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Developed by Austin G. McCoy, Zachary Noel, Adam H. Sparks, Martin Chilvers, Michigan Soybean Promotion Committee, Project GREEEN, North Central Soybean Research Program, GRDC Project DAQ00186.

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CONTRIBUTING

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Developed by Austin G. McCoy, Zachary Noel, Adam H. Sparks, Martin Chilvers, Michigan Soybean Promotion Committee, Project GREEEN, North Central Soybean Research Program, GRDC Project DAQ00186.

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Developed by Austin G. McCoy, Zachary Noel, Adam H. Sparks, Martin Chilvers, Michigan Soybean Promotion Committee, Project GREEEN, North Central Soybean Research Program, GRDC Project DAQ00186.

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Developed by Austin G. McCoy, Zachary Noel, Adam H. Sparks, Martin Chilvers, Michigan Soybean Promotion Committee, Project GREEEN, North Central Soybean Research Program, GRDC Project DAQ00186.

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+ + + + + + + + diff --git a/articles/betadiversity.html b/articles/betadiversity.html new file mode 100644 index 00000000..e2ac7e8e --- /dev/null +++ b/articles/betadiversity.html @@ -0,0 +1,1573 @@ + + + + + + + +Beta-diversity Analysis with hagis • hagis + + + + + + + + + + + + +
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+ + + + +
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+

Beta-diversity Analysis with hagis

+

Austin +McCoy

+ +

2024-03-11

+ + Source: vignettes/betadiversity.Rmd + + +
+ + + +
+

Load Packages +

+

These packages are necessary for this analysis to be conducted.

+ +

Import the data to be used in analysis. These example data are +included in {hagis} and so when loading {hagis} they become available in +your R session.

+
+head(P_sojae_survey) # survey sample data
+
##    Isolate         Line         Rps Total HR (1) Lesion (2)
+##      <int>       <char>      <char> <int>  <int>      <int>
+## 1:       1     Williams susceptible    10      0          0
+## 2:       1       Harlon      Rps 1a    10      4          0
+## 3:       1 Harosoy 13xx      Rps 1b     8      0          0
+## 4:       1     L75-3735      Rps 1c    10     10          0
+## 5:       1    PI 103091      Rps 1d     9      2          0
+## 6:       1  Williams 82      Rps 1k    10      0          0
+##    Lesion to cotyledon (3) Dead (4) total.susc total.resis perc.susc perc.resis
+##                      <int>    <int>      <int>       <int>     <int>      <int>
+## 1:                       0       10         10           0       100          0
+## 2:                       0        6          6           4        60         40
+## 3:                       0        8          8           0       100          0
+## 4:                       0        0          0          10         0        100
+## 5:                       1        6          7           2        78         22
+## 6:                       0       10         10           0       100          0
+
+head(sample_meta) # metatada about the sample collection locations
+
##   Sample   Locale
+## 1      1 Michigan
+## 2     10 Michigan
+## 3     11 Michigan
+## 4     12 Michigan
+## 5     13 Michigan
+## 6     14 Michigan
+

This removes the “MPS17_” from the isolates, so that they will be +read as numeric instead of character. The next step removes the “Rps” +from the gene names, so that they will be read as numeric instead of +character.

+
+P_sojae_survey$Isolate <-
+  gsub(pattern = "MPS17_",
+       replacement = "",
+       x = P_sojae_survey$Isolate)
+P_sojae_survey$Rps <-
+  gsub(pattern = "Rps ",
+       replacement = "",
+       x = P_sojae_survey$Rps)
+

Set up the {hagis} arguments for analysis. Please see +vignette("hagis") for more details on how to specify +arguments for the functions in this package.

+
+hagis_args <- list(
+  x = P_sojae_survey,
+  cutoff = 60,
+  control = "susceptible",
+  sample = "Isolate",
+  gene = "Rps",
+  perc_susc = "perc.susc"
+)
+
+
+

Convert the Dataset to a Binary Data Matrix +

+

Using create_binary_matrix() transforms the data so that +it is in the correct format (binary data matrix) for PCOA analysis.

+
+P_sojae_survey.matrix <- do.call(create_binary_matrix, hagis_args)
+
+P_sojae_survey.matrix
+
##    1a 1b 1c 1d 1k 2 3a 3b 3c 4 5 6 7
+## 1   1  1  0  1  1 1  1  1  0 0 1 1 1
+## 10  1  0  1  0  0 0  0  1  0 0 1 0 1
+## 11  1  0  1  0  0 0  0  1  0 0 1 1 1
+## 12  1  1  1  1  1 1  0  0  0 0 0 1 1
+## 13  1  1  1  1  1 0  0  1  0 0 0 0 1
+## 14  1  1  1  0  1 0  0  1  0 0 1 1 1
+## 15  1  1  1  1  1 1  0  1  0 1 1 1 1
+## 16  1  1  1  0  1 0  0  1  0 0 1 0 1
+## 17  1  1  1  1  1 1  0  1  0 1 1 0 1
+## 18  1  1  1  1  1 0  1  1  0 0 1 1 1
+## 19  1  1  1  1  1 1  1  1  1 0 0 1 1
+## 2   1  1  1  0  1 1  0  1  1 1 0 1 1
+## 20  1  1  1  1  1 1  1  1  1 0 1 0 1
+## 21  1  1  1  1  1 1  1  1  1 1 1 1 1
+## 3   1  1  1  1  1 1  0  1  0 1 0 1 1
+## 4   1  0  1  1  1 1  0  1  0 0 1 0 1
+## 5   1  0  1  1  1 1  0  1  0 0 0 1 1
+## 6   1  0  1  1  1 1  0  1  0 0 1 0 1
+## 7   1  1  1  1  1 1  0  1  0 0 0 0 1
+## 8   1  1  1  1  1 1  0  1  0 0 0 0 1
+## 9   1  0  1  1  0 0  0  1  0 0 1 0 1
+

The P_sojae_survey.matrix object contains the RPS genes +numbered as rows and the columns are numbered isolates. A “1” indicates +the isolate caused disease on the RPS gene, and a “0” means it did +not.

+
+
+

Perform Principle Coordinates Analysis (PCOA) +

+

The following code will transpose the +P_sojae_survey.matrix and then calculate the Jaccard +distances for each isolate. Jaccard distance calculations need to be +used as pathotype data is presence/absence for virulence. Lastly, PCOA +is performed to identify the variance explained by each principal +coordinate. This will be used later to visualize and identify distance +pathotype groupings by geographic location. In this example, Jaccard +distances are used as this is presence/absence data.

+
+P_sojae_survey.matrix.jaccard <-
+  vegdist(P_sojae_survey.matrix, "jaccard", na.rm = TRUE)
+

After performing the principal coordinates analysis, we see that the +scree plot says that about 70% of the variation in Jaccard distances are +explained within the first two dimensions (i.e., axes). This is +good. Usually a good rule of thumb is that if the second dimension is +roughly half variation explained in the first dimension you don’t need +to look further at the third or n+1 dimensions.

+
+princoor.pathotype <- pcoa(P_sojae_survey.matrix.jaccard) 
+
+barplot(princoor.pathotype$values$Relative_eig[1:10])
+

+

Now we can calculate the percentage of variation that each principal +coordinate accounts for. Another way to calculate the percent variation +is to look at the Relative_eig column.

+
+# Dimension (i.e., Axis 1 (PCOA1))
+Axis1.percent <-
+  princoor.pathotype$values$Relative_eig[[1]] * 100
+
+# Dimension (i.e., Axis 2 (PCOA2))
+Axis2.percent <-
+  princoor.pathotype$values$Relative_eig[[2]] * 100
+
+Axis1.percent
+
## [1] 50.39109
+
+Axis2.percent
+
## [1] 21.79118
+

Now we can make a data frame with the two principal coordinates that +account for the most variation in the data. We will then add metadata to +the data frame pca.data.

+
+princoor.pathotype.data <-
+  data.frame(
+    Sample = as.integer(rownames(princoor.pathotype$vectors)),
+    X = princoor.pathotype$vectors[, 1],
+    Y = princoor.pathotype$vectors[, 2]
+  )
+
+
+

Add Metadata to your PCOA Data +

+

You will need to add information on the sample collection location or +other data to help identify different pathotype groupings based on +geographic location or other factors. We will use +left_join() from {dplyr} to combine the +princoor.pathotype.data with the metadata that we’ve +already loaded, sample_meta that contains a geographic +location (state name).

+
+princoor.pathotype.data <-
+  left_join(princoor.pathotype.data, sample_meta, by = "Sample")
+
+princoor.pathotype.data
+
##    Sample            X           Y   Locale
+## 1       1  0.127655783  0.08704548 Michigan
+## 2      10 -0.441075005  0.01573886 Michigan
+## 3      11 -0.336826428  0.21804192 Michigan
+## 4      12  0.217071376 -0.03795693 Michigan
+## 5      13 -0.003862869 -0.14942952 Michigan
+## 6      14 -0.108034781  0.18124849 Michigan
+## 7      15  0.082862290  0.06501932 Michigan
+## 8      16 -0.182559838  0.01610323 Michigan
+## 9      17  0.043684771 -0.07086878 Michigan
+## 10     18 -0.007048533  0.11629976 Michigan
+## 11     19  0.224528803  0.06847162 Michigan
+## 12      2  0.190565779  0.16984304 Michigan
+## 13     20  0.081014587 -0.01986841 Michigan
+## 14     21  0.156490576  0.15103874 Michigan
+## 15      3  0.187665103  0.01477482 Michigan
+## 16      4 -0.088772478 -0.14970662 Michigan
+## 17      5  0.069295886 -0.06198926 Michigan
+## 18      6 -0.088772478 -0.14970662 Michigan
+## 19      7  0.102108599 -0.17330058 Michigan
+## 20      8  0.102108599 -0.17330058 Michigan
+## 21      9 -0.328099742 -0.11749798 Michigan
+

Now we will plot the PCA data using {ggplot2} and color the points +based on location, Locale, and identify the 95% confidence +interval of those groups using the stat_ellipse() +function.

+
+ggplot(data = princoor.pathotype.data, aes(x = X, y = Y)) +
+  geom_point(aes(colour = Locale)) +
+  xlab(paste("PCOA1 - ", round(Axis1.percent, 2), "%", sep = "")) +
+  ylab(paste("PCOA2 - ", round(Axis2.percent, 2), "%", sep = "")) +
+  theme_bw() +
+  theme(
+    axis.title.x = element_text(face = "bold", size = 15),
+    axis.title.y = element_text(face = "bold", size = 15),
+    axis.text = element_text(face = "bold", size = 10),
+    legend.title = element_text(face = "bold", size = 10),
+    legend.text = element_text(face = "bold", size = 10),
+    legend.key.size = unit(1, 'lines')
+  ) +
+  stat_ellipse(data = princoor.pathotype.data, aes(x = X, y = Y),
+               level = 0.95) +
+  ggtitle("Pathotype Jaccard Distances PCOA")
+

+
+
+

Statistical Tests for Beta Diversity +

+

When using two or more pathotype datasets for comparisons, you can +use beta-diversity tests to identify if there are significant +differences between their sampled pathotype compositions. These code are +presented as an example for further downstream analysis that can be used +when comparing two or more populations’ pathotype composition.

+

In these examples we will artificially split the dataset into two, so +that these analyses can be shown. When performing your own analyses you +will likely have two geographic locations to compare already. Make sure +you can differentiate these populations with the metadata file used +previously (i.e., column in the dataset that specifies where +the isolate came from; USA, Brazil, China, Australia, etc.).

+
+
+

Permutation Based Analysis of Variance (PERMANOVA) and +Beta-dispersion Analyses +

+
+

Beta-Dispersion +

+

Beta-dispersion tests if the dispersion, variance, of two or more +groups are significantly different or not. First, an item named “groups” +must be made that contains lists of the location and number of isolates +from that location used in the analysis. We will then check to make sure +the lists in “groups” adds up to the number of isolated used in +analysis.

+

First, make a list of the locations for each pathotype. Note that +when you are using two or more locations you will need to make a list +for each location with a length of isolates used.

+
+groups <- factor(c(rep("Michigan_1", 11), rep("Michigan_2", 10)))
+
+# this number shows how many isolates are in all "groups" lists combined
+length(groups)
+
## [1] 21
+
+# this shows the number of isolates within your data set, these numbers should
+# match for downstream analyses to work!! 
+length(unique(P_sojae_survey$Isolate))
+
## [1] 21
+

Next, beta-dispersion will be calculated using the Jaccard distance +matrix made previously. An ANOVA is then performed to identify +significance within the dataset. Post-hoc tests can be used to identify +significant interactions between specific locations within the dataset. +This can then be plotted to visualize how the beta-dispersion is +different between groups.

+
+ # calculates the beta-dispersion for each group, when comparing 2 or more
+pathotype.disp <-
+  betadisper(P_sojae_survey.matrix.jaccard, groups)
+
+# tests if centroid distances are significantly different from each other
+pathotype.disp.anova <- anova(pathotype.disp) 
+pathotype.disp.anova
+
## Analysis of Variance Table
+## 
+## Response: Distances
+##           Df   Sum Sq   Mean Sq F value Pr(>F)
+## Groups     1 0.008375 0.0083752  0.9672 0.3377
+## Residuals 19 0.164523 0.0086591
+
+# test significance between each group
+pathotype.disp.TukeyHSD <- TukeyHSD(pathotype.disp)
+pathotype.disp.TukeyHSD
+
##   Tukey multiple comparisons of means
+##     95% family-wise confidence level
+## 
+## Fit: aov(formula = distances ~ group, data = df)
+## 
+## $group
+##                              diff        lwr        upr     p adj
+## Michigan_2-Michigan_1 -0.03998626 -0.1250853 0.04511275 0.3377355
+
+# plot showing the dispersion for each group
+plot(pathotype.disp, hull = FALSE, ellipse = TRUE)
+

+

The ANOVA identified no significant differences between groups +dispersion (p = 0.3377355). This means that the groups dispersion, or +variance, is not significantly different from each other and the groups +dispersion is likely homogeneous between groups. At this point, a Tukey +HSD test is not warranted, but we use it as an example here. Again, a +p-value of 0.3377355 is reported from the Tukey HSD tests and we reject +the hypothesis that these groups may have different dispersion. We can +plot the dispersion for each group using the plot() +function. As expected, since we have identified no significant +differences, the two groups dispersion overlap a great deal and are not +distinct from each other. Again this shows that pathotype dispersion +between the groups is homogeneous and not different in this +instance.

+

If we were working with a data set that had groups with significantly +different dispersion we would expect to see a significant ANOVA p-value +(p < 0.05) as well as significance when using the Tukey HSD test. +Lastly, the plotted dispersion will form distinct, separate, groups +which can be observed.

+

Differences in beta-dispersion may indicate separate pathotype groups +which should be further investigated with Permutation Based Analysis of +Variance (PERMANOVA) and Analysis of Similarity (ANOSIM) analysis. +Groups which have similar dispersion may still be significantly +different in regards to their centroids, which will be tested using a +PERMANOVA.

+
+
+

Permutation Based Analysis of Variance (PERMANOVA) +

+

PERMANOVA tests if the centroids, similar to means, of each group are +significantly different from each other. Likewise, an \(R^2\) statistic is calculated, showing the +percentage of the variance explained by the groups.

+
+pathotype.adonis <- adonis(P_sojae_survey.matrix.jaccard ~ groups)
+
## 'adonis' will be deprecated: use 'adonis2' instead
+
+pathotype.adonis
+
## $aov.tab
+## Permutation: free
+## Number of permutations: 999
+## 
+## Terms added sequentially (first to last)
+## 
+##           Df SumsOfSqs  MeanSqs F.Model      R2 Pr(>F)
+## groups     1   0.11358 0.113583  1.6229 0.07869  0.177
+## Residuals 19   1.32976 0.069988         0.92131       
+## Total     20   1.44335                  1.00000       
+## 
+## $call
+## adonis(formula = P_sojae_survey.matrix.jaccard ~ groups)
+## 
+## $coefficients
+## NULL
+## 
+## $coef.sites
+##                     [,1]        [,2]        [,3]       [,4]       [,5]
+## (Intercept)  0.356206294  0.47247525  0.45504258 0.36111093 0.33087421
+## groups1     -0.007721445 -0.02921472 -0.04813257 0.01795243 0.01040219
+##                    [,6]       [,7]        [,8]       [,9]       [,10]
+## (Intercept)  0.33955905 0.27518277  0.35412523 0.28774687  0.31463605
+## groups1     -0.05774087 0.01311983 -0.04013468 0.03179116 -0.03474624
+##                  [,11]      [,12]      [,13]      [,14]      [,15]      [,16]
+## (Intercept) 0.33437434 0.38567493 0.32215777 0.32867133 0.30119623 0.29928127
+## groups1     0.02883821 0.03019708 0.03363795 0.02097902 0.03420322 0.05551671
+##                  [,17]      [,18]      [,19]      [,20]       [,21]
+## (Intercept) 0.32090891 0.29928127 0.28518807 0.28518807 0.412384837
+## groups1     0.05108374 0.05551671 0.05197906 0.05197906 0.008538684
+## 
+## $f.perms
+##                 [,1]
+##    [1,]  0.259630276
+##    [2,]  1.971327269
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+##  [897,]  0.549331235
+##  [898,]  1.057101230
+##  [899,]  0.447509026
+##  [900,]  1.997336275
+##  [901,]  0.678843712
+##  [902,]  0.128124162
+##  [903,]  1.930135539
+##  [904,]  4.373590559
+##  [905,]  0.252363763
+##  [906,]  0.988587260
+##  [907,]  0.812358930
+##  [908,]  0.149262097
+##  [909,]  1.836538946
+##  [910,]  1.248097201
+##  [911,]  1.217955699
+##  [912,]  1.259594959
+##  [913,]  0.471260332
+##  [914,]  0.183585155
+##  [915,]  1.708875720
+##  [916,]  1.524585153
+##  [917,]  0.523563914
+##  [918,]  1.647340389
+##  [919,]  0.885144858
+##  [920,]  0.936704185
+##  [921,]  0.461601611
+##  [922,]  3.122150848
+##  [923,]  0.771011354
+##  [924,]  1.040630185
+##  [925,]  2.180396188
+##  [926,]  0.289122442
+##  [927,]  0.530113769
+##  [928,]  1.284219879
+##  [929,]  1.475247886
+##  [930,]  2.059350989
+##  [931,]  3.944114366
+##  [932,]  0.177892097
+##  [933,]  0.818868453
+##  [934,]  0.126878659
+##  [935,]  1.352691191
+##  [936,]  1.519007959
+##  [937,]  0.803219407
+##  [938,]  1.964364833
+##  [939,]  0.631026358
+##  [940,]  0.221735075
+##  [941,]  0.780605516
+##  [942,]  0.359379573
+##  [943,]  0.315643186
+##  [944,]  1.657965309
+##  [945,]  0.328268977
+##  [946,]  1.571044939
+##  [947,]  1.951633929
+##  [948,]  0.853564897
+##  [949,]  0.790650301
+##  [950,]  0.636738826
+##  [951,]  3.222250823
+##  [952,]  0.795266382
+##  [953,]  1.853556792
+##  [954,]  0.320850581
+##  [955,]  1.284442447
+##  [956,]  0.598295295
+##  [957,]  0.148109159
+##  [958,]  3.149576113
+##  [959,]  0.770231709
+##  [960,]  1.072591879
+##  [961,]  2.827279973
+##  [962,]  0.947261502
+##  [963,]  0.625533610
+##  [964,]  1.121267101
+##  [965,]  0.579460363
+##  [966,]  0.795941386
+##  [967,]  0.840858939
+##  [968,]  0.360025256
+##  [969,]  0.207159689
+##  [970,]  1.472652183
+##  [971,]  0.880867891
+##  [972,]  0.940357655
+##  [973,]  1.586306948
+##  [974,]  0.685237838
+##  [975,]  0.881547216
+##  [976,]  1.036237682
+##  [977,]  0.982812920
+##  [978,]  1.012641650
+##  [979,]  0.643581627
+##  [980,]  0.095899717
+##  [981,]  1.404277076
+##  [982,]  2.133221545
+##  [983,]  0.402866369
+##  [984,] -0.108778496
+##  [985,]  0.437212792
+##  [986,]  1.900921256
+##  [987,]  0.610377361
+##  [988,]  2.370415842
+##  [989,]  0.377966606
+##  [990,]  0.425350597
+##  [991,]  1.596733384
+##  [992,]  0.326378573
+##  [993,]  1.400302734
+##  [994,]  0.606283285
+##  [995,] -0.020363051
+##  [996,]  0.812292022
+##  [997,]  0.242607357
+##  [998,]  1.141070230
+##  [999,]  1.519128377
+## 
+## $model.matrix
+##    (Intercept) groups1
+## 1            1       1
+## 2            1       1
+## 3            1       1
+## 4            1       1
+## 5            1       1
+## 6            1       1
+## 7            1       1
+## 8            1       1
+## 9            1       1
+## 10           1       1
+## 11           1       1
+## 12           1      -1
+## 13           1      -1
+## 14           1      -1
+## 15           1      -1
+## 16           1      -1
+## 17           1      -1
+## 18           1      -1
+## 19           1      -1
+## 20           1      -1
+## 21           1      -1
+## 
+## $terms
+## P_sojae_survey.matrix.jaccard ~ groups
+## attr(,"variables")
+## list(P_sojae_survey.matrix.jaccard, groups)
+## attr(,"factors")
+##                               groups
+## P_sojae_survey.matrix.jaccard      0
+## groups                             1
+## attr(,"term.labels")
+## [1] "groups"
+## attr(,"order")
+## [1] 1
+## attr(,"intercept")
+## [1] 1
+## attr(,"response")
+## [1] 1
+## attr(,".Environment")
+## <environment: R_GlobalEnv>
+## 
+## attr(,"class")
+## [1] "adonis"
+

The PERMANOVA identified no significant differences between the +groups centroids, or means (p = 0.177). In addition to +identifying significance between group centroids, the PERMANOVA also +calculates how much of the variance can be explained by the specified +groups (see the \(R^2\) column in the +PERMANOVA output). In this case, the \(R^2\) is 0.177, so 17.7% of the variance is +explained by the groups used in analysis. Based on the PERMANOVA results +we can conclude that these two groups are not different from each other +and likely have similar pathotypes to each other.

+
+
+

Analysis of Similarity (ANOSIM) +

+

ANOSIM statistic (R) ranges from between -1 and 1. Positive numbers +suggest that there is more similarity within groups than there is +between groups. Values close to zero indicate no difference between +groups (i.e., similarities are the same between groups).

+
+pathotype.anosim <- anosim(P_sojae_survey.matrix.jaccard, groups)
+
+pathotype.anosim
+
## 
+## Call:
+## anosim(x = P_sojae_survey.matrix.jaccard, grouping = groups) 
+## Dissimilarity: jaccard 
+## 
+## ANOSIM statistic R: 0.06882 
+##       Significance: 0.152 
+## 
+## Permutation: free
+## Number of permutations: 999
+

ANOSIM statistic (R) was 0.0688182, so there are more similarities +between groups than there are within groups. This is evidence that the +groups are not different from one another. Likewise the significance is +>0.05 so there is no significant difference between groups’ +similarities.

+
+
+
+ + + +
+ + + +
+

+

Developed by Austin G. McCoy, Zachary Noel, Adam H. Sparks, Martin Chilvers, Michigan Soybean Promotion Committee, Project GREEEN, North Central Soybean Research Program, GRDC Project DAQ00186.

+
+ +
+

+

Site built with pkgdown 2.0.7.

+
+ +
+
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+
+ + + + +
+
+
+

hagis: Tools for Analysis of Plant Pathogen +Pathotype Complexities, Distributions and Diversity

+

Austin McCoy +and Zachary Noel

+ +

2024-03-11

+ + Source: vignettes/hagis.Rmd + + +
+ + + +
+

Getting Started With {hagis} +

+

The following examples are based on a dataset from Michigan State +University Phytophthora sojae surveys for soybean phytophthora +root rot pathotyping efforts.

+

First you’ll want to load in your data set, for right now let’s use a +practice data set made for the {hagis} package, named +P_sojae_survey. The data set is available in your R session +automatically when you load the {hagis} package.

+
+library("hagis")
+head(P_sojae_survey)
+#>    Isolate         Line         Rps Total HR (1) Lesion (2)
+#>      <int>       <char>      <char> <int>  <int>      <int>
+#> 1:       1     Williams susceptible    10      0          0
+#> 2:       1       Harlon      Rps 1a    10      4          0
+#> 3:       1 Harosoy 13xx      Rps 1b     8      0          0
+#> 4:       1     L75-3735      Rps 1c    10     10          0
+#> 5:       1    PI 103091      Rps 1d     9      2          0
+#> 6:       1  Williams 82      Rps 1k    10      0          0
+#>    Lesion to cotyledon (3) Dead (4) total.susc total.resis perc.susc perc.resis
+#>                      <int>    <int>      <int>       <int>     <int>      <int>
+#> 1:                       0       10         10           0       100          0
+#> 2:                       0        6          6           4        60         40
+#> 3:                       0        8          8           0       100          0
+#> 4:                       0        0          0          10         0        100
+#> 5:                       1        6          7           2        78         22
+#> 6:                       0       10         10           0       100          0
+

We see in the gene column that each gene is prepended +with “Rps”. We can remove this to make the graphs cleaner and report the +genes in tables as we would in a manuscript. Note that this will work +for any string you enter as the first value, pattern. The +second string, replacement, is the replacement value, the +third, x, is where to look and make the changes.

+
+P_sojae_survey$Rps <-
+  gsub(pattern = "Rps ",
+       replacement = "",
+       x = P_sojae_survey$Rps)
+head(P_sojae_survey)
+#>    Isolate         Line         Rps Total HR (1) Lesion (2)
+#>      <int>       <char>      <char> <int>  <int>      <int>
+#> 1:       1     Williams susceptible    10      0          0
+#> 2:       1       Harlon          1a    10      4          0
+#> 3:       1 Harosoy 13xx          1b     8      0          0
+#> 4:       1     L75-3735          1c    10     10          0
+#> 5:       1    PI 103091          1d     9      2          0
+#> 6:       1  Williams 82          1k    10      0          0
+#>    Lesion to cotyledon (3) Dead (4) total.susc total.resis perc.susc perc.resis
+#>                      <int>    <int>      <int>       <int>     <int>      <int>
+#> 1:                       0       10         10           0       100          0
+#> 2:                       0        6          6           4        60         40
+#> 3:                       0        8          8           0       100          0
+#> 4:                       0        0          0          10         0        100
+#> 5:                       1        6          7           2        78         22
+#> 6:                       0       10         10           0       100          0
+

This practice data set contains 21 isolates’, Isolate, +virulence data on a set of 14 differential soybean cultivars, +Line. This package uses the percentage of +susceptible, inoculated, plants to determine effective resistance genes, +pathotype diversity and frequency, as well as individual isolates +pathotypes.

+

To help ensure that the proper data are used in calculations, the +user is asked to provide some information that instruct {hagis} about +what data to use.

+
+
+
+

Function Arguments Used in {hagis} +

+

We have striven to make {hagis} as intuitive to use as possible. Part +of that means that we have used the same arguments for the three main +functions, summarize_gene(), +calculate_complexities() and +calculate_diversities(). Each of these functions take the +same arguments:

+
    +

  • x this is your data set name, e.g., +P_sojae_survey from the example above, allows for the +function to identify where it will be pulling these columns (and their +associated row values) from to use (i.e. your data collection +Excel spreadsheet)

    • +

  • cutoff this value sets the cutoff for susceptible +reactions. For example, cutoff = 60 means that all genes +with 60% or more of the plants rated susceptible will be treated as +susceptible. You can change this to whatever percentage you require for +your study.

    • +

  • control specifies the value used in the +gene column to denote a susceptible control used in the +study

    • +

  • sample specifies the column header for the column +which identifies the isolates tested

    • +

  • gene specifies the column header for the column +which identifies the genes tested

    • +

  • perc_susc specifies the column header for the column +which identifies the percent susceptible plants for each gene

    • +
+

Ordinarily you would use functions in {hagis} or other R packages +like this:

+
+Rps.summary <- summarize_gene(
+  x = P_sojae_survey,
+  cutoff = 60,
+  control = "susceptible",
+  sample = "Isolate",
+  gene = "Rps",
+  perc_susc = "perc.susc"
+)
+

However, because the functions share arguments we can create a +list() of arguments and share them with some of the +functions from {hagis}. First, we make a list of the arguments that +summarize_gene(), calculate_diversities() and +calculate_complexities() use that specify our inputs based +on our example data:

+
+hagis_args <- list(
+  x = P_sojae_survey,
+  cutoff = 60,
+  control = "susceptible",
+  sample = "Isolate",
+  gene = "Rps",
+  perc_susc = "perc.susc"
+)
+

Now that we have a list of arguments, we can now save time entering +the same data for each function and also avoid typos or entering +different cutoff values, etc. between the functions.

+
+
+
+

Determination of Effective Resistance Genes +

+

Below is an example of tables and graphics that can be produced using +the summarize_gene() function to identify effective +resistance genes tested against the sampled Phytophthora sojae +population.

+

The summarize_gene() function allows you to produce a +detailed table showing the number of virulent isolates +(N_virulent_isolates), as well as offering a percentage of +the isolates tested which are pathogenic on each gene +(percent_pathogenic).

+
+Rps.summary <- do.call(summarize_gene, hagis_args)
+
+Rps.summary
+#>            gene N_virulent_isolates percent_pathogenic
+#>          <char>               <num>              <num>
+#>  1: susceptible                  21          100.00000
+#>  2:          1a                  21          100.00000
+#>  3:          1b                  15           71.42857
+#>  4:          1c                  20           95.23810
+#>  5:          1d                  16           76.19048
+#>  6:          1k                  18           85.71429
+#>  7:           2                  14           66.66667
+#>  8:          3a                   5           23.80952
+#>  9:          3b                  20           95.23810
+#> 10:          3c                   4           19.04762
+#> 11:           4                   5           23.80952
+#> 12:           5                  13           61.90476
+#> 13:           6                  11           52.38095
+#> 14:           7                  21          100.00000
+

Using the pander library we can make the table much more +attractive in RMarkdown.

+
+library(pander)
+
+pander(Rps.summary)
+ +++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
geneN_virulent_isolatespercent_pathogenic
susceptible21100
1a21100
1b1571.43
1c2095.24
1d1676.19
1k1885.71
21466.67
3a523.81
3b2095.24
3c419.05
4523.81
51361.9
61152.38
721100
+
+

Plotting Rps Summary Data +

+

{hagis} also provides functions to quickly graph your data using +{ggplot2}.

+

Two functions are provided to plot the summary depending on your +needs. If you need the frequency, use +autoplot(Rps.summary, type = "percentage"), or if you +desire the distribution +autoplot(Rps.summary, type = "count"). Both return the same +graph, only the y-axis change; percent for frequency and n for +distribution.

+
+autoplot(Rps.summary, type = "percentage")
+

+
+
+autoplot(Rps.summary, type = "count")
+

+
+
+
+
+

Pathotype Complexities +

+

Pathotype frequency, distribution as well as statistics such as mean +pathotype complexity can be calculated using the +calculate_complexities() function. This function will +return a list() of two data.table() objects, +grouped_complexities and +individual_complexities. grouped_complexities +returns a list() as a data.table() object +showing the frequency and distribution of pathotype complexities for the +sampled population. individual_complexities() returns a +list() as a data.table() object showing each +individual isolates pathotype complexity. An isolates pathotype +complexity refers to the number of resistance genes that it is able to +overcome and cause disease on, i.e., a pathotype complexity of +“7” would mean that isolate can cause disease on 7 different resistance +genes.

+
+complexities <- do.call(calculate_complexities, hagis_args)
+
+complexities
+#> 
+#> Grouped Complexities
+#>     complexity frequency distribution
+#>         <fctr>     <int>        <int>
+#>  1:          1         0            0
+#>  2:          2         0            0
+#>  3:          3         0            0
+#>  4:          4         0            0
+#>  5:          5         1            1
+#>  6:          6         2            2
+#>  7:          7         2            2
+#>  8:          8         7            7
+#>  9:          9         0            0
+#> 10:         10         5            5
+#> 11:         11         3            3
+#> 12:         12         0            0
+#> 13:         13         1            1
+#> 
+#> 
+#> Individual Complexities
+#>     sample N_samp
+#>     <char>  <num>
+#>  1:      1     10
+#>  2:      2     10
+#>  3:      3     10
+#>  4:      4      8
+#>  5:      5      8
+#>  6:      6      8
+#>  7:      7      8
+#>  8:      8      8
+#>  9:      9      6
+#> 10:     10      5
+#> 11:     11      6
+#> 12:     12      8
+#> 13:     13      7
+#> 14:     14      8
+#> 15:     15     11
+#> 16:     16      7
+#> 17:     17     10
+#> 18:     18     10
+#> 19:     19     11
+#> 20:     20     11
+#> 21:     21     13
+#>     sample N_samp
+

Once again, using pander we can make these tables much more +attractive in RMarkdown. Since complexities is a +list() object, we can refer to each object directly by name +and print them as follows.

+
+pander(complexities$grouped_complexities)
+ +++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
complexityfrequencydistribution
100
200
300
400
511
622
722
877
900
1055
1133
1200
1311
+
+
+pander(complexities$indvidual_complexities)
+ ++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
sampleN_samp
110
210
310
48
58
68
78
88
96
105
116
128
137
148
1511
167
1710
1810
1911
2011
2113
+

Using summary() will return the mean, standard error +(se) and standard deviation (sd) for pathotypes of a complexities +object.

+
+pander(summary(complexities))
+ +++++ + + + + + + + + + + +
MeanSDSE
8.7142.0040.4372
+
+

Plotting Complexities Data +

+

Two functions are provided to plot the complexities depending on your +needs. If you need the frequency, use +autoplot(complexties, type = "percentage"), or if you +desire the distribution +autoplot(complexities, type = "count"). Both return the +same graph, only the y-axis change; percent for frequency and n for +distribution.

+
+autoplot(complexities, type = "percentage")
+

+
+
+autoplot(complexities, type = "count")
+

+
+
+
+
+

Diversity Indices, Frequency of Unique Pathotypes and Individual +Isolate Pathotypes +

+

Diversity indices are extremely useful when trying to identify +differences between two populations. Here, pathotype diversities are +calculated for the isolate population using the +calculate_diversities() function. Likewise, individual +isolates’ pathotypes, number of isolates used in the study, number of +pathotypes within the study are calculated.

+

Five diversity indices are calculated when calling +calculate_diversities().

+
    +

  • Simple diversity index, which will show the proportion of unique +pathotypes to total samples. As the values gets closer to 1, there is +greater diversity in pathoypes within the population. Simple diversity +is calculated as: \[D = +\frac{Np}{Ns}\] where \(Np\) is +the number of pathotypes and \(Ns\) is +the number of samples.

    • +

  • Gleason diversity index, an alternate version of Simple diversity +index, is less sensitive to sample size than the Simple index. \[D = \frac{ (Np - 1) }{ log(Ns)}\] Where +\(Np\) is the number of pathotypes and +\(Ns\) is the number of +samples.

    • +

  • Shannon diversity index is typically between 1.5 and 3.5, as +richness and evenness of the population increase, so does the Shannon +index value. \[D = -\sum_{i = 1}^{R} p_i \log +p_i\] Where \(p_i\) is the +proportional abundance of species \(i\).

    • +

  • Simpson diversity index values range from 0 to 1, 1 represents +high diversity and 0 represents no diversity. Where diversity is +calculated as: \[D = \sum_{i = 1}^{R} +p_i^2\]

    • +

  • Evenness ranges from 0 to 1, as the Evenness value approaches 1, +there is a more even distribution of each pathotype’s frequency within +the population. Where Evenness is calculated as: \[D = \frac{H'}{log(Np)}\] where \(H'\) is the Shannon diversity index and +\(Np\) is the number of +pathotypes.

    • +
+
+diversity <- do.call(calculate_diversities, hagis_args)
+diversity
+#> 
+#> hagis Diversities
+#> 
+#> Number of Samples 21
+#> Number of Pathotypes 19 
+#> 
+#> Indices
+#> Simple   0.9047619 
+#> Gleason  5.912257 
+#> Shannon  2.912494 
+#> Simpson  0.9433107 
+#> Evenness  0.9891509
+

Or using pander for reporting, a nice table is +generated.

+
+pander(diversity)
+ + +++++++ + + + + + + + + + + + + + + +
Diversity indices where n = 21 with 19 pathotypes.
SimpleGleasonShannonSimpsonEvenness
0.90485.9122.9120.94330.9892
+
+

Table of Diversities +

+

To generate a table of diversities, use +diversities_table(). {hagis} will automatically create a +pander object for you. This is because it is much easier to +read the resulting table in the console than the raw +data.frame and insert into reports.

+
+diversities_table(diversity)
+ ++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
FrequencyPathotype
11a, 1b, 1c, 1d, 1k, 2, 3a, 3b, 3c, 4, 5, 6, 7
11a, 1b, 1c, 1d, 1k, 2, 3a, 3b, 3c, 5, 7
11a, 1b, 1c, 1d, 1k, 2, 3a, 3b, 3c, 6, 7
11a, 1b, 1c, 1d, 1k, 2, 3b, 4, 5, 6, 7
11a, 1b, 1c, 1d, 1k, 2, 3b, 4, 5, 7
11a, 1b, 1c, 1d, 1k, 2, 3b, 4, 6, 7
21a, 1b, 1c, 1d, 1k, 2, 3b, 7
11a, 1b, 1c, 1d, 1k, 2, 6, 7
11a, 1b, 1c, 1d, 1k, 3a, 3b, 5, 6, 7
11a, 1b, 1c, 1d, 1k, 3b, 7
11a, 1b, 1c, 1k, 2, 3b, 3c, 4, 6, 7
11a, 1b, 1c, 1k, 3b, 5, 6, 7
11a, 1b, 1c, 1k, 3b, 5, 7
11a, 1b, 1d, 1k, 2, 3a, 3b, 5, 6, 7
21a, 1c, 1d, 1k, 2, 3b, 5, 7
11a, 1c, 1d, 1k, 2, 3b, 6, 7
11a, 1c, 1d, 3b, 5, 7
11a, 1c, 3b, 5, 6, 7
11a, 1c, 3b, 5, 7
+

To generate a table of individual pathotypes, use +individual_pathotypes(). Here again, {hagis} provides a +pander object for ease of use.

+
+
+

Table of Individual Pathotypes +

+ + ++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
SamplePathotype
11a, 1b, 1d, 1k, 2, 3a, 3b, 5, 6, 7
101a, 1c, 3b, 5, 7
111a, 1c, 3b, 5, 6, 7
121a, 1b, 1c, 1d, 1k, 2, 6, 7
131a, 1b, 1c, 1d, 1k, 3b, 7
141a, 1b, 1c, 1k, 3b, 5, 6, 7
151a, 1b, 1c, 1d, 1k, 2, 3b, 4, 5, 6, 7
161a, 1b, 1c, 1k, 3b, 5, 7
171a, 1b, 1c, 1d, 1k, 2, 3b, 4, 5, 7
181a, 1b, 1c, 1d, 1k, 3a, 3b, 5, 6, 7
191a, 1b, 1c, 1d, 1k, 2, 3a, 3b, 3c, 6, 7
21a, 1b, 1c, 1k, 2, 3b, 3c, 4, 6, 7
201a, 1b, 1c, 1d, 1k, 2, 3a, 3b, 3c, 5, 7
211a, 1b, 1c, 1d, 1k, 2, 3a, 3b, 3c, 4, 5, 6, 7
31a, 1b, 1c, 1d, 1k, 2, 3b, 4, 6, 7
41a, 1c, 1d, 1k, 2, 3b, 5, 7
51a, 1c, 1d, 1k, 2, 3b, 6, 7
61a, 1c, 1d, 1k, 2, 3b, 5, 7
71a, 1b, 1c, 1d, 1k, 2, 3b, 7
81a, 1b, 1c, 1d, 1k, 2, 3b, 7
91a, 1c, 1d, 3b, 5, 7
+
+
+
+

Advanced Plotting +

+
+

{hagis} autoplot() Objects +

+

Since {hagis} uses {ggplot2} to generate its plots, you can easily +theme the outputs using common {ggplot2} themes and other options +provided by {hagis} directly through autoplot().

+
+library(ggplot2)
+
+Rps.plot <- autoplot(Rps.summary, type = "percentage")
+
+Rps.plot
+

+
+

Changing the ggplot2 Theme +

+

Use {ggplot2}’s theme_minimal() theme.

+
+Rps.plot <- Rps.plot +
+  theme_minimal()
+
+Rps.plot
+

+
+
+

Changing the Font +

+

Set the font to be a bold-face serif family font.

+
+Rps.plot <- Rps.plot +
+  theme(text = element_text(face = "bold",
+                            family = "serif"))
+
+Rps.plot
+

+
+
+

Make a Horizontal Plot +

+

If your Rps gene names are too long, flipping the axis can +make the graph more legible without rotating the x-axis labels.

+
+Rps.plot <- Rps.plot +
+  coord_flip()
+
+Rps.plot
+

+
+
+

Use Colors in Autoplot Objects +

+

You can use named, e.g., “red”, “yellow”, “blue”, colors in +R or you can use custom hexadecimal color codes. Illustrated below is +using Michigan State University (MSU) Green, hex code #18453b, using +theme_bw() with a serif font.

+
+autoplot(Rps.summary,
+         type = "percentage",
+         color = "#18453b") +
+  theme_bw() +
+  theme(text = element_text(face = "bold",
+                            family = "serif"))
+

+
+
+

Sorting the x-axis +

+

You can sort the x-axis of any graph produced using +autoplot() in an ascending or +descending order using the order parameter in +autoplot().

+
+autoplot(Rps.summary,
+          type = "percentage",
+          color = "#18453b",
+          order = "ascending") +
+  theme_bw() +
+  theme(text = element_text(face = "bold",
+                            family = "serif"))
+

+
+
+
+
+ + + +
+ + + +
+

+

Developed by Austin G. McCoy, Zachary Noel, Adam H. Sparks, Martin Chilvers, Michigan Soybean Promotion Committee, Project GREEEN, North Central Soybean Research Program, GRDC Project DAQ00186.

+
+ +
+

+

Site built with pkgdown 2.0.7.

+
+ +
+
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+
+ + + +
+
+
+

Articles

+
+ +
+

All vignettes

+

+ +
Beta-diversity Analysis with hagis
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+
hagis: Tools for Analysis of Plant Pathogen Pathotype Complexities, Distributions and Diversity
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+
+
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+ + +
+

Developed by Austin G. McCoy, Zachary Noel, Adam H. Sparks, Martin Chilvers, Michigan Soybean Promotion Committee, Project GREEEN, North Central Soybean Research Program, GRDC Project DAQ00186.

+
+ +
+

Site built with pkgdown 2.0.7.

+
+ +
+ + + + + + + + diff --git a/authors.html b/authors.html new file mode 100644 index 00000000..7941f293 --- /dev/null +++ b/authors.html @@ -0,0 +1,160 @@ + +Authors and Citation • hagis + + +
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+ + + +
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+
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+

Authors

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+ + +
  • +

    Austin G. McCoy. Author, conceptor. +

    +
    • +
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    Zachary Noel. Author, conceptor. +

    +
    • +
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    Adam H. Sparks. Author, maintainer. +

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    • +
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    Martin Chilvers. Author, thesis advisor. +

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    Zhian N. Kamvar. Contributor, reviewer. +

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    Michigan Soybean Promotion Committee. Funder. +

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    Project GREEEN. Funder. +

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    North Central Soybean Research Program. Funder. +

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    GRDC Project DAQ00186. Funder. +

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Citation

+ Source: inst/CITATION +
+
+ + +

McCoy AG, Noel ZA, Sparks AH, Chilvers M (2019). +“hagis, an R Package Resource for Pathotype Analysis of Phytophthora sojae Populations Causing Stem and Root Rot of Soybean.” +Molecular Plant-Microbe Interactions, 32(12), 1574–1576. +doi:10.1094/MPMI-07-19-0180-A, https://doi.org/10.1094/MPMI-07-19-0180-A. +

+ 
@Article{,
+  author = {Austin Glenn McCoy and Zachary A. Noel and Adam H. Sparks and Martin Chilvers},
+  title = {{hagis}, an {R} Package Resource for Pathotype Analysis of {Phytophthora sojae} Populations Causing Stem and Root Rot of Soybean},
+  journal = {Molecular Plant-Microbe Interactions},
+  year = {2019},
+  volume = {32},
+  number = {12},
+  pages = {1574–1576},
+  month = {nov},
+  doi = {10.1094/MPMI-07-19-0180-A},
+  url = {https://doi.org/10.1094/MPMI-07-19-0180-A},
+}
+

McCoy AG, Noel ZA, Sparks AH, Chilvers MI (????). +hagis: Analysis of Plant Pathogen Pathotype Complexities, Distributions and Diversity. +doi:10.5281/zenodo.2619820, R package version 3.1.11.9000, https://openplantpathology.github.io/hagis/. +

+ 
@Manual{,
+  title = {{hagis}: Analysis of Plant Pathogen Pathotype Complexities, Distributions and Diversity},
+  author = {Austin G. McCoy and Zachary A. Noel and Adam H. Sparks and Martin I Chilvers},
+  note = {R package version 3.1.11.9000},
+  url = {https://openplantpathology.github.io/hagis/},
+  doi = {10.5281/zenodo.2619820},
+}
+ +
+ +
+ + + +
+

Developed by Austin G. McCoy, Zachary Noel, Adam H. Sparks, Martin Chilvers, Michigan Soybean Promotion Committee, Project GREEEN, North Central Soybean Research Program, GRDC Project DAQ00186.

+
+ +
+

Site built with pkgdown 2.0.7.

+
+ +
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+ // loop to fetch from lvl0, lvl1, etc. + for (var idx in hierarchy) { + words = words.concat(hierarchy[idx].matchedWords); + } + + var content = hit._highlightResult.content; + if (content) { + words = words.concat(content.matchedWords); + } + + // return unique words + var words_uniq = [...new Set(words)]; + return words_uniq; +} + +function updateHitURL(hit) { + + var words = matchedWords(hit); + var url = ""; + + if (hit.anchor) { + url = hit.url_without_anchor + '?q=' + escape(words.join(" ")) + '#' + hit.anchor; + } else { + url = hit.url + '?q=' + escape(words.join(" ")); + } + + return url; +} diff --git a/index.html b/index.html new file mode 100644 index 00000000..7ea6626d --- /dev/null +++ b/index.html @@ -0,0 +1,263 @@ + + + + + + + +Analysis of Plant Pathogen Pathotype Complexities, Distributions and Diversity • hagis + + + + + + + + + + + + +
    +
    + + + + +
    +
    +
    +

    hagis +

    + + +
    +

    Introduction +

    +

    The goal of {hagis} is to provide analysis tools for plant pathogens with gene-for-gene interactions in the R programming language that the original Habgood-Gilmour Spreadsheet, HaGiS, (Herrmann, Löwer and Schachtel) provided.

    +

    This R package has been published in MPMI as a resource announcement (McCoy et al. 2019). You may wish to refer to that paper for further information on this package.

    +
    +
    +

    Overview +

    +

    {hagis} was initially created for Phytophthora sojae surveys by Dr. Austin McCoy and Dr. Zachary Noel at Michigan State University in the US, where the disease has been managed primarily via deployment of resistance genes (Rps genes, resistance to P. sojae) in commercial soybean cultivars and by the application of fungicide seed treatments. However, repeated use of resistance genes can cause populations to adapt over time rendering these resistance genes ineffective. To determine current effectiveness of resistance genes for managing P. sojae, state-wide surveys (in the US) are conducted to determine the pathotype (previously referred to as “race”) structure within sampled population of P. sojae.

    +

    However, the package is not only useful for P. sojae work. It was built to be useful for other plant pathogen gene-for-gene interaction systems, e.g., wheat rusts, but it has been used in animal science as well, see Cain et al. 2022.

    +

    The goal of this package is to provide all the necessary analyses needed when conducting a pathotype surveys, including: distribution of susceptibilities (effective and non-effective resistance genes), distribution of pathotype complexities with statistics, pathotype frequency distribution, as well as diversity indices for pathotypes in an efficient and reproducible manner.

    +

    New users are encouraged to visit the documentation, https://openplantpathology.github.io/hagis/articles/hagis.html, for detailed information on how to use {hagis} along with working examples using a built-in data set.

    +
    +
    +

    Quick Start Install +

    +
    +

    Stable Version +

    +

    A CRAN version of {hagis} is available but lags behind the GitHub version. They both will give exactly the same results.

    + +
    +
    +

    Development Version +

    +

    A development version is available from from GitHub that may provide bug fixes or new features not yet available from CRAN.

    +
    +if (!require("remotes")) {
+  install.packages("remotes", repos = "http://cran.rstudio.com/")
+  library("remotes")
+}
+
+install_github("openplantpathology/hagis", build_vignettes = TRUE)
    +
    +
    +
    +

    Meta +

    +
    +

    Citation +

    +

    When you use {hagis}, please cite by using:

    +
    +citation("hagis")
    +
    ## To cite package 'hagis' in publications use:
+## 
+##   McCoy AG, Noel ZA, Sparks AH, Chilvers M (2019). "hagis, an R Package
+##   Resource for Pathotype Analysis of Phytophthora sojae Populations
+##   Causing Stem and Root Rot of Soybean." _Molecular Plant-Microbe
+##   Interactions_, *32*(12), 1574–1576. doi:10.1094/MPMI-07-19-0180-A
+##   <https://doi.org/10.1094/MPMI-07-19-0180-A>,
+##   <https://doi.org/10.1094/MPMI-07-19-0180-A>.
+## 
+##   McCoy AG, Noel ZA, Sparks AH, Chilvers MI (????). _hagis: Analysis of
+##   Plant Pathogen Pathotype Complexities, Distributions and Diversity_.
+##   doi:10.5281/zenodo.2619820 <https://doi.org/10.5281/zenodo.2619820>,
+##   R package version 3.1.10.9000,
+##   <https://openplantpathology.github.io/hagis/>.
+## 
+## To see these entries in BibTeX format, use 'print(<citation>,
+## bibtex=TRUE)', 'toBibtex(.)', or set
+## 'options(citation.bibtex.max=999)'.
    +
    +
    +

    Code of Conduct +

    +

    Please note that the {hagis} project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

    +
    +
    +
    +

    References +

    +
    +

    Herrmann, Löwer and Schachtel. (1999), A new tool for entry and analysis of virulence data for plant pathogens. Plant Pathology, 48: 154-158. DOI: 10.1046/j.1365-3059.1999.00325.x

    +
    +
    +

    McCoy, Noel, Sparks and Chilvers. (2019). ‘hagis’, an R Package Resource for Pathotype Analysis of Phytophthora sojae Populations Causing Stem and Root Rot of Soybean. Molecular Plant-Microbe Interactions 32.12 (Nov 2019) p. 1574-1576. DOI: 10.1094/MPMI-07-19-0180-A

    +
    +
    +

    Cain, Norris, Ripley, Suri, Finnerty, Gravatte, and Nielsen, 2022. The microbial community associated with Parascaris spp. infecting juvenile horses. Parasites & Vectors, 15(1), pp.1-17. 10.1186/s13071-022-05533-y

    +
    +
    +
    +
    + + +
    + + +
    +

    +

    Developed by Austin G. McCoy, Zachary Noel, Adam H. Sparks, Martin Chilvers, Michigan Soybean Promotion Committee, Project GREEEN, North Central Soybean Research Program, GRDC Project DAQ00186.

    +
    + +
    +

    +

    Site built with pkgdown 2.0.7.

    +
    + +
    +
    + + + + + + + + diff --git a/issue_template.html b/issue_template.html new file mode 100644 index 00000000..f74caba0 --- /dev/null +++ b/issue_template.html @@ -0,0 +1,91 @@ + +NA • hagis + + +
    +
    + + + +
    +
    +
    +

    NA

    +
    + +
    Session Info +
    +
    +
    + + + +
    + + + +
    +

    Developed by Austin G. McCoy, Zachary Noel, Adam H. Sparks, Martin Chilvers, Michigan Soybean Promotion Committee, Project GREEEN, North Central Soybean Research Program, GRDC Project DAQ00186.

    +
    + +
    +

    Site built with pkgdown 2.0.7.

    +
    + +
    + + + + + + + + diff --git a/link.svg b/link.svg new file mode 100644 index 00000000..88ad8276 --- /dev/null +++ b/link.svg @@ -0,0 +1,12 @@ + + + + + + diff --git a/news/index.html b/news/index.html new file mode 100644 index 00000000..44d62284 --- /dev/null +++ b/news/index.html @@ -0,0 +1,252 @@ + +Changelog • hagis + + +
    +
    + + + +
    +
    +
    +

    Changelog

    + Source: NEWS.md +
    + +
    +

    hagis (development version)

    +
    +
    +

    hagis 3.1.112023-09-06

    +
    +

    Minor changes

    +

    • Put more guardrails in place for CRAN. Ensure that all tests are skipped on CRAN using a universal statement rather than skip_on_cran().

      • +

    • Use data.table::setDTthreads(1L) at the top of both vignettes.

      • +

    • Use @examplesIf interactive() for all examples so that they don’t run on CRAN.

      • +
    +
    +

    Bug fixes

    +
    • Removes a redundant zzz.R file that had globals in it. This revealed that one of the autoplot functions was missing the @autoglobal tag, which has been added.
      • +
    +
    +
    +

    hagis 3.1.10

    +
    +

    Minor changes

    +
    • Skip ALL tests on CRAN. Because, y’know, CRAN…
      • +
    +
    +
    +

    hagis 3.1.9

    +
    +

    “Bug” fixes

    +
    • Only run plotting examples if session is interactive to “fix” a “bug” with CRAN determining that the examples suddenly take too long to run.
      • +
    +
    +
    +

    hagis 3.1.8

    +
    +

    Bug fixes

    +
    • Fix “long running” tests.
      • +
    +
    +

    Minor changes

    +

    • Add Cain et al. paper to README.

      • +

    • Minor grammar edits.

      • +
    +
    +
    +

    hagis 3.1.7

    +
    +

    Minor changes

    +

    • Use {roxyglobals}.

      • +

    • Format package names as {package name} not package name or package name in documentation.

      • +
    +
    +

    Bug fixes

    +
    • Use proper title case in function titles.
      • +
    +
    +
    +

    hagis 3.1.62023-04-25

    +
    +

    Bug fixes

    +
    • Fixes bug where .create_summary_isolate(), an internal function, was exported. It should not be user-facing and is now no longer exported or documented.
      • +
    +
    +
    +

    hagis 3.1.5

    +
    +

    Minor changes

    +

    • Improved documentation formatting.

      • +

    • Improved handling of internal global variables.

      • +

    • Further updates to test infrastructure.

      • +

    • Update CITATION to follow CRAN’s desired format.

      • +
    +
    +
    +

    hagis 3.1.42022-08-15

    +
    +

    Minor changes

    +

    • Update outdated URLs.

      • +

    • Update test infrastructure.

      • +
    +
    +
    +

    hagis 3.1.32021-10-14

    +
    +

    Minor changes

    +

    • Sample names in calculate_diversities() are not required to be numeric values. Previously, this column was converted to numeric so if character values were present, these values became NA. This allows for greater flexibility when analysing the data as sample names are often more descriptive than just a numerical value.

      • +

    • Spelling corrections in code comments

      • +

    • Clean up CITATION file

      • +

    • README is now more complete with information and links to the MPMI paper

      • +
    +
    +
    +

    hagis 3.1.22021-09-30

    +
    +

    Minor changes

    +

    • Improved documentation formatting

      • +

    • Update ROxygen details

      • +

    • Fixes incomplete end of line in test-create_binary_matrix.R

      • +

    • Add wordlist of allowed words for spellchecking

      • +

    • More consistent code styling in vignettes

      • +

    • Prefer “_” to “*” for italics

      • +

    • More verbose handling of importing {data.table} as a whole package using “R/utils-data.table.R” in place of “R/zzz.R”

      • +
    +
    +
    +

    hagis 3.1.12020-12-09

    +
    +

    Minor Changes

    +

    • Use {ape}, {vegan}, {dplyr} and {vidiffr} packages conditionally

      • +

    • Remove {covr} from Suggests

      • +

    • Better documentation formatting

      • +
    +
    +
    +

    hagis 3.1.02020-10-07

    +
    +

    Major Changes

    +

    • Add new function, create_binary_matrix() to format data for exporting beta diversity matrices representing the pathotype of each isolate. Users can export a binary pathotype data matrix which could then be used to visualize beta-diversity of pathotypes using {vegan} or {ape} in R

      • +

    • Add new vignette, “Beta-diversity Analyses”, to illustrate the use of the new functionality

      • +
    +
    +

    Minor Changes

    +

    • Use ROxygen 7.1.1

      • +

    • Spell check and correct spelling errors

      • +
    +
    +
    +

    hagis 3.0.12019-11-18

    +
    +

    Minor Changes

    +

    • Update citation with full MPMI citation

      • +

    • Fix issue in CITATION file where {nasapower} was referred to in text

      • +

    • Use ROxygen 7.0.0

      • +

    • Remove an extra “/” in the CITATION’s DOI

      • +
    +
    +
    +

    hagis 3.0.02019-08-26

    +
    +

    Defunct functions

    +
    • +plot() is now defunct. Use autoplot() to plot {hagis} objects in place of plot(). This is to avoid the side-effect of generating and displaying a plot every time plot() is called, which can be troublesome when using {ggplot2} themes since it created two plots, the original with the base theme and the new themed plot
      • +
    +
    +

    Minor Changes

    +

    • Rename output column N_susc to N_virulent_isolates

      • +

    • Don’t round results from summarize_gene() or calculate_complexities() before returning values to user

      • +

    • Implement fix suggested by @zkamvar to ensure that the user-input data is not changed from a data.frame or tibble object to a data.table object in the R session

      • +

    • Add ability to sort graph x-axis in ascending or descending order based on the y-axis values rather than only by gene or complexity.

      • +

    • Move example data set into internal data and provide documentation for them

      • +

    • Provide documentation for how diversity indices are calculated along with mathematical notation where possible to display

      • +

    • Calculate Shannon and Simpson indices internally rather than rely on {vegan} to reduce number of Dependencies

      • +

    • Replace the term field with column in documentation

      • +

    • Test coverage now 100 %

      • +

    • Add funding agencies to DESCRIPTION field

      • +
    +
    +
    +

    hagis 2.0.02019-06-24

    +

    • Initial CRAN release

      • +

    • Completely new R-package format rather than just Rmd and script files

      • +
    +
    +

    hagis 1.0.0

    +
    • Initial release of Rmd and script files by A. McCoy and Z. Noel
      • +
    +
    + + + +
    + + +
    +

    Developed by Austin G. McCoy, Zachary Noel, Adam H. Sparks, Martin Chilvers, Michigan Soybean Promotion Committee, Project GREEEN, North Central Soybean Research Program, GRDC Project DAQ00186.

    +
    + +
    +

    Site built with pkgdown 2.0.7.

    +
    + +
    + + + + + + + + diff --git a/pkgdown.css b/pkgdown.css new file mode 100644 index 00000000..80ea5b83 --- /dev/null +++ b/pkgdown.css @@ -0,0 +1,384 @@ +/* Sticky footer */ + +/** + * Basic idea: https://philipwalton.github.io/solved-by-flexbox/demos/sticky-footer/ + * Details: https://github.com/philipwalton/solved-by-flexbox/blob/master/assets/css/components/site.css + * + * .Site -> body > .container + * .Site-content -> body > .container .row + * .footer -> footer + * + * Key idea seems to be to ensure that .container and __all its parents__ + * have height set to 100% + * + */ + +html, body { + height: 100%; +} + +body { + position: relative; +} + +body > .container { + display: flex; + height: 100%; + flex-direction: column; +} + +body > .container .row { + flex: 1 0 auto; +} + +footer { + margin-top: 45px; + padding: 35px 0 36px; + border-top: 1px solid #e5e5e5; + color: #666; + display: flex; + flex-shrink: 0; +} +footer p { + margin-bottom: 0; +} +footer div { + flex: 1; +} +footer .pkgdown { + text-align: right; +} +footer p { + margin-bottom: 0; +} + +img.icon { + float: right; +} + +/* Ensure in-page images don't run outside their container */ +.contents img { + max-width: 100%; + height: auto; +} + +/* Fix bug in bootstrap (only seen in firefox) */ +summary { + display: list-item; +} + +/* Typographic tweaking ---------------------------------*/ + +.contents .page-header { + margin-top: calc(-60px + 1em); +} + +dd { + margin-left: 3em; +} + +/* Section anchors ---------------------------------*/ + +a.anchor { + display: none; + margin-left: 5px; + width: 20px; + height: 20px; + + background-image: url(./link.svg); + background-repeat: no-repeat; + background-size: 20px 20px; + background-position: center center; +} + +h1:hover .anchor, +h2:hover .anchor, +h3:hover .anchor, +h4:hover .anchor, +h5:hover .anchor, +h6:hover .anchor { + display: inline-block; +} + +/* Fixes for fixed navbar --------------------------*/ + +.contents h1, .contents h2, .contents h3, .contents h4 { + padding-top: 60px; 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+} +.hanging-indent div.csl-entry { + margin-left:2em; + text-indent:-2em; +} +div.csl-left-margin { + min-width:2em; + float:left; +} +div.csl-right-inline { + margin-left:2em; + padding-left:1em; +} +div.csl-indent { + margin-left: 2em; +} diff --git a/pkgdown.js b/pkgdown.js new file mode 100644 index 00000000..6f0eee40 --- /dev/null +++ b/pkgdown.js @@ -0,0 +1,108 @@ +/* http://gregfranko.com/blog/jquery-best-practices/ */ +(function($) { + $(function() { + + $('.navbar-fixed-top').headroom(); + + $('body').css('padding-top', $('.navbar').height() + 10); + $(window).resize(function(){ + $('body').css('padding-top', $('.navbar').height() + 10); + }); + + $('[data-toggle="tooltip"]').tooltip(); + + var cur_path = paths(location.pathname); + var links = $("#navbar ul li a"); + var max_length = -1; + var pos = -1; + for (var i = 0; i < links.length; i++) { + if (links[i].getAttribute("href") === "#") + continue; + // Ignore external links + if (links[i].host !== location.host) + continue; + + var nav_path = paths(links[i].pathname); + + var length = prefix_length(nav_path, cur_path); + if (length > max_length) { + max_length = length; + pos = i; + } + } + + // Add class to parent
  • , and enclosing
  • if in dropdown + if (pos >= 0) { + var menu_anchor = $(links[pos]); + menu_anchor.parent().addClass("active"); + menu_anchor.closest("li.dropdown").addClass("active"); + } + }); + + function paths(pathname) { + var pieces = pathname.split("/"); + pieces.shift(); // always starts with / + + var end = pieces[pieces.length - 1]; + if (end === "index.html" || end === "") + pieces.pop(); + return(pieces); + } + + // Returns -1 if not found + function prefix_length(needle, haystack) { + if (needle.length > haystack.length) + return(-1); + + // Special case for length-0 haystack, since for loop won't run + if (haystack.length === 0) { + return(needle.length === 0 ? 0 : -1); + } + + for (var i = 0; i < haystack.length; i++) { + if (needle[i] != haystack[i]) + return(i); + } + + return(haystack.length); + } + + /* Clipboard --------------------------*/ + + function changeTooltipMessage(element, msg) { + var tooltipOriginalTitle=element.getAttribute('data-original-title'); + element.setAttribute('data-original-title', msg); + $(element).tooltip('show'); + element.setAttribute('data-original-title', tooltipOriginalTitle); + } + + if(ClipboardJS.isSupported()) { + $(document).ready(function() { + var copyButton = ""; + + $("div.sourceCode").addClass("hasCopyButton"); + + // Insert copy buttons: + $(copyButton).prependTo(".hasCopyButton"); + + // Initialize tooltips: + $('.btn-copy-ex').tooltip({container: 'body'}); + + // Initialize clipboard: + var clipboardBtnCopies = new ClipboardJS('[data-clipboard-copy]', { + text: function(trigger) { + return trigger.parentNode.textContent.replace(/\n#>[^\n]*/g, ""); + } + }); + + clipboardBtnCopies.on('success', function(e) { + changeTooltipMessage(e.trigger, 'Copied!'); + e.clearSelection(); + }); + + clipboardBtnCopies.on('error', function() { + changeTooltipMessage(e.trigger,'Press Ctrl+C or Command+C to copy'); + }); + }); + } +})(window.jQuery || window.$) diff --git a/pkgdown.yml b/pkgdown.yml new file mode 100644 index 00000000..1c599872 --- /dev/null +++ b/pkgdown.yml @@ -0,0 +1,8 @@ +pandoc: 3.1.11 +pkgdown: 2.0.7 +pkgdown_sha: ~ +articles: + betadiversity: betadiversity.html + hagis: hagis.html +last_built: 2024-03-11T04:30Z + diff --git a/pull_request_template.html b/pull_request_template.html new file mode 100644 index 00000000..bf864e5a --- /dev/null +++ b/pull_request_template.html @@ -0,0 +1,108 @@ + +NA • hagis + + +
    +
    + + + +
    +
    +
    +

    NA

    +
    + + +
    +

    Description

    + +
    +
    + + +
    +
    +

    Example

    + + +
    + + +
    + + + +
    + + + +
    +

    Developed by Austin G. McCoy, Zachary Noel, Adam H. Sparks, Martin Chilvers, Michigan Soybean Promotion Committee, Project GREEEN, North Central Soybean Research Program, GRDC Project DAQ00186.

    +
    + +
    +

    Site built with pkgdown 2.0.7.

    +
    + +
    + + + + + + + + diff --git a/reference/P_sojae_survey.html b/reference/P_sojae_survey.html new file mode 100644 index 00000000..ac6d7e14 --- /dev/null +++ b/reference/P_sojae_survey.html @@ -0,0 +1,148 @@ + +Phytophthora sojae Survey Example Data — P_sojae_survey • hagis + + +
    +
    + + + +
    +
    +
    +

    Phytophthora sojae Survey Example Data

    + Source: R/data.R + +
    + +
    +

    Data from a Phytophthora sojae survey

    +
    + +
    + 
    data(P_sojae_survey)
    +
    + +
    +

    Format

    +

    An object of class data.table with 294 observations of 12 variables

    Isolate
    +

    P. sojae isolate identifier

    + +
    Line
    +

    Soybean cultivar

    + +
    Rps
    +

    Rps gene identifier

    + +
    Total
    +

    Total number of plants inoculated

    + +
    HR (1)
    +

    Number of plants that exhibit a hypersensitive response after +inoculation

    + +
    Lesion (2)
    +

    Number of plants that develop a lesion at inoculation +site

    + +
    Lesion to cotyledon (3)
    +

    Number of plants that develop a lesion, +which advances to the hypocotyl of the seedling after infection

    + +
    Dead (4)
    +

    Number of dead plants that are observed after inoculation

    + +
    total.susc
    +

    The total number of susceptible plants (Lesion+Lesion to +cotyledon+Dead)

    + +
    total.resis
    +

    The total number of resistant plants (equal to HR +value)

    + + +
    +
    +

    Source

    +

    Data from an ongoing 2017 Phytophthora sojae survey in Michigan, +conducted by A. G. McCoy et al..

    +
    + +
    +

    Examples

    + 
    if (FALSE) { # interactive()
+data(P_sojae_survey)
+P_sojae_survey
+}
+
    +
    +
    + +
    + + +
    +

    Developed by Austin G. McCoy, Zachary Noel, Adam H. Sparks, Martin Chilvers, Michigan Soybean Promotion Committee, Project GREEEN, North Central Soybean Research Program, GRDC Project DAQ00186.

    +
    + +
    +

    Site built with pkgdown 2.0.7.

    +
    + +
    + + + + + + + + diff --git a/reference/Rplot001.png b/reference/Rplot001.png new file mode 100644 index 0000000000000000000000000000000000000000..17a358060aed2a86950757bbd25c6f92c08c458f GIT binary patch literal 1011 zcmeAS@N?(olHy`uVBq!ia0y~yV0-|=9Be?5+AI5}0x7m6Z+90U4Fo@(ch>_c&H|6f zVg?3oArNM~bhqvg0|WD9PZ!6KiaBo&GBN^{G%5UFpXcEKVvd5*5Eu=C0SJK)8A6*F U7`aXvEC5;V>FVdQ&MBb@00SN#Z2$lO literal 0 HcmV?d00001 diff --git a/reference/autoplot.hagis.complexities.html b/reference/autoplot.hagis.complexities.html new file mode 100644 index 00000000..f11ab161 --- /dev/null +++ b/reference/autoplot.hagis.complexities.html @@ -0,0 +1,158 @@ + +Plot hagis Complexities Objects — autoplot.hagis.complexities • hagis + + +
    +
    + + + +
    +
    +
    +

    Plot hagis Complexities Objects

    + Source: R/calculate_complexities.R + +
    + +
    +

    Creates a ggplot2 object of the frequency of +complexity (percent per complexity) or a ggplot2 object of the +distribution (number per complexity) calculated by +calculate_complexities().

    +
    + +
    + 
    # S3 method for hagis.complexities
+autoplot(object, type, color = NULL, order = NULL, ...)
    +
    + +
    +

    Arguments

    +
    object
    +

    a hagis complexities object generated by +calculate_complexities(). Character.

    + + +
    type
    +

    a vector of values for which the bar plot is desired. Specify +whether to return a graph of the frequency of complexities as a percentage, +"percentage", or as the count, "count". Character.

    + + +
    color
    +

    a named or hexadecimal color value to use for the bar color

    + + +
    order
    +

    sort the x-axis of the bar chart by ascending or descending +order of frequency. Accepts ascending or descending input values. +Defaults to complexity value. Character.

    + + +
    ...
    +

    passed to the chosen geom(s)

    + +
    +
    +

    Value

    + + +

    A ggplot2 object

    +
    + +
    +

    Examples

    + 
    if (FALSE) { # interactive()
+# Using the built-in data set, `P_sojae_survey`
+data(P_sojae_survey)
+
+# calculate susceptibilities with a 60 % cutoff value
+complexities <- calculate_complexities(x = P_sojae_survey,
+                                       cutoff = 60,
+                                       control = "susceptible",
+                                       sample = "Isolate",
+                                       gene = "Rps",
+                                       perc_susc = "perc.susc")
+
+# Visualize the distribution (count or actual values)
+autoplot(complexities, type = "count")
+
+# Visualize the frequency (percentages)
+autoplot(complexities, type = "percentage")
+}
+
    +
    +
    + +
    + + +
    +

    Developed by Austin G. McCoy, Zachary Noel, Adam H. Sparks, Martin Chilvers, Michigan Soybean Promotion Committee, Project GREEEN, North Central Soybean Research Program, GRDC Project DAQ00186.

    +
    + +
    +

    Site built with pkgdown 2.0.7.

    +
    + +
    + + + + + + + + diff --git a/reference/autoplot.hagis.gene.summary.html b/reference/autoplot.hagis.gene.summary.html new file mode 100644 index 00000000..f816a3c1 --- /dev/null +++ b/reference/autoplot.hagis.gene.summary.html @@ -0,0 +1,151 @@ + +Plot hagis Summary Objects — autoplot.hagis.gene.summary • hagis + + +
    +
    + + + +
    +
    +
    +

    Plot hagis Summary Objects

    + Source: R/summarize_gene.R + +
    + +
    +

    Creates a ggplot2 object of the gene summaries +calculated by summarize_gene()

    +
    + +
    + 
    # S3 method for hagis.gene.summary
+autoplot(object, type, color = NULL, order = NULL, ...)
    +
    + +
    +

    Arguments

    +
    object
    +

    a hagis.gene.summary object generated by summarize_gene(). +Character.

    + + +
    type
    +

    a vector of values for which the bar plot is desired. Specify +whether to return a graph of the percent pathogenic isolates, percentage, +or as the count, count. Character.

    + + +
    color
    +

    a named or hexadecimal color value to use for the bar color

    + + +
    order
    +

    sort the x-axis of the bar chart by ascending or descending +order of N_virulent_isolates or percent_pathogenic. Accepts ascending +or descending input values. Defaults to gene name. Character.

    + + +
    ...
    +

    passed to the chosen geom(s)

    + +
    +
    +

    Value

    + + +

    A ggplot2 plot

    +
    + +
    +

    Examples

    + 
    if (FALSE) { # interactive()
+# Using the built-in data set, `P_sojae_survey`
+data(P_sojae_survey)
+
+# calculate susceptibilities with a 60 % cutoff value
+susc <- summarize_gene(x = P_sojae_survey,
+                       cutoff = 60,
+                       control = "susceptible",
+                       sample = "Isolate",
+                       gene = "Rps",
+                       perc_susc = "perc.susc")
+
+# Visualize the summary of genes
+autoplot(susc, type = "percentage")
+}
+
    +
    +
    + +
    + + +
    +

    Developed by Austin G. McCoy, Zachary Noel, Adam H. Sparks, Martin Chilvers, Michigan Soybean Promotion Committee, Project GREEEN, North Central Soybean Research Program, GRDC Project DAQ00186.

    +
    + +
    +

    Site built with pkgdown 2.0.7.

    +
    + +
    + + + + + + + + diff --git a/reference/calculate_complexities.html b/reference/calculate_complexities.html new file mode 100644 index 00000000..aa44b374 --- /dev/null +++ b/reference/calculate_complexities.html @@ -0,0 +1,166 @@ + +Calculate Distribution of Complexities by Sample — calculate_complexities • hagis + + +
    +
    + + + +
    +
    +
    +

    Calculate Distribution of Complexities by Sample

    + Source: R/calculate_complexities.R + +
    + +
    +

    Calculate the distribution of susceptibilities by sample id.

    +
    + +
    + 
    calculate_complexities(x, cutoff, control, sample, gene, perc_susc)
    +
    + +
    +

    Arguments

    +
    x
    +

    a data.frame containing the data.

    + + +
    cutoff
    +

    value for percent susceptible cutoff. Numeric.

    + + +
    control
    +

    value used to denote the susceptible control in the gene +column. Character.

    + + +
    sample
    +

    column providing the unique identification for each sample +being tested. Character.

    + + +
    gene
    +

    column providing the gene(s) being tested. Character.

    + + +
    perc_susc
    +

    column providing the percent susceptible reactions. +Character.

    + +
    +
    +

    Value

    + + +

    calculate_complexities returns an object of class +hagis.complexities.

    + + +

    An object of class hagis.complexities is a list containing the following +components

    grouped_complexities
    +

    a data.table::data.table() object of +grouped complexities

    + +
    individual_complexities
    +

    a data.table::data.table() object of +individual complexities

    + + +
    + +
    +

    Examples

    + 
    if (FALSE) { # interactive()
+
+# Using the built-in data set, `P_sojae_survey`
+data(P_sojae_survey)
+
+P_sojae_survey
+
+# calculate susceptibilities with a 60 % cutoff value
+complexities <- calculate_complexities(x = P_sojae_survey,
+                                       cutoff = 60,
+                                       control = "susceptible",
+                                       sample = "Isolate",
+                                       gene = "Rps",
+                                       perc_susc = "perc.susc")
+complexities
+
+summary(complexities)
+}
+
    +
    +
    + +
    + + +
    +

    Developed by Austin G. McCoy, Zachary Noel, Adam H. Sparks, Martin Chilvers, Michigan Soybean Promotion Committee, Project GREEEN, North Central Soybean Research Program, GRDC Project DAQ00186.

    +
    + +
    +

    Site built with pkgdown 2.0.7.

    +
    + +
    + + + + + + + + diff --git a/reference/calculate_diversities.html b/reference/calculate_diversities.html new file mode 100644 index 00000000..c09ede57 --- /dev/null +++ b/reference/calculate_diversities.html @@ -0,0 +1,210 @@ + +Calculate Diversities Indices — calculate_diversities • hagis + + +
    +
    + + + +
    +
    +
    +

    Calculate Diversities Indices

    + Source: R/calculate_diversities.R + +
    + +
    +

    Calculate five pathogen diversity indices.

    +

    Diversity indices include:

    • Simple diversity index, which will show the proportion of unique pathotypes +to total samples. As the values gets closer to 1, there is greater +diversity in pathoypes within the population. Simple diversity is +calculated as: +$$ D = \frac{Np}{Ns} $$ +where \(Np\) is the number of pathotypes and \(Ns\) is the number of +samples.

      • +

    • Gleason diversity index, an alternate version of Simple diversity index, is +less sensitive to sample size than the Simple index. +$$ D = \frac{ (Np - 1) }{ log(Ns)}$$ +Where \(Np\) is the number of pathotypes and \(Ns\) is the number of +samples.

      • +

    • Shannon diversity index is typically between 1.5 and 3.5, as richness and +evenness of the population increase, so does the Shannon index value. +$$ D = -\sum_{i = 1}^{R} p_i \log p_i $$ Where +\(p_i\) is the proportional abundance of species \(i\).

      • +

    • Simpson diversity index values range from 0 to 1, 1 represents high +diversity and 0 represents no diversity. Where diversity is calculated as: +$$ D = \sum_{i = 1}^{R} p_i^2 $$

      • +

    • Evenness ranges from 0 to 1, as the Evenness value approaches 1, there is a +more even distribution of each pathoype's frequency within the population. +Where Evenness is calculated as: +$$ D = \frac{H'}{log(Np) }$$ +where \(H'\) is the Shannon diversity index and \(Np\) is the number +of pathotypes.

      • +
    + +
    + 
    calculate_diversities(x, cutoff, control, sample, gene, perc_susc)
    +
    + +
    +

    Arguments

    +
    x
    +

    a data.frame containing the data.

    + + +
    cutoff
    +

    value for percent susceptible cutoff. Numeric.

    + + +
    control
    +

    value used to denote the susceptible control in the gene +column. Character.

    + + +
    sample
    +

    column providing the unique identification for each sample +being tested. Character.

    + + +
    gene
    +

    column providing the gene(s) being tested. Character.

    + + +
    perc_susc
    +

    column providing the percent susceptible reactions. +Character.

    + +
    +
    +

    Value

    + + +

    hagis.diversities object containing

    • Number of Samples

      • +

    • Number of Pathotypes

      • +

    • Simple Diversity Index

      • +

    • Gleason Diversity Index

      • +

    • Shannon Diversity Index

      • +

    • Simpson Diversity Index

      • +

    • Evenness Diversity Index

      • +
    + +
    +

    Examples

    + 
    if (FALSE) { # interactive()
+# Using the built-in data set, P_sojae_survey
+data(P_sojae_survey)
+
+P_sojae_survey
+
+# calculate susceptibilities with a 60 % cutoff value
+diversities <- calculate_diversities(x = P_sojae_survey,
+                                     cutoff = 60,
+                                     control = "susceptible",
+                                     sample = "Isolate",
+                                     gene = "Rps",
+                                     perc_susc = "perc.susc")
+
+diversities
+}
+
    +
    +
    + +
    + + +
    +

    Developed by Austin G. McCoy, Zachary Noel, Adam H. Sparks, Martin Chilvers, Michigan Soybean Promotion Committee, Project GREEEN, North Central Soybean Research Program, GRDC Project DAQ00186.

    +
    + +
    +

    Site built with pkgdown 2.0.7.

    +
    + +
    + + + + + + + + diff --git a/reference/create_binary_matrix.html b/reference/create_binary_matrix.html new file mode 100644 index 00000000..4ceca0d6 --- /dev/null +++ b/reference/create_binary_matrix.html @@ -0,0 +1,158 @@ + +Create Binary Data Matrix From Pathotype Data — create_binary_matrix • hagis + + +
    +
    + + + +
    +
    +
    +

    Create Binary Data Matrix From Pathotype Data

    + Source: R/create_binary_matrix.R + +
    + +
    +

    Creates a binary data matrix from pathotype data representing +the pathotype of each isolate. This binary data matrix can be used to +visualize beta-diversity of pathotypes using vegan and +ape.

    +
    + +
    + 
    create_binary_matrix(x, cutoff, control, sample, gene, perc_susc)
    +
    + +
    +

    Arguments

    +
    x
    +

    a data.frame containing the data.

    + + +
    cutoff
    +

    value for percent susceptible cutoff. Numeric.

    + + +
    control
    +

    value used to denote the susceptible control in the gene +column. Character.

    + + +
    sample
    +

    column providing the unique identification for each sample +being tested. Character.

    + + +
    gene
    +

    column providing the gene(s) being tested. Character.

    + + +
    perc_susc
    +

    column providing the percent susceptible reactions. +Character.

    + +
    +
    +

    Value

    + + +

    create_binary_matrix returns a binary matrix of pathotype data as +a matrix object

    +
    + +
    +

    Examples

    + 
    if (FALSE) { # interactive()
+
+# Using the built-in data set, `P_sojae_survey`
+data(P_sojae_survey)
+
+P_sojae_survey
+
+# calculate susceptibilities with a 60 % cutoff value
+final_matrix <- create_binary_matrix(x = P_sojae_survey,
+                                    cutoff = 60,
+                                    control = "susceptible",
+                                    sample = "Isolate",
+                                    gene = "Rps",
+                                    perc_susc = "perc.susc")
+final_matrix
+}
+
    +
    +
    + +
    + + +
    +

    Developed by Austin G. McCoy, Zachary Noel, Adam H. Sparks, Martin Chilvers, Michigan Soybean Promotion Committee, Project GREEEN, North Central Soybean Research Program, GRDC Project DAQ00186.

    +
    + +
    +

    Site built with pkgdown 2.0.7.

    +
    + +
    + + + + + + + + diff --git a/reference/diversities_table.html b/reference/diversities_table.html new file mode 100644 index 00000000..fa9ac5fb --- /dev/null +++ b/reference/diversities_table.html @@ -0,0 +1,141 @@ + +Custom Print for hagis Diversities Tables — diversities_table • hagis + + +
    +
    + + + +
    +
    +
    +

    Custom Print for hagis Diversities Tables

    + Source: R/calculate_diversities.R + +
    + +
    +

    Print the frequency table of diversities from a hagis.diversities object +The resulting object is a pander table (a text object for Markdown) for +ease of use in reporting and viewing in the console.

    +
    + +
    + 
    diversities_table(x, ...)
    +
    + +
    +

    Arguments

    +
    x
    +

    a hagis.diversities object generated by calculate_diversities()

    + + +
    ...
    +

    other arguments passed to pander::panderOptions()

    + +
    +
    +

    Value

    + + +

    A pander object of diversities

    +
    + + +
    +

    Examples

    + 
    if (FALSE) { # interactive()
+# Using the built-in data set, P_sojae_survey
+data(P_sojae_survey)
+
+P_sojae_survey
+
+# calculate susceptibilities with a 60 % cutoff value
+diversities <- calculate_diversities(x = P_sojae_survey,
+                                     cutoff = 60,
+                                     control = "susceptible",
+                                     sample = "Isolate",
+                                     gene = "Rps",
+                                     perc_susc = "perc.susc")
+
+# print the diversities table
+diversities_table(diversities)
+}
+
    +
    +
    + +
    + + +
    +

    Developed by Austin G. McCoy, Zachary Noel, Adam H. Sparks, Martin Chilvers, Michigan Soybean Promotion Committee, Project GREEEN, North Central Soybean Research Program, GRDC Project DAQ00186.

    +
    + +
    +

    Site built with pkgdown 2.0.7.

    +
    + +
    + + + + + + + + diff --git a/reference/hagis-package.html b/reference/hagis-package.html new file mode 100644 index 00000000..ccbeee8c --- /dev/null +++ b/reference/hagis-package.html @@ -0,0 +1,114 @@ + +hagis: Analysis of Plant Pathogen Pathotype Complexities, Distributions and Diversity — hagis-package • hagis + + +
    +
    + + + +
    +
    +
    +

    hagis: Analysis of Plant Pathogen Pathotype Complexities, Distributions and Diversity

    + Source: R/hagis-package.R + +
    + +
    +

    Analysis of plant pathogen pathotype survey data. Functions provided calculate distribution of susceptibilities, distribution of complexities with statistics, pathotype frequency distribution, as well as diversity indices for pathotypes. This package is meant to be a direct replacement for Herrmann, Löwer and Schachtel's (1999) doi:10.1046/j.1365-3059.1999.00325.x + Habgood-Gilmour Spreadsheet, 'HaGiS', previously used for pathotype analysis.

    +
    + + + +
    +

    Author

    +

    Maintainer: Adam H. Sparks adamhsparks@gmail.com (ORCID)

    +

    Authors:

    Other contributors:

    • Zhian N. Kamvar (ORCID) [contributor, reviewer]

      • +

    • Michigan Soybean Promotion Committee [funder]

      • +

    • Project GREEEN [funder]

      • +

    • North Central Soybean Research Program [funder]

      • +

    • GRDC Project DAQ00186 [funder]

      • +
    + +
    + +
    + + +
    +

    Developed by Austin G. McCoy, Zachary Noel, Adam H. Sparks, Martin Chilvers, Michigan Soybean Promotion Committee, Project GREEEN, North Central Soybean Research Program, GRDC Project DAQ00186.

    +
    + +
    +

    Site built with pkgdown 2.0.7.

    +
    + +
    + + + + + + + + diff --git a/reference/index.html b/reference/index.html new file mode 100644 index 00000000..ce9f3241 --- /dev/null +++ b/reference/index.html @@ -0,0 +1,130 @@ + +Function reference • hagis + + +
    +
    + + + +
    +
    +
    +

    Reference

    +
    + + + + + + + + + + + + + + + + + + + + + + +
    +

    All functions

    +

    +
    +

    P_sojae_survey

    +

    Phytophthora sojae Survey Example Data

    +

    autoplot(<hagis.complexities>)

    +

    Plot hagis Complexities Objects

    +

    autoplot(<hagis.gene.summary>)

    +

    Plot hagis Summary Objects

    +

    calculate_complexities()

    +

    Calculate Distribution of Complexities by Sample

    +

    calculate_diversities()

    +

    Calculate Diversities Indices

    +

    create_binary_matrix()

    +

    Create Binary Data Matrix From Pathotype Data

    +

    diversities_table()

    +

    Custom Print for hagis Diversities Tables

    +

    individual_pathotypes()

    +

    Prints Individual Pathotypes for Each Sample

    +

    sample_meta

    +

    Phytophthora sojae Survey Example Metadata

    +

    summarize_gene()

    +

    Calculate and Summarize Distribution of Susceptibilities by Gene

    + + +
    + + +
    +

    Developed by Austin G. McCoy, Zachary Noel, Adam H. Sparks, Martin Chilvers, Michigan Soybean Promotion Committee, Project GREEEN, North Central Soybean Research Program, GRDC Project DAQ00186.

    +
    + +
    +

    Site built with pkgdown 2.0.7.

    +
    + +
    + + + + + + + + diff --git a/reference/individual_pathotypes.html b/reference/individual_pathotypes.html new file mode 100644 index 00000000..2e226544 --- /dev/null +++ b/reference/individual_pathotypes.html @@ -0,0 +1,143 @@ + +Prints Individual Pathotypes for Each Sample — individual_pathotypes • hagis + + +
    +
    + + + +
    +
    +
    +

    Prints Individual Pathotypes for Each Sample

    + Source: R/calculate_diversities.R + +
    + +
    +

    Print an object from a hagis.diversities object with individual pathotypes, +i.e. each sample's pathotype. The resulting object is a pander table +(a text object for Markdown) for ease of use in reporting and viewing in the +console.

    +
    + +
    + 
    individual_pathotypes(x, ...)
    +
    + +
    +

    Arguments

    +
    x
    +

    a hagis.diversities object generated by calculate_diversities()

    + + +
    ...
    +

    other arguments passed to pander::panderOptions()

    + +
    +
    +

    Value

    + + +

    A pander object of individual pathotypes

    +
    + + +
    +

    Examples

    + 
    if (FALSE) { # interactive()
+# Using the built-in data set, P_sojae_survey
+data(P_sojae_survey)
+
+P_sojae_survey
+
+# calculate susceptibilities with a 60 % cutoff value
+diversities <- calculate_diversities(x = P_sojae_survey,
+                                     cutoff = 60,
+                                     control = "susceptible",
+                                     sample = "Isolate",
+                                     gene = "Rps",
+                                     perc_susc = "perc.susc")
+
+# print the diversities table
+individual_pathotypes(diversities)
+}
+
    +
    +
    + +
    + + +
    +

    Developed by Austin G. McCoy, Zachary Noel, Adam H. Sparks, Martin Chilvers, Michigan Soybean Promotion Committee, Project GREEEN, North Central Soybean Research Program, GRDC Project DAQ00186.

    +
    + +
    +

    Site built with pkgdown 2.0.7.

    +
    + +
    + + + + + + + + diff --git a/reference/reexports.html b/reference/reexports.html new file mode 100644 index 00000000..faa83581 --- /dev/null +++ b/reference/reexports.html @@ -0,0 +1,105 @@ + +Objects exported from other packages — reexports • hagis + + +
    +
    + + + +
    +
    +
    +

    Objects exported from other packages

    + Source: R/summarize_gene.R + +
    + +
    +

    These objects are imported from other packages. Follow the links +below to see their documentation.

    +
    ggplot2
    +

    autoplot

    + + +
    + + + +
    + +
    + + +
    +

    Developed by Austin G. McCoy, Zachary Noel, Adam H. Sparks, Martin Chilvers, Michigan Soybean Promotion Committee, Project GREEEN, North Central Soybean Research Program, GRDC Project DAQ00186.

    +
    + +
    +

    Site built with pkgdown 2.0.7.

    +
    + +
    + + + + + + + + diff --git a/reference/sample_meta.html b/reference/sample_meta.html new file mode 100644 index 00000000..3a0bb55b --- /dev/null +++ b/reference/sample_meta.html @@ -0,0 +1,121 @@ + +Phytophthora sojae Survey Example Metadata — sample_meta • hagis + + +
    +
    + + + +
    +
    +
    +

    Phytophthora sojae Survey Example Metadata

    + Source: R/data.R + +
    + +
    +

    Metadata to accompany internal data from a Phytophthora sojae survey used +for an example in the "Beta-diversity Analyses" vignette.

    +
    + +
    + 
    data(sample_meta)
    +
    + +
    +

    Format

    +

    An object of class data.frame with 21 observations of 2 variables

    Sample
    +

    P. sojae sample identifier

    + +
    Locale
    +

    US State where sample was collected, limited to Michigan

    + + +
    +
    +

    Source

    +

    Data from an ongoing 2017 Phytophthora sojae survey in Michigan, +conducted by A. G. McCoy et al..

    +
    + +
    +

    Examples

    + 
    if (FALSE) { # interactive()
+data(sample_meta)
+sample_meta
+}
+
    +
    +
    + +
    + + +
    +

    Developed by Austin G. McCoy, Zachary Noel, Adam H. Sparks, Martin Chilvers, Michigan Soybean Promotion Committee, Project GREEEN, North Central Soybean Research Program, GRDC Project DAQ00186.

    +
    + +
    +

    Site built with pkgdown 2.0.7.

    +
    + +
    + + + + + + + + diff --git a/reference/summarize_gene.html b/reference/summarize_gene.html new file mode 100644 index 00000000..a89238b9 --- /dev/null +++ b/reference/summarize_gene.html @@ -0,0 +1,170 @@ + +Calculate and Summarize Distribution of Susceptibilities by Gene — summarize_gene • hagis + + +
    +
    + + + +
    +
    +
    +

    Calculate and Summarize Distribution of Susceptibilities by Gene

    + Source: R/summarize_gene.R + +
    + +
    +

    Calculate the distribution of susceptibilities by gene.

    +
    + +
    + 
    summarize_gene(x, cutoff, control, sample, gene, perc_susc)
    +
    + +
    +

    Arguments

    +
    x
    +

    a data.frame containing the data.

    + + +
    cutoff
    +

    value for percent susceptible cutoff. Numeric.

    + + +
    control
    +

    value used to denote the susceptible control in the gene +column. Character.

    + + +
    sample
    +

    column providing the unique identification for each sample +being tested. Character.

    + + +
    gene
    +

    column providing the gene(s) being tested. Character.

    + + +
    perc_susc
    +

    column providing the percent susceptible reactions. +Character.

    + +
    +
    +

    Value

    + + +

    returns an object of class

    +

    +

    hagis.gene.summary

    + + +

    An object of class hagis.gene.summary is a

    +

    +

    data.table::data.table()

    + + +

    containing the following components columns

    gene
    +

    the gene

    + +
    N_virulent_isolates
    +

    the total number virulent isolates for a given +gene in the gene column

    + +
    percent_pathogenic
    +

    the frequency with which a gene is pathogenic

    + + +
    + +
    +

    Examples

    + 
    if (FALSE) { # interactive()
+# Using the built-in data set, `P_sojae_survey`
+data(P_sojae_survey)
+
+P_sojae_survey
+
+# calculate susceptibilities with a 60 % cutoff value
+susc <- summarize_gene(x = P_sojae_survey,
+                       cutoff = 60,
+                       control = "susceptible",
+                       sample = "Isolate",
+                       gene = "Rps",
+                       perc_susc = "perc.susc")
+susc
+}
+
    +
    +
    + +
    + + +
    +

    Developed by Austin G. McCoy, Zachary Noel, Adam H. Sparks, Martin Chilvers, Michigan Soybean Promotion Committee, Project GREEEN, North Central Soybean Research Program, GRDC Project DAQ00186.

    +
    + +
    +

    Site built with pkgdown 2.0.7.

    +
    + +
    + + + + + + + + diff --git a/sitemap.xml b/sitemap.xml new file mode 100644 index 00000000..3f8c13b7 --- /dev/null +++ b/sitemap.xml @@ -0,0 +1,81 @@ + + + + /404.html + + + /CODE_OF_CONDUCT.html + + + /CONTRIBUTING.html + + + /LICENSE-text.html + + + /LICENSE.html + + + /articles/betadiversity.html + + + /articles/hagis.html + + + /articles/index.html + + + /authors.html + + + /index.html + + + /issue_template.html + + + /news/index.html + + + /pull_request_template.html + + + /reference/P_sojae_survey.html + + + /reference/autoplot.hagis.complexities.html + + + /reference/autoplot.hagis.gene.summary.html + + + /reference/calculate_complexities.html + + + /reference/calculate_diversities.html + + + /reference/create_binary_matrix.html + + + /reference/diversities_table.html + + + /reference/hagis-package.html + + + /reference/index.html + + + /reference/individual_pathotypes.html + + + /reference/reexports.html + + + /reference/sample_meta.html + + + /reference/summarize_gene.html + +