matthijs S. Berends< / a > , < a href = "https://www.rug.nl/staff/c.f.luz/" > Christian F. Luz< / a > , < a href = "https://www.rug.nl/staff/a.w.friedrich/" > Alexander W. Friedrich< / a > , < a href = "https://www.rug.nl/staff/b.sinha/" > Bhanu N. M. Sinha< / a > , < a href = "https://www.rug.nl/staff/c.j.albers/" > Casper J. Albers< / a > , < a href = "https://www.rug.nl/staff/c.glasner/" > Corinna Glasner< / a > .< / p >
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< p > Site built with < a href = "https://pkgdown.r-lib.org/" > pkgdown< / a > 1.6.1.< / p >
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