Microbiome selection
Jigyasa Arora and Sasha Mikheyev
2/16/2018
Phenotypic analysis
generating the correct format for all the fly eclosion time files-
library(knitr)
library(kableExtra)
library(gridExtra)
library(scales)
library(RColorBrewer)
library(tidyverse) #dplyr package included
library(stringr)
library(lme4)
library(nlme)
library(effects)
library(DESeq2)
library(qiime2R)
library(phyloseq)
library(DivNet)
library(vegan)
library(sjPlot) # mixed effects table summary
theme_set(theme_minimal() + theme(legend.position = "bottom", axis.title = element_text(size = 20), axis.text = element_text(size = 10), strip.text = element_text(size = 20)))flydata <- read_csv("data/flydata.csv") %>% mutate(id = paste(diet, selection, round, line, vial), time = emergence_time, replicate = paste(diet, selection, round, line))## Parsed with column specification:
## cols(
## emergence_time = col_double(),
## round = col_double(),
## diet = col_character(),
## selection = col_character(),
## line = col_double(),
## vial = col_character()
## )Mixed model analysis
flydataFactors <- flydata %>% mutate(day = ifelse(time<225, 1, ifelse(time<240, 2, ifelse(time<270, 3, 4)))) %>% as.data.frame()
flydataFactors$selection <- factor(flydataFactors$selection)
flydataFactors$diet <- factor(flydataFactors$diet)
mod <- lmer(time ~ round * diet * selection + (1|line:replicate), data = flydata )
hist(resid(mod))
tab_model(mod, show.icc = FALSE, show.re.var = FALSE, pred.labels = c("Intercept (High-sugar diet, No-selection control)", "Selection cycle", "No-sugar diet", "Selection treatment", "Selection cycle × No-sugar diet", "Selection cycle × Selection treatment", "No-sugar diet × Selection treatment", "Selection cycle × No-sugar diet× Selection treatment"))|  | time | ||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| Intercept (High-sugar diet, No-selection control) | 292.96 | 288.36 – 297.57 | <0.001 |
| Selection cycle | -2.59 | -3.95 – -1.23 | <0.001 |
| No-sugar diet | -50.00 | -56.29 – -43.70 | <0.001 |
| Selection treatment | 3.58 | -3.21 – 10.37 | 0.302 |
| Selection cycle × No-sugar diet | 0.81 | -1.06 – 2.68 | 0.397 |
| Selection cycle × Selection treatment | -0.85 | -2.83 – 1.14 | 0.402 |
| No-sugar diet × Selection treatment | -1.52 | -10.57 – 7.53 | 0.742 |
| Selection cycle × No-sugar diet× Selection treatment | 0.65 | -2.03 – 3.33 | 0.634 |
| N line | 10 | ||
| N replicate | 199 | ||
| Observations | 10850 | ||
| Marginal R2 / Conditional R2 | 0.779 / 0.834 | ||
effect(term="round:selection", xlevels=list(selection=c("selection","noselection")), mod=mod) %>% as.data.frame() %>% ggplot(aes(round,fit,color=selection))+geom_line()## NOTE: round:selection is not a high-order term in the model
effect(term="round:diet", xlevels=list(diet=c("control","nsd")), mod=mod) %>% as.data.frame() %>% ggplot(aes(round,fit,color=diet))+geom_line()## NOTE: round:diet is not a high-order term in the model
effect(term="round:diet:selection", xlevels=list(selection=c("selection","noselection"),diet=c("control","nsd")), mod=mod) %>% as.data.frame()%>%
ggplot(aes(round,fit,color=selection,linetype=diet))+geom_line()
diet_labeller <- function(variable, value) {
diet_names <- list('nsd'='No-sugar diet', 'hsd'='High-sugar diet')
selection_names <- list('selection'='Selection', 'noselection'='No Selection')
if (variable == 'selection')
return(selection_names[value])
else if (variable == 'diet')
return(diet_names[value])
else
return(as.character(value))
}
effect(term="round:diet:selection", xlevels=list(selection=c("selection","noselection"),diet=c("control","nsd")), mod=mod) %>%
as.data.frame() %>%
left_join(flydata, by = c("round", "diet", "selection")) %>%
ggplot(aes(round-1, fit, color=selection)) + geom_boxplot(aes(round-1, time, group = interaction(round,selection)), alpha = 0.1) +
geom_line() + geom_ribbon(aes(ymin=lower,ymax=upper, linetype = NA), alpha=0.3) +
facet_grid(diet~., scales="free", labeller=diet_labeller) +
scale_color_manual( labels = c("Control", "Selection"), values = c("dodgerblue", "orange")) + ylab("Eclosion time (hours)") + xlab("Selection cycle") + guides(alpha = F, color=guide_legend(override.aes=list(fill=NA)), legend.title=element_blank())## Warning: Column `diet` joining factor and character vector, coercing into
## character vector## Warning: Column `selection` joining factor and character vector, coercing
## into character vector## Warning: The labeller API has been updated. Labellers taking `variable`and
## `value` arguments are now deprecated. See labellers documentation.
effect(xlevels=list(round=c(1,5)), term="round:diet", mod=mod) %>% as.data.frame()## NOTE: round:diet is not a high-order term in the model## round diet fit se lower upper
## 1 1 hsd 291.7926 1.287873 289.2682 294.3171
## 2 5 hsd 279.6747 1.165651 277.3898 281.9595
## 3 1 nsd 242.1527 1.126594 239.9444 244.3610
## 4 5 nsd 234.6194 1.120892 232.4222 236.8165ggsave("figures/modeFit.pdf", height=7, width =5)
ggplot(flydataFactors, aes(day, fill=selection))+geom_histogram(stat="count", position="dodge") +facet_grid(.~diet) ## Warning: Ignoring unknown parameters: binwidth, bins, pad
tradeoff <- flydata %>% group_by(diet, round, selection, line, replicate) %>% summarize(time = mean(time), flies = n())
tradeoff %>% filter(round == 5) %>% ggplot(aes(time, flies, color = selection, shape = as.factor(round))) + geom_point() + facet_grid(.~diet, scales = "free") + stat_smooth(method="lm", aes(group = 1)) 
Selection has no effect on fly emergence, though emergence time decreases throughout the experiment in any case.
In round 4 there are no flies eclosing in the fourth day. Maybe some problem? Does this affect results?
mod2 <- lme(time ~ round * diet * selection , random=~1|line/replicate, method="REML", data = flydata %>% filter(round != 4))
summary(mod2)## Linear mixed-effects model fit by REML
## Data: flydata %>% filter(round != 4)
## AIC BIC logLik
## 75772.98 75852.16 -37875.49
##
## Random effects:
## Formula: ~1 | line
## (Intercept)
## StdDev: 0.0183111
##
## Formula: ~1 | replicate %in% line
## (Intercept) Residual
## StdDev: 6.227346 10.92282
##
## Fixed effects: time ~ round * diet * selection
## Value Std.Error DF t-value
## (Intercept) 292.13191 2.358482 9727 123.86438
## round -1.75222 0.728942 142 -2.40379
## dietnsd -49.72051 3.223359 142 -15.42506
## selectionselection 3.51368 3.473564 142 1.01155
## round:dietnsd 0.51087 1.009733 142 0.50595
## round:selectionselection -0.80158 1.057964 142 -0.75766
## dietnsd:selectionselection -0.65678 4.630908 142 -0.14182
## round:dietnsd:selectionselection -0.18434 1.440149 142 -0.12800
## p-value
## (Intercept) 0.0000
## round 0.0175
## dietnsd 0.0000
## selectionselection 0.3135
## round:dietnsd 0.6137
## round:selectionselection 0.4499
## dietnsd:selectionselection 0.8874
## round:dietnsd:selectionselection 0.8983
## Correlation:
## (Intr) round ditnsd slctns rnd:dt rnd:sl
## round -0.895
## dietnsd -0.732 0.655
## selectionselection -0.679 0.608 0.497
## round:dietnsd 0.646 -0.722 -0.890 -0.439
## round:selectionselection 0.617 -0.689 -0.451 -0.901 0.497
## dietnsd:selectionselection 0.509 -0.456 -0.696 -0.750 0.620 0.676
## round:dietnsd:selectionselection -0.453 0.506 0.624 0.662 -0.701 -0.735
## dtnsd:
## round
## dietnsd
## selectionselection
## round:dietnsd
## round:selectionselection
## dietnsd:selectionselection
## round:dietnsd:selectionselection -0.892
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -5.12947950 -0.76200648 -0.09791744 0.81577919 4.72341589
##
## Number of Observations: 9886
## Number of Groups:
## line replicate %in% line
## 10 159The model is robust to dropping this round entirely
Microbial analysis
Load data manually from qiime2
otus <- read_qza("data/table.qza")
taxonomy <- read_qza("data/taxonomy.qza")
tree<-read_qza("data/rooted-tree.qza")
taxonomy<-read_qza("data/taxonomy.qza")
tax_table <- data.frame(taxon = as.character(taxonomy$data$Taxon)) %>% separate(taxon, c("Kingdom", "Phylum", "Class", "Order", "Family", "Genus", "Species"), sep = ";") %>% mutate_all(funs(gsub(".*__", "", .))) ## Warning: Expected 7 pieces. Additional pieces discarded in 73 rows [4, 9,
## 23, 40, 63, 70, 79, 83, 89, 93, 107, 108, 109, 111, 119, 120, 123, 126,
## 130, 133, ...].## Warning: Expected 7 pieces. Missing pieces filled with `NA` in 445 rows [1,
## 2, 3, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, ...].## Warning: funs() is soft deprecated as of dplyr 0.8.0
## Please use a list of either functions or lambdas:
##
## # Simple named list:
## list(mean = mean, median = median)
##
## # Auto named with `tibble::lst()`:
## tibble::lst(mean, median)
##
## # Using lambdas
## list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))
## This warning is displayed once per session.rownames(tax_table) <- taxonomy$data$Feature.ID
tax_table <- as.matrix(tax_table)
# loading metadata
metadata <- read.table("data/metadata.txt", sep='\t', header=T, row.names = 1, comment="")
# create a bunch of category columns for divnet and append phenotypic data
metadata <- metadata %>%
mutate( sample_names = rownames(metadata),
round = round - 1,
roundSelection = paste(round, selection),
roundSelectionDiet = paste(round, selection, diet),
id = paste(diet, selection, round, line, vial),
line = factor(line)) %>%
left_join(flydata %>% group_by(id) %>% summarize(time = mean(time)))
rownames(metadata) <- metadata$sample_names
# make final product
microbedat <- phyloseq(otu_table(otus$data, taxa_are_rows = T), phy_tree(tree$data), tax_table(tax_table), sample_data(metadata))Descriptive bar plots
These are not super-useful, but highlight the difference between diets
abundantClasses <- psmelt(microbedat) %>% group_by(Genus) %>% summarize(frac = n() / nrow(.)) %>% filter(frac > 0.01) %>% na.omit() %>% pull(Genus)
#https://personal.sron.nl/~pault/data/colourschemes.pdf
mycolsBright <- c( "Acetobacter" = "#4477AA", "Acinetobacter" = "#66CCEE", "Lactobacillus" = "#228833" , "Pseudomonas" = "#CCBB44", "Staphylococcus" = "#EE6677")
psmelt(microbedat) %>%filter(Genus %in% abundantClasses) %>% ggplot(aes_string(x = "round", y = "Abundance", fill = "Genus")) + geom_bar(stat = "identity", position = "fill") + theme(axis.text.x = element_text(angle = 0, hjust = 0), legend.text=element_text(size=rel(1)), legend.title=element_blank()) + scale_y_sqrt() + ylab(expression(sqrt("Fraction of community"))) + xlab("Selection cycle") + facet_grid(diet~selection, scales="free", labeller=diet_labeller) + scale_fill_manual(values = mycolsBright) 
ggsave("figures/barplot.pdf", height = 7, width = 7)Alpha diversity
divnet_all <- divnet(tax_glom(microbedat, taxrank = "Genus"), X = "roundSelectionDiet", ncores = 20)
divnet_summary <- divnet_all$shannon %>% summary %>% left_join(metadata) %>% group_by(roundSelectionDiet) %>% summarize(alpha = estimate[1], lower = lower[1], upper = upper[1], round = round[1], selection = selection[1], diet = diet[1]) divnet_summary %>% ggplot(aes(selection, alpha, color = selection)) + geom_point() + geom_linerange(aes(ymin = lower, ymax = upper)) + facet_grid(diet~round, scales = "free") + theme_minimal() + theme(axis.text.x=element_blank()) + xlab("Round of evolution") + guides(color = F)
ggsave("figures/divnet.pdf")## Saving 5 x 5 in imageAlpha diversity using a less sophisticated approach
(p <- plot_richness(microbedat, "round", measures = "Shannon", color = "selection") + facet_grid(diet~., scales = "free_y") + stat_smooth(method="lm") + scale_color_manual(name = "Selection", labels = c("Control", "Selection"), values = c("dodgerblue", "orange")) + ylab("Shannon diversity") + xlab("Selection cycle") + theme(legend.title=element_blank(), axis.text.x = element_text(angle = 0, hjust = 0)) )## Warning in estimate_richness(physeq, split = TRUE, measures = measures): The data you have provided does not have
## any singletons. This is highly suspicious. Results of richness
## estimates (for example) are probably unreliable, or wrong, if you have already
## trimmed low-abundance taxa from the data.
##
## We recommended that you find the un-trimmed data and retry.
tab_model(lm(value ~ diet*round, data = p$data), pred.labels = c("Intercept (High-sugar diet)", "No-sugar diet", "Selection cycle", "Selection cycle × No-sugar diet"))|  | value | ||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| Intercept (High-sugar diet) | 0.04 | -0.06 – 0.15 | 0.420 |
| No-sugar diet | 0.17 | 0.02 – 0.32 | 0.031 |
| Selection cycle | 0.02 | -0.02 – 0.06 | 0.353 |
| Selection cycle × No-sugar diet | 0.20 | 0.14 – 0.26 | <0.001 |
| Observations | 58 | ||
| R2 / R2 adjusted | 0.833 / 0.824 | ||
ggsave("figures/shannon.pdf", height = 6, width = 6)Examining microbial community differentiation as in the mixed model
d = UniFrac(tax_glom(microbedat, taxrank = "Genus"))
d.mds <- metaMDS(d, zerodist=ignore)## Run 0 stress 0.1661259
## Run 1 stress 0.1661293
## ... Procrustes: rmse 0.0004967718 max resid 0.00245224
## ... Similar to previous best
## Run 2 stress 0.1773121
## Run 3 stress 0.1662344
## ... Procrustes: rmse 0.00521738 max resid 0.02349514
## Run 4 stress 0.1775354
## Run 5 stress 0.1826861
## Run 6 stress 0.1957775
## Run 7 stress 0.177312
## Run 8 stress 0.1662322
## ... Procrustes: rmse 0.005109491 max resid 0.02377291
## Run 9 stress 0.1772646
## Run 10 stress 0.1661298
## ... Procrustes: rmse 0.003428709 max resid 0.02111954
## Run 11 stress 0.1890769
## Run 12 stress 0.1661294
## ... Procrustes: rmse 0.0009662116 max resid 0.002815028
## ... Similar to previous best
## Run 13 stress 0.1957786
## Run 14 stress 0.1662543
## ... Procrustes: rmse 0.003715019 max resid 0.02332357
## Run 15 stress 0.1661258
## ... New best solution
## ... Procrustes: rmse 0.0003046658 max resid 0.0009580474
## ... Similar to previous best
## Run 16 stress 0.1662541
## ... Procrustes: rmse 0.003707549 max resid 0.02341273
## Run 17 stress 0.1755086
## Run 18 stress 0.1661322
## ... Procrustes: rmse 0.003473376 max resid 0.02142207
## Run 19 stress 0.1750021
## Run 20 stress 0.1661263
## ... Procrustes: rmse 0.0004474407 max resid 0.001424323
## ... Similar to previous best
## *** Solution reachedscrs <- cbind(metadata, data.frame(MDS1 = d.mds$points[,1], MDS2 = d.mds$points[,2]))
cent <- cbind(metadata, data.frame(MDS1 = d.mds$points[,1], MDS2 = d.mds$points[,2])) %>% aggregate(cbind(MDS1, MDS2) ~ diet + round + selection, data = ., FUN = mean)
segs <- merge(scrs, setNames(cent, c('diet', 'round','selection' ,'oNMDS1','oNMDS2')),
by = c('diet', 'round', 'selection'), sort = FALSE)
ggplot(scrs, aes(x = MDS1, y = MDS2, shape = selection, color = factor(round))) +
facet_wrap(~diet) + scale_colour_brewer(palette = "YlOrRd") +
geom_segment(data = segs,
mapping = aes(xend = oNMDS1, yend = oNMDS2)) + # spiders
geom_point(data = cent, size = 3) + # centroids
geom_point() + # sample scores
coord_fixed() # same axis scaling
(microbes.adonis <- adonis2(d ~ diet * selection * round, as(sample_data(microbedat), "data.frame"), permutations = 10000, strata = line))## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 10000
##
## adonis2(formula = d ~ diet * selection * round, data = as(sample_data(microbedat), "data.frame"), permutations = 10000, strata = line)
## Df SumOfSqs R2 F Pr(>F)
## diet 1 2.0683 0.21845 16.6231 9.999e-05 ***
## selection 1 0.0594 0.00627 0.4771 0.8018
## round 1 0.2174 0.02296 1.7469 0.1220
## diet:selection 1 0.2006 0.02119 1.6121 0.1566
## diet:round 1 0.3981 0.04205 3.1994 0.0108 *
## selection:round 1 0.2119 0.02238 1.7029 0.1327
## diet:selection:round 1 0.0912 0.00963 0.7330 0.5859
## Residual 50 6.2213 0.65708
## Total 57 9.4682 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(betadisper(d, sample_data(microbedat)$diet))## Warning in betadisper(d, sample_data(microbedat)$diet): some squared
## distances are negative and changed to zero## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 1 0.20142 0.201416 6.0247 0.01724 *
## Residuals 56 1.87216 0.033431
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1plot(betadisper(d, sample_data(microbedat)$diet))## Warning in betadisper(d, sample_data(microbedat)$diet): some squared
## distances are negative and changed to zero
microbes.adonis## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 10000
##
## adonis2(formula = d ~ diet * selection * round, data = as(sample_data(microbedat), "data.frame"), permutations = 10000, strata = line)
## Df SumOfSqs R2 F Pr(>F)
## diet 1 2.0683 0.21845 16.6231 9.999e-05 ***
## selection 1 0.0594 0.00627 0.4771 0.8018
## round 1 0.2174 0.02296 1.7469 0.1220
## diet:selection 1 0.2006 0.02119 1.6121 0.1566
## diet:round 1 0.3981 0.04205 3.1994 0.0108 *
## selection:round 1 0.2119 0.02238 1.7029 0.1327
## diet:selection:round 1 0.0912 0.00963 0.7330 0.5859
## Residual 50 6.2213 0.65708
## Total 57 9.4682 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1There are dispersion differences between the diets, so let’s analyze them separately
HSD permutation test
microbedat.hsd <- tax_glom(subset_samples(microbedat, diet == "hsd"), taxrank = "Genus")
d.hsd = UniFrac(microbedat.hsd)
(hsd.adonis <- adonis2(d.hsd ~ selection * round, as(sample_data(microbedat.hsd), "data.frame"), permutations = 10000, strata = line))## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 10000
##
## adonis2(formula = d.hsd ~ selection * round, data = as(sample_data(microbedat.hsd), "data.frame"), permutations = 10000, strata = line)
## Df SumOfSqs R2 F Pr(>F)
## selection 1 0.1742 0.03838 1.0533 0.3919
## round 1 0.1023 0.02253 0.6184 0.7271
## selection:round 1 0.1275 0.02810 0.7711 0.5883
## Residual 25 4.1341 0.91099
## Total 28 4.5380 1.00000NSD permutation test
microbedat.nsd <- tax_glom(subset_samples(microbedat, diet == "nsd"), taxrank = "Genus")
d.nsd = UniFrac(microbedat.nsd)
(hsd.adonis <- adonis2(d.nsd ~ selection * round, as(sample_data(microbedat.nsd), "data.frame"), permutations = 10000, strata = line))## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 10000
##
## adonis2(formula = d.nsd ~ selection * round, data = as(sample_data(microbedat.nsd), "data.frame"), permutations = 10000, strata = line)
## Df SumOfSqs R2 F Pr(>F)
## selection 1 0.08577 0.02997 1.0274 0.388
## round 1 0.51320 0.17933 6.1469 9.999e-05 ***
## selection:round 1 0.17558 0.06135 2.1030 0.107
## Residual 25 2.08724 0.72935
## Total 28 2.86179 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(betadisper(d.nsd, sample_data(microbedat.nsd)$round))## Warning in betadisper(d.nsd, sample_data(microbedat.nsd)$round): some
## squared distances are negative and changed to zero## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 4 0.09364 0.023411 0.4804 0.7498
## Residuals 24 1.16944 0.048727Looks like there is evidence of community change in the low sugar diet over time, but no detectable change in the high sugar diet.
Changes in OTU abundance
As a function of fly emergence time
microbedat.phyloseq.time <- phyloseq_to_deseq2(subset_samples(microbedat, diet == "hsd" & time > 0), ~ time + line)## converting counts to integer modemicrobedat.phyloseq.time <- results(DESeq(microbedat.phyloseq.time, test="LRT", fitType="local", reduced = ~ line))## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testinghead(microbedat.phyloseq.time[order(microbedat.phyloseq.time$pvalue),])## log2 fold change (MLE): line 10 vs 1
## LRT p-value: '~ time + line' vs '~ line'
## DataFrame with 6 rows and 6 columns
## baseMean log2FoldChange
## <numeric> <numeric>
## 0bf86e80ab287770313005cfedc02aa6 24.7592917716998 0.792959035145591
## 2cb918624b64a0f450864feb5ef39a1c 0.878361477464068 3.21023370832391
## 7eb1df4a0a4af4d89aa9c5d5bf827444 0.0743967205000962 3.0540404867522
## 9b8f4530fffd78453dd5ecee8247fbc4 0.0686738958462426 2.99240537233919
## 3b4673bd2141d5ce0ac87fbee9a1c6ba 0.136370061541386 2.72699484726743
## 802547545e68bfb764fb7e3a66d71374 1.29543463551755 2.56150914514678
## lfcSE stat
## <numeric> <numeric>
## 0bf86e80ab287770313005cfedc02aa6 4.56651450273001 0.804998258767597
## 2cb918624b64a0f450864feb5ef39a1c 8.16099939918329 0.208639052557686
## 7eb1df4a0a4af4d89aa9c5d5bf827444 8.16244727825418 0.20626784308285
## 9b8f4530fffd78453dd5ecee8247fbc4 8.16250160489022 0.197380475545874
## 3b4673bd2141d5ce0ac87fbee9a1c6ba 8.16271817372288 0.187755912760666
## 802547545e68bfb764fb7e3a66d71374 8.16302714417741 0.173307847146628
## pvalue padj
## <numeric> <numeric>
## 0bf86e80ab287770313005cfedc02aa6 0.369603161261352 1
## 2cb918624b64a0f450864feb5ef39a1c 0.647836198117257 1
## 7eb1df4a0a4af4d89aa9c5d5bf827444 0.649708490881428 1
## 9b8f4530fffd78453dd5ecee8247fbc4 0.656843611044843 1
## 3b4673bd2141d5ce0ac87fbee9a1c6ba 0.66479095166259 1
## 802547545e68bfb764fb7e3a66d71374 0.677188596965612 1microbedat.phyloseq.time <- phyloseq_to_deseq2(subset_samples(microbedat, diet == "nsd" & time > 0), ~ time + line)## converting counts to integer modemicrobedat.phyloseq.time <- results(DESeq(microbedat.phyloseq.time, test="LRT", fitType="local", reduced = ~ line))## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testinghead(microbedat.phyloseq.time[order(microbedat.phyloseq.time$pvalue),])## log2 fold change (MLE): line 10 vs 1
## LRT p-value: '~ time + line' vs '~ line'
## DataFrame with 6 rows and 6 columns
## baseMean log2FoldChange
## <numeric> <numeric>
## 6012605abede608110aa60e12695d5a3 0.340575758609416 2.95370524067047
## ddf83c0b68613f341902790a082bc5a3 1.25916863505581 2.17093562227027
## b656988f920c0d400c660c5f337ffb67 0.319977917188704 -0.693104787863253
## db9f0d4fb97420260553ad52e56ef69b 2.66350343321965 -0.477341066862258
## 3411f0860e8e37277e8ff7d546337c3f 0.267604860112273 -1.15447903586402
## e299dc11f1ef328a611946fb19e9422d 0.161589109470717 -1.0949771255914
## lfcSE stat
## <numeric> <numeric>
## 6012605abede608110aa60e12695d5a3 7.04224473995456 0.162895345989963
## ddf83c0b68613f341902790a082bc5a3 6.86706648601626 0.152606934512079
## b656988f920c0d400c660c5f337ffb67 7.02904222212107 0.0883245434138615
## db9f0d4fb97420260553ad52e56ef69b 5.72863054592215 0.0716390156040063
## 3411f0860e8e37277e8ff7d546337c3f 7.02060399826492 0.0504821230376891
## e299dc11f1ef328a611946fb19e9422d 7.02207742932735 0.0503916576102981
## pvalue padj
## <numeric> <numeric>
## 6012605abede608110aa60e12695d5a3 0.686504711714834 1
## ddf83c0b68613f341902790a082bc5a3 0.696056423624934 1
## b656988f920c0d400c660c5f337ffb67 0.76631805586275 1
## db9f0d4fb97420260553ad52e56ef69b 0.788965154845989 1
## 3411f0860e8e37277e8ff7d546337c3f 0.822226457457341 1
## e299dc11f1ef328a611946fb19e9422d 0.822383156508776 1No individual OTU is associated with fly emergence tim
As a function of round
microbedat.phyloseq.round <- phyloseq_to_deseq2(tax_glom(subset_samples(microbedat, diet == "hsd"), taxrank = "Genus"), ~ round + line)## converting counts to integer mode## the design formula contains a numeric variable with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() functionmicrobedat.phyloseq.round <- results(DESeq(microbedat.phyloseq.round, test="LRT", fitType="local", reduced = ~ line))## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testinghead(microbedat.phyloseq.round[order(microbedat.phyloseq.round$pvalue),])## log2 fold change (MLE): line 10 vs 1
## LRT p-value: '~ round + line' vs '~ line'
## DataFrame with 6 rows and 6 columns
## baseMean log2FoldChange
## <numeric> <numeric>
## 3b4673bd2141d5ce0ac87fbee9a1c6ba 13.0118428681771 0.0177751212782488
## 5b3a8ffeed018d279c38505129abd5fd 3.74030565186215 3.31900341871001
## c5395c876efbfada63433d5be9c4e06b 2.76782618237799 3.31736835414907
## e1b2e90c23e8c2c49d7895f14e441f96 4.6691847512055 5.27096330350529
## b75a5884989d17f8424b7c08ee28f09c 3.76462953569669 -6.71570016400104
## 37d1cd8011e75115ae32119254132bca 66.2220264893501 -0.246551488586504
## lfcSE stat
## <numeric> <numeric>
## 3b4673bd2141d5ce0ac87fbee9a1c6ba 7.21607893072118 1.88433008785536
## 5b3a8ffeed018d279c38505129abd5fd 7.20334660758818 1.14124405928748
## c5395c876efbfada63433d5be9c4e06b 7.20349863849314 1.14103223005445
## e1b2e90c23e8c2c49d7895f14e441f96 7.18907304294161 0.904319672596593
## b75a5884989d17f8424b7c08ee28f09c 7.24272351866331 0.852913390973008
## 37d1cd8011e75115ae32119254132bca 2.14968286017655 0.837262057786575
## pvalue padj
## <numeric> <numeric>
## 3b4673bd2141d5ce0ac87fbee9a1c6ba 0.169842820067783 1
## 5b3a8ffeed018d279c38505129abd5fd 0.285389604953306 1
## c5395c876efbfada63433d5be9c4e06b 0.285434317751509 1
## e1b2e90c23e8c2c49d7895f14e441f96 0.341626082721452 1
## b75a5884989d17f8424b7c08ee28f09c 0.355729458028925 1
## 37d1cd8011e75115ae32119254132bca 0.360180999508781 1microbedat.phyloseq.round <- phyloseq_to_deseq2(tax_glom(subset_samples(microbedat, diet == "nsd"), taxrank = "Genus"), ~ round + line)## converting counts to integer mode## the design formula contains a numeric variable with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() functionmicrobedat.phyloseq.round <- results(DESeq(microbedat.phyloseq.round, test="LRT", fitType="local", reduced = ~ line))## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testing## 1 rows did not converge in beta, labelled in mcols(object)$fullBetaConv. Use larger maxit argument with nbinomLRThead(microbedat.phyloseq.round[order(microbedat.phyloseq.round$pvalue),])## log2 fold change (MLE): line 10 vs 1
## LRT p-value: '~ round + line' vs '~ line'
## DataFrame with 6 rows and 6 columns
## baseMean log2FoldChange
## <numeric> <numeric>
## 46d866c5c1bdd89200e1867baf5567db 55306.2023332879 3.21081126637371
## e481c86d25c16bda35869bb7b1232252 280.892914342214 2.25787012803927
## 82a590792bb28943e234eec7d37ffafe 37.3456168142284 -5.18037282148377
## fe201038a0eb3464a9a3afbc0b91f168 21.2933351542597 -18.619012334243
## 54f0d22e60ac26b58c06de3fb6d137c4 2624.19915647487 -0.795983967793166
## 3b4673bd2141d5ce0ac87fbee9a1c6ba 121.62356176636 -1.52138335944504
## lfcSE stat
## <numeric> <numeric>
## 46d866c5c1bdd89200e1867baf5567db 3.53312015752624 7.08814953364737
## e481c86d25c16bda35869bb7b1232252 4.80953892946386 6.1675163692334
## 82a590792bb28943e234eec7d37ffafe 7.05850480399858 2.46288128091848
## fe201038a0eb3464a9a3afbc0b91f168 7.15549084325466 1.78885355164293
## 54f0d22e60ac26b58c06de3fb6d137c4 3.16734401827821 1.33205066678266
## 3b4673bd2141d5ce0ac87fbee9a1c6ba 2.35489949552596 1.24766934998371
## pvalue padj
## <numeric> <numeric>
## 46d866c5c1bdd89200e1867baf5567db 0.00775953271076444 0.104093722897698
## e481c86d25c16bda35869bb7b1232252 0.0130117153622122 0.104093722897698
## 82a590792bb28943e234eec7d37ffafe 0.116564785919079 0.621678858235086
## fe201038a0eb3464a9a3afbc0b91f168 0.181065908653573 0.703994896226761
## 54f0d22e60ac26b58c06de3fb6d137c4 0.248440729475066 0.703994896226761
## 3b4673bd2141d5ce0ac87fbee9a1c6ba 0.263998086085035 0.703994896226761head(microbedat.phyloseq.round[order(microbedat.phyloseq.round$pvalue),])## log2 fold change (MLE): line 10 vs 1
## LRT p-value: '~ round + line' vs '~ line'
## DataFrame with 6 rows and 6 columns
## baseMean log2FoldChange
## <numeric> <numeric>
## 46d866c5c1bdd89200e1867baf5567db 55306.2023332879 3.21081126637371
## e481c86d25c16bda35869bb7b1232252 280.892914342214 2.25787012803927
## 82a590792bb28943e234eec7d37ffafe 37.3456168142284 -5.18037282148377
## fe201038a0eb3464a9a3afbc0b91f168 21.2933351542597 -18.619012334243
## 54f0d22e60ac26b58c06de3fb6d137c4 2624.19915647487 -0.795983967793166
## 3b4673bd2141d5ce0ac87fbee9a1c6ba 121.62356176636 -1.52138335944504
## lfcSE stat
## <numeric> <numeric>
## 46d866c5c1bdd89200e1867baf5567db 3.53312015752624 7.08814953364737
## e481c86d25c16bda35869bb7b1232252 4.80953892946386 6.1675163692334
## 82a590792bb28943e234eec7d37ffafe 7.05850480399858 2.46288128091848
## fe201038a0eb3464a9a3afbc0b91f168 7.15549084325466 1.78885355164293
## 54f0d22e60ac26b58c06de3fb6d137c4 3.16734401827821 1.33205066678266
## 3b4673bd2141d5ce0ac87fbee9a1c6ba 2.35489949552596 1.24766934998371
## pvalue padj
## <numeric> <numeric>
## 46d866c5c1bdd89200e1867baf5567db 0.00775953271076444 0.104093722897698
## e481c86d25c16bda35869bb7b1232252 0.0130117153622122 0.104093722897698
## 82a590792bb28943e234eec7d37ffafe 0.116564785919079 0.621678858235086
## fe201038a0eb3464a9a3afbc0b91f168 0.181065908653573 0.703994896226761
## 54f0d22e60ac26b58c06de3fb6d137c4 0.248440729475066 0.703994896226761
## 3b4673bd2141d5ce0ac87fbee9a1c6ba 0.263998086085035 0.703994896226761Estimating selection coefficients and heritability
Selection coefficient
all_means<-read.csv("column_level_means_allfiles.csv",header=TRUE)
average_means<-all_means%>%group_by(fullname,generation,line)%>%summarise_at(.vars = names(.)[2],funs(mean(., na.rm=TRUE)))
#adding the selected/choosen vial informtion-
choosen_means<-read.csv("meaneclosion_parentandoffspring_data_linear.csv",header=TRUE)
#joining the two files- #all.y=TRUE as we need to include the zero means
selection2<-merge(average_means,choosen_means,by.x=c("generation","fullname","line"),by.y=c("Generation","Diet","Line"),all.y=TRUE)
selection2[is.na(selection2)] <- 0
#selection-coefficient calculations-
selection_coefficient<-selection2%>%mutate(selection_coefficient=vial_mean/mean_of_each_column)
selection_coefficient$selection<-unlist(lapply(strsplit(as.character(selection_coefficient$fullname),split="_"),"[",2))
selection_coefficient$diet<-unlist(lapply(strsplit(as.character(selection_coefficient$fullname),split="_"),"[",1))
selected_only<-selection_coefficient%>%filter(selection=="selection")
selected_only<-selected_only%>%filter(!selection_coefficient==Inf)
#some summations-
selected_summation<-selected_only %>% group_by(diet,generation) %>% summarise_at(vars(mean_of_each_column,vial_mean), sum)
selected_summation$mean_of_each_column<-selected_summation$mean_of_each_column/10
selected_summation$vial_mean<-selected_summation$vial_mean/10
selected_summation<-selected_summation%>%mutate(selection_coefficient=vial_mean-mean_of_each_column)
(s<-mean(selected_summation$selection_coefficient)) #selection coefficient## [1] -6.833194sd(selected_summation$selection_coefficient)## [1] 11.4756‘Heritability’
This is the correlation between phenotypes in source and donor media
parent_offspring<-read.csv("meaneclosion_parentandoffspring_Data_FOandF1.csv",header=TRUE)
parent_offspring$media<-unlist(lapply(strsplit(as.character(parent_offspring$Diet),split="_"),"[",1))
parent_offspring$selection<-unlist(lapply(strsplit(as.character(parent_offspring$Diet),split="_"),"[",2))
noselected<-parent_offspring%>%filter(selection=="noselection")
noselected2<-noselected%>%filter(F0_parent_mean!=0)
noselected3<-noselected2%>%filter(F1_child_mean!=0)
x <- noselected3$F0_parent_mean
y <- noselected3$F1_child_mean
(h2 <-cor(x, y)^2) #heritability## [1] 0.8442553h2*s## [1] -5.76896Session info
## setting value
## version R version 3.5.3 (2019-03-11)
## os macOS Mojave 10.14.6
## system x86_64, darwin15.6.0
## ui X11
## language (EN)
## collate en_US.UTF-8
## ctype en_US.UTF-8
## tz Asia/Tokyo
## date 2019-10-03| package | ondiskversion | loadedversion | path | loadedpath | attached | is_base | date | source | md5ok | library | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| abind | abind | 1.4.5 | 1.4-5 | /Users/sasha/Library/R/3.5/library/abind | /Users/sasha/Library/R/3.5/library/abind | FALSE | FALSE | 2016-07-21 | CRAN (R 3.5.3) | NA | /Users/sasha/Library/R/3.5/library |
| acepack | acepack | 1.4.1 | 1.4.1 | /Users/sasha/Library/R/3.5/library/acepack | /Users/sasha/Library/R/3.5/library/acepack | FALSE | FALSE | 2016-10-29 | CRAN (R 3.5.0) | NA | /Users/sasha/Library/R/3.5/library |
| ade4 | ade4 | 1.7.13 | 1.7-13 | /Users/sasha/Library/R/3.5/library/ade4 | /Users/sasha/Library/R/3.5/library/ade4 | FALSE | FALSE | 2018-08-31 | CRAN (R 3.5.0) | NA | /Users/sasha/Library/R/3.5/library |
| annotate | annotate | 1.60.1 | 1.60.1 | /Users/sasha/Library/R/3.5/library/annotate | /Users/sasha/Library/R/3.5/library/annotate | FALSE | FALSE | 2019-03-07 | Bioconductor | NA | /Users/sasha/Library/R/3.5/library |
| AnnotationDbi | AnnotationDbi | 1.44.0 | 1.44.0 | /Users/sasha/Library/R/3.5/library/AnnotationDbi | /Users/sasha/Library/R/3.5/library/AnnotationDbi | FALSE | FALSE | 2018-10-30 | Bioconductor | NA | /Users/sasha/Library/R/3.5/library |
| ape | ape | 5.3 | 5.3 | /Users/sasha/Library/R/3.5/library/ape | /Users/sasha/Library/R/3.5/library/ape | FALSE | FALSE | 2019-03-17 | CRAN (R 3.5.2) | NA | /Users/sasha/Library/R/3.5/library |
| assertthat | assertthat | 0.2.1 | 0.2.1 | /Users/sasha/Library/R/3.5/library/assertthat | /Users/sasha/Library/R/3.5/library/assertthat | FALSE | FALSE | 2019-03-21 | CRAN (R 3.5.3) | NA | /Users/sasha/Library/R/3.5/library |
| backports | backports | 1.1.4 | 1.1.4 | /Users/sasha/Library/R/3.5/library/backports | /Users/sasha/Library/R/3.5/library/backports | FALSE | FALSE | 2019-04-10 | CRAN (R 3.5.2) | NA | /Users/sasha/Library/R/3.5/library |
| base64enc | base64enc | 0.1.3 | 0.1-3 | /Users/sasha/Library/R/3.5/library/base64enc | /Users/sasha/Library/R/3.5/library/base64enc | FALSE | FALSE | 2015-07-28 | CRAN (R 3.5.3) | NA | /Users/sasha/Library/R/3.5/library |
| bayestestR | bayestestR | 0.2.5 | 0.2.5 | /Users/sasha/Library/R/3.5/library/bayestestR | /Users/sasha/Library/R/3.5/library/bayestestR | FALSE | FALSE | 2019-08-06 | CRAN (R 3.5.2) | NA | /Users/sasha/Library/R/3.5/library |
| Biobase | Biobase | 2.42.0 | 2.42.0 | /Users/sasha/Library/R/3.5/library/Biobase | /Users/sasha/Library/R/3.5/library/Biobase | TRUE | FALSE | 2018-10-30 | Bioconductor | NA | /Users/sasha/Library/R/3.5/library |
| BiocGenerics | BiocGenerics | 0.28.0 | 0.28.0 | /Users/sasha/Library/R/3.5/library/BiocGenerics | /Users/sasha/Library/R/3.5/library/BiocGenerics | TRUE | FALSE | 2018-10-30 | Bioconductor | NA | /Users/sasha/Library/R/3.5/library |
| BiocParallel | BiocParallel | 1.16.6 | 1.16.6 | /Users/sasha/Library/R/3.5/library/BiocParallel | /Users/sasha/Library/R/3.5/library/BiocParallel | TRUE | FALSE | 2019-02-17 | Bioconductor | NA | /Users/sasha/Library/R/3.5/library |
| biomformat | biomformat | 1.10.1 | 1.10.1 | /Users/sasha/Library/R/3.5/library/biomformat | /Users/sasha/Library/R/3.5/library/biomformat | FALSE | FALSE | 2019-01-04 | Bioconductor | NA | /Users/sasha/Library/R/3.5/library |
| Biostrings | Biostrings | 2.50.2 | 2.50.2 | /Users/sasha/Library/R/3.5/library/Biostrings | /Users/sasha/Library/R/3.5/library/Biostrings | FALSE | FALSE | 2019-01-03 | Bioconductor | NA | /Users/sasha/Library/R/3.5/library |
| bit | bit | 1.1.14 | 1.1-14 | /Users/sasha/Library/R/3.5/library/bit | /Users/sasha/Library/R/3.5/library/bit | FALSE | FALSE | 2018-05-29 | CRAN (R 3.5.0) | NA | /Users/sasha/Library/R/3.5/library |
| bit64 | bit64 | 0.9.7 | 0.9-7 | /Users/sasha/Library/R/3.5/library/bit64 | /Users/sasha/Library/R/3.5/library/bit64 | FALSE | FALSE | 2017-05-08 | CRAN (R 3.5.0) | NA | /Users/sasha/Library/R/3.5/library |
| bitops | bitops | 1.0.6 | 1.0-6 | /Users/sasha/Library/R/3.5/library/bitops | /Users/sasha/Library/R/3.5/library/bitops | FALSE | FALSE | 2013-08-17 | CRAN (R 3.5.3) | NA | /Users/sasha/Library/R/3.5/library |
| blob | blob | 1.2.0 | 1.2.0 | /Users/sasha/Library/R/3.5/library/blob | /Users/sasha/Library/R/3.5/library/blob | FALSE | FALSE | 2019-07-09 | CRAN (R 3.5.2) | NA | /Users/sasha/Library/R/3.5/library |
| boot | boot | 1.3.23 | 1.3-23 | /Library/Frameworks/R.framework/Versions/3.5/Resources/library/boot | /Library/Frameworks/R.framework/Versions/3.5/Resources/library/boot | FALSE | FALSE | 2019-07-05 | CRAN (R 3.5.2) | NA | /Library/Frameworks/R.framework/Versions/3.5/Resources/library |
| breakaway | breakaway | 4.6.8 | 4.6.8 | /Users/sasha/Library/R/3.5/library/breakaway | /Users/sasha/Library/R/3.5/library/breakaway | TRUE | FALSE | 2019-06-10 | Github (adw96/breakaway@07268b0) | NA | /Users/sasha/Library/R/3.5/library |
| broom | broom | 0.5.2 | 0.5.2 | /Users/sasha/Library/R/3.5/library/broom | /Users/sasha/Library/R/3.5/library/broom | FALSE | FALSE | 2019-04-07 | CRAN (R 3.5.2) | NA | /Users/sasha/Library/R/3.5/library |
| callr | callr | 3.3.1 | 3.3.1 | /Users/sasha/Library/R/3.5/library/callr | /Users/sasha/Library/R/3.5/library/callr | FALSE | FALSE | 2019-07-18 | CRAN (R 3.5.2) | NA | /Users/sasha/Library/R/3.5/library |
| carData | carData | 3.0.2 | 3.0-2 | /Users/sasha/Library/R/3.5/library/carData | /Users/sasha/Library/R/3.5/library/carData | TRUE | FALSE | 2018-09-30 | CRAN (R 3.5.0) | NA | /Users/sasha/Library/R/3.5/library |
| cellranger | cellranger | 1.1.0 | 1.1.0 | /Users/sasha/Library/R/3.5/library/cellranger | /Users/sasha/Library/R/3.5/library/cellranger | FALSE | FALSE | 2016-07-27 | CRAN (R 3.5.3) | NA | /Users/sasha/Library/R/3.5/library |
| checkmate | checkmate | 1.9.4 | 1.9.4 | /Users/sasha/Library/R/3.5/library/checkmate | /Users/sasha/Library/R/3.5/library/checkmate | FALSE | FALSE | 2019-07-04 | CRAN (R 3.5.2) | NA | /Users/sasha/Library/R/3.5/library |
| cli | cli | 1.1.0 | 1.1.0 | /Users/sasha/Library/R/3.5/library/cli | /Users/sasha/Library/R/3.5/library/cli | FALSE | FALSE | 2019-03-19 | CRAN (R 3.5.3) | NA | /Users/sasha/Library/R/3.5/library |
| cluster | cluster | 2.1.0 | 2.1.0 | /Library/Frameworks/R.framework/Versions/3.5/Resources/library/cluster | /Library/Frameworks/R.framework/Versions/3.5/Resources/library/cluster | FALSE | FALSE | 2019-06-19 | CRAN (R 3.5.2) | NA | /Library/Frameworks/R.framework/Versions/3.5/Resources/library |
| coda | coda | 0.19.3 | 0.19-3 | /Users/sasha/Library/R/3.5/library/coda | /Users/sasha/Library/R/3.5/library/coda | FALSE | FALSE | 2019-07-05 | CRAN (R 3.5.2) | NA | /Users/sasha/Library/R/3.5/library |
| codetools | codetools | 0.2.16 | 0.2-16 | /Library/Frameworks/R.framework/Versions/3.5/Resources/library/codetools | /Library/Frameworks/R.framework/Versions/3.5/Resources/library/codetools | FALSE | FALSE | 2018-12-24 | CRAN (R 3.5.3) | NA | /Library/Frameworks/R.framework/Versions/3.5/Resources/library |
| colorspace | colorspace | 1.4.1 | 1.4-1 | /Users/sasha/Library/R/3.5/library/colorspace | /Users/sasha/Library/R/3.5/library/colorspace | FALSE | FALSE | 2019-03-18 | CRAN (R 3.5.3) | NA | /Users/sasha/Library/R/3.5/library |
| crayon | crayon | 1.3.4 | 1.3.4 | /Users/sasha/Library/R/3.5/library/crayon | /Users/sasha/Library/R/3.5/library/crayon | FALSE | FALSE | 2017-09-16 | CRAN (R 3.5.3) | NA | /Users/sasha/Library/R/3.5/library |
| data.table | data.table | 1.12.2 | 1.12.2 | /Users/sasha/Library/R/3.5/library/data.table | /Users/sasha/Library/R/3.5/library/data.table | FALSE | FALSE | 2019-04-07 | CRAN (R 3.5.2) | NA | /Users/sasha/Library/R/3.5/library |
| DBI | DBI | 1.0.0 | 1.0.0 | /Users/sasha/Library/R/3.5/library/DBI | /Users/sasha/Library/R/3.5/library/DBI | FALSE | FALSE | 2018-05-02 | CRAN (R 3.5.3) | NA | /Users/sasha/Library/R/3.5/library |
| DelayedArray | DelayedArray | 0.8.0 | 0.8.0 | /Users/sasha/Library/R/3.5/library/DelayedArray | /Users/sasha/Library/R/3.5/library/DelayedArray | TRUE | FALSE | 2018-10-30 | Bioconductor | NA | /Users/sasha/Library/R/3.5/library |
| desc | desc | 1.2.0 | 1.2.0 | /Users/sasha/Library/R/3.5/library/desc | /Users/sasha/Library/R/3.5/library/desc | FALSE | FALSE | 2018-05-01 | CRAN (R 3.5.3) | NA | /Users/sasha/Library/R/3.5/library |
| DESeq2 | DESeq2 | 1.22.2 | 1.22.2 | /Users/sasha/Library/R/3.5/library/DESeq2 | /Users/sasha/Library/R/3.5/library/DESeq2 | TRUE | FALSE | 2019-01-04 | Bioconductor | NA | /Users/sasha/Library/R/3.5/library |
| devtools | devtools | 2.1.0 | 2.1.0 | /Users/sasha/Library/R/3.5/library/devtools | /Users/sasha/Library/R/3.5/library/devtools | FALSE | FALSE | 2019-07-06 | CRAN (R 3.5.2) | NA | /Users/sasha/Library/R/3.5/library |
| digest | digest | 0.6.20 | 0.6.20 | /Users/sasha/Library/R/3.5/library/digest | /Users/sasha/Library/R/3.5/library/digest | FALSE | FALSE | 2019-07-04 | CRAN (R 3.5.2) | NA | /Users/sasha/Library/R/3.5/library |
| DivNet | DivNet | 0.3.1 | 0.3.1 | /Users/sasha/Library/R/3.5/library/DivNet | /Users/sasha/Library/R/3.5/library/DivNet | TRUE | FALSE | 2019-06-10 | Github (adw96/DivNet@1c17836) | NA | /Users/sasha/Library/R/3.5/library |
| doParallel | doParallel | 1.0.15 | 1.0.15 | /Users/sasha/Library/R/3.5/library/doParallel | /Users/sasha/Library/R/3.5/library/doParallel | FALSE | FALSE | 2019-08-02 | CRAN (R 3.5.2) | NA | /Users/sasha/Library/R/3.5/library |
| doSNOW | doSNOW | 1.0.18 | 1.0.18 | /Users/sasha/Library/R/3.5/library/doSNOW | /Users/sasha/Library/R/3.5/library/doSNOW | FALSE | FALSE | 2019-07-27 | CRAN (R 3.5.2) | NA | /Users/sasha/Library/R/3.5/library |
| dplyr | dplyr | 0.8.3 | 0.8.3 | /Users/sasha/Library/R/3.5/library/dplyr | /Users/sasha/Library/R/3.5/library/dplyr | TRUE | FALSE | 2019-07-04 | CRAN (R 3.5.2) | NA | /Users/sasha/Library/R/3.5/library |
| effects | effects | 4.1.2 | 4.1-2 | /Users/sasha/Library/R/3.5/library/effects | /Users/sasha/Library/R/3.5/library/effects | TRUE | FALSE | 2019-09-03 | CRAN (R 3.5.2) | NA | /Users/sasha/Library/R/3.5/library |
| emmeans | emmeans | 1.4 | 1.4 | /Users/sasha/Library/R/3.5/library/emmeans | /Users/sasha/Library/R/3.5/library/emmeans | FALSE | FALSE | 2019-08-01 | CRAN (R 3.5.2) | NA | /Users/sasha/Library/R/3.5/library |
| estimability | estimability | 1.3 | 1.3 | /Users/sasha/Library/R/3.5/library/estimability | /Users/sasha/Library/R/3.5/library/estimability | FALSE | FALSE | 2018-02-11 | CRAN (R 3.5.0) | NA | /Users/sasha/Library/R/3.5/library |
| evaluate | evaluate | 0.14 | 0.14 | /Users/sasha/Library/R/3.5/library/evaluate | /Users/sasha/Library/R/3.5/library/evaluate | FALSE | FALSE | 2019-05-28 | CRAN (R 3.5.2) | NA | /Users/sasha/Library/R/3.5/library |
| forcats | forcats | 0.4.0 | 0.4.0 | /Users/sasha/Library/R/3.5/library/forcats | /Users/sasha/Library/R/3.5/library/forcats | TRUE | FALSE | 2019-02-17 | CRAN (R 3.5.3) | NA | /Users/sasha/Library/R/3.5/library |
| foreach | foreach | 1.4.7 | 1.4.7 | /Users/sasha/Library/R/3.5/library/foreach | /Users/sasha/Library/R/3.5/library/foreach | FALSE | FALSE | 2019-07-27 | CRAN (R 3.5.2) | NA | /Users/sasha/Library/R/3.5/library |
| foreign | foreign | 0.8.72 | 0.8-72 | /Library/Frameworks/R.framework/Versions/3.5/Resources/library/foreign | /Library/Frameworks/R.framework/Versions/3.5/Resources/library/foreign | FALSE | FALSE | 2019-08-02 | CRAN (R 3.5.2) | NA | /Library/Frameworks/R.framework/Versions/3.5/Resources/library |
| Formula | Formula | 1.2.3 | 1.2-3 | /Users/sasha/Library/R/3.5/library/Formula | /Users/sasha/Library/R/3.5/library/Formula | FALSE | FALSE | 2018-05-03 | CRAN (R 3.5.0) | NA | /Users/sasha/Library/R/3.5/library |
| fs | fs | 1.3.1 | 1.3.1 | /Users/sasha/Library/R/3.5/library/fs | /Users/sasha/Library/R/3.5/library/fs | FALSE | FALSE | 2019-05-06 | CRAN (R 3.5.2) | NA | /Users/sasha/Library/R/3.5/library |
| genefilter | genefilter | 1.64.0 | 1.64.0 | /Users/sasha/Library/R/3.5/library/genefilter | /Users/sasha/Library/R/3.5/library/genefilter | FALSE | FALSE | 2018-10-30 | Bioconductor | NA | /Users/sasha/Library/R/3.5/library |
| geneplotter | geneplotter | 1.60.0 | 1.60.0 | /Users/sasha/Library/R/3.5/library/geneplotter | /Users/sasha/Library/R/3.5/library/geneplotter | FALSE | FALSE | 2018-10-30 | Bioconductor | NA | /Users/sasha/Library/R/3.5/library |
| generics | generics | 0.0.2 | 0.0.2 | /Users/sasha/Library/R/3.5/library/generics | /Users/sasha/Library/R/3.5/library/generics | FALSE | FALSE | 2018-11-29 | CRAN (R 3.5.3) | NA | /Users/sasha/Library/R/3.5/library |
| GenomeInfoDb | GenomeInfoDb | 1.18.2 | 1.18.2 | /Users/sasha/Library/R/3.5/library/GenomeInfoDb | /Users/sasha/Library/R/3.5/library/GenomeInfoDb | TRUE | FALSE | 2019-02-12 | Bioconductor | NA | /Users/sasha/Library/R/3.5/library |
| GenomeInfoDbData | GenomeInfoDbData | 1.2.0 | 1.2.0 | /Users/sasha/Library/R/3.5/library/GenomeInfoDbData | /Users/sasha/Library/R/3.5/library/GenomeInfoDbData | FALSE | FALSE | 2019-06-14 | Bioconductor | NA | /Users/sasha/Library/R/3.5/library |
| GenomicRanges | GenomicRanges | 1.34.0 | 1.34.0 | /Users/sasha/Library/R/3.5/library/GenomicRanges | /Users/sasha/Library/R/3.5/library/GenomicRanges | TRUE | FALSE | 2018-10-30 | Bioconductor | NA | /Users/sasha/Library/R/3.5/library |
| ggeffects | ggeffects | 0.12.0 | 0.12.0 | /Users/sasha/Library/R/3.5/library/ggeffects | /Users/sasha/Library/R/3.5/library/ggeffects | FALSE | FALSE | 2019-09-03 | CRAN (R 3.5.2) | NA | /Users/sasha/Library/R/3.5/library |
| ggplot2 | ggplot2 | 3.2.1 | 3.2.1 | /Users/sasha/Library/R/3.5/library/ggplot2 | /Users/sasha/Library/R/3.5/library/ggplot2 | TRUE | FALSE | 2019-08-10 | CRAN (R 3.5.2) | NA | /Users/sasha/Library/R/3.5/library |
| ggrepel | ggrepel | 0.8.1 | 0.8.1 | /Users/sasha/Library/R/3.5/library/ggrepel | /Users/sasha/Library/R/3.5/library/ggrepel | FALSE | FALSE | 2019-05-07 | CRAN (R 3.5.2) | NA | /Users/sasha/Library/R/3.5/library |
| glmmTMB | glmmTMB | 0.2.3 | 0.2.3 | /Users/sasha/Library/R/3.5/library/glmmTMB | /Users/sasha/Library/R/3.5/library/glmmTMB | FALSE | FALSE | 2019-01-11 | CRAN (R 3.5.2) | NA | /Users/sasha/Library/R/3.5/library |
| glue | glue | 1.3.1 | 1.3.1 | /Users/sasha/Library/R/3.5/library/glue | /Users/sasha/Library/R/3.5/library/glue | FALSE | FALSE | 2019-03-12 | CRAN (R 3.5.3) | NA | /Users/sasha/Library/R/3.5/library |
| gridExtra | gridExtra | 2.3 | 2.3 | /Users/sasha/Library/R/3.5/library/gridExtra | /Users/sasha/Library/R/3.5/library/gridExtra | TRUE | FALSE | 2017-09-09 | CRAN (R 3.5.3) | NA | /Users/sasha/Library/R/3.5/library |
| gtable | gtable | 0.3.0 | 0.3.0 | /Users/sasha/Library/R/3.5/library/gtable | /Users/sasha/Library/R/3.5/library/gtable | FALSE | FALSE | 2019-03-25 | CRAN (R 3.5.3) | NA | /Users/sasha/Library/R/3.5/library |
| haven | haven | 2.1.1 | 2.1.1 | /Users/sasha/Library/R/3.5/library/haven | /Users/sasha/Library/R/3.5/library/haven | FALSE | FALSE | 2019-07-04 | CRAN (R 3.5.2) | NA | /Users/sasha/Library/R/3.5/library |
| Hmisc | Hmisc | 4.2.0 | 4.2-0 | /Users/sasha/Library/R/3.5/library/Hmisc | /Users/sasha/Library/R/3.5/library/Hmisc | FALSE | FALSE | 2019-01-26 | CRAN (R 3.5.2) | NA | /Users/sasha/Library/R/3.5/library |
| hms | hms | 0.5.1 | 0.5.1 | /Users/sasha/Library/R/3.5/library/hms | /Users/sasha/Library/R/3.5/library/hms | FALSE | FALSE | 2019-08-23 | CRAN (R 3.5.2) | NA | /Users/sasha/Library/R/3.5/library |
| htmlTable | htmlTable | 1.13.1 | 1.13.1 | /Users/sasha/Library/R/3.5/library/htmlTable | /Users/sasha/Library/R/3.5/library/htmlTable | FALSE | FALSE | 2019-01-07 | CRAN (R 3.5.2) | NA | /Users/sasha/Library/R/3.5/library |
| htmltools | htmltools | 0.3.6 | 0.3.6 | /Users/sasha/Library/R/3.5/library/htmltools | /Users/sasha/Library/R/3.5/library/htmltools | FALSE | FALSE | 2017-04-28 | CRAN (R 3.5.3) | NA | /Users/sasha/Library/R/3.5/library |
| htmlwidgets | htmlwidgets | 1.3 | 1.3 | /Users/sasha/Library/R/3.5/library/htmlwidgets | /Users/sasha/Library/R/3.5/library/htmlwidgets | FALSE | FALSE | 2018-09-30 | CRAN (R 3.5.3) | NA | /Users/sasha/Library/R/3.5/library |
| httr | httr | 1.4.1 | 1.4.1 | /Users/sasha/Library/R/3.5/library/httr | /Users/sasha/Library/R/3.5/library/httr | FALSE | FALSE | 2019-08-05 | CRAN (R 3.5.2) | NA | /Users/sasha/Library/R/3.5/library |
| igraph | igraph | 1.2.4.1 | 1.2.4.1 | /Users/sasha/Library/R/3.5/library/igraph | /Users/sasha/Library/R/3.5/library/igraph | FALSE | FALSE | 2019-04-22 | CRAN (R 3.5.2) | NA | /Users/sasha/Library/R/3.5/library |
| insight | insight | 0.4.1 | 0.4.1 | /Users/sasha/Library/R/3.5/library/insight | /Users/sasha/Library/R/3.5/library/insight | FALSE | FALSE | 2019-07-24 | CRAN (R 3.5.2) | NA | /Users/sasha/Library/R/3.5/library |
| IRanges | IRanges | 2.16.0 | 2.16.0 | /Users/sasha/Library/R/3.5/library/IRanges | /Users/sasha/Library/R/3.5/library/IRanges | TRUE | FALSE | 2018-10-30 | Bioconductor | NA | /Users/sasha/Library/R/3.5/library |
| iterators | iterators | 1.0.12 | 1.0.12 | /Users/sasha/Library/R/3.5/library/iterators | /Users/sasha/Library/R/3.5/library/iterators | FALSE | FALSE | 2019-07-26 | CRAN (R 3.5.2) | NA | /Users/sasha/Library/R/3.5/library |
| jsonlite | jsonlite | 1.6 | 1.6 | /Users/sasha/Library/R/3.5/library/jsonlite | /Users/sasha/Library/R/3.5/library/jsonlite | FALSE | FALSE | 2018-12-07 | CRAN (R 3.5.3) | NA | /Users/sasha/Library/R/3.5/library |
| kableExtra | kableExtra | 1.1.0 | 1.1.0 | /Users/sasha/Library/R/3.5/library/kableExtra | /Users/sasha/Library/R/3.5/library/kableExtra | TRUE | FALSE | 2019-03-16 | CRAN (R 3.5.2) | NA | /Users/sasha/Library/R/3.5/library |
| knitr | knitr | 1.24 | 1.24 | /Users/sasha/Library/R/3.5/library/knitr | /Users/sasha/Library/R/3.5/library/knitr | TRUE | FALSE | 2019-08-08 | CRAN (R 3.5.2) | NA | /Users/sasha/Library/R/3.5/library |
| labeling | labeling | 0.3 | 0.3 | /Users/sasha/Library/R/3.5/library/labeling | /Users/sasha/Library/R/3.5/library/labeling | FALSE | FALSE | 2014-08-23 | CRAN (R 3.5.3) | NA | /Users/sasha/Library/R/3.5/library |
| lattice | lattice | 0.20.38 | 0.20-38 | /Library/Frameworks/R.framework/Versions/3.5/Resources/library/lattice | /Library/Frameworks/R.framework/Versions/3.5/Resources/library/lattice | TRUE | FALSE | 2018-11-04 | CRAN (R 3.5.3) | NA | /Library/Frameworks/R.framework/Versions/3.5/Resources/library |
| latticeExtra | latticeExtra | 0.6.28 | 0.6-28 | /Users/sasha/Library/R/3.5/library/latticeExtra | /Users/sasha/Library/R/3.5/library/latticeExtra | FALSE | FALSE | 2016-02-09 | CRAN (R 3.5.0) | NA | /Users/sasha/Library/R/3.5/library |
| lazyeval | lazyeval | 0.2.2 | 0.2.2 | /Users/sasha/Library/R/3.5/library/lazyeval | /Users/sasha/Library/R/3.5/library/lazyeval | FALSE | FALSE | 2019-03-15 | CRAN (R 3.5.3) | NA | /Users/sasha/Library/R/3.5/library |
| lme4 | lme4 | 1.1.21 | 1.1-21 | /Users/sasha/Library/R/3.5/library/lme4 | /Users/sasha/Library/R/3.5/library/lme4 | TRUE | FALSE | 2019-03-05 | CRAN (R 3.5.2) | NA | /Users/sasha/Library/R/3.5/library |
| locfit | locfit | 1.5.9.1 | 1.5-9.1 | /Users/sasha/Library/R/3.5/library/locfit | /Users/sasha/Library/R/3.5/library/locfit | FALSE | FALSE | 2013-04-20 | CRAN (R 3.5.0) | NA | /Users/sasha/Library/R/3.5/library |
| lubridate | lubridate | 1.7.4 | 1.7.4 | /Users/sasha/Library/R/3.5/library/lubridate | /Users/sasha/Library/R/3.5/library/lubridate | FALSE | FALSE | 2018-04-11 | CRAN (R 3.5.3) | NA | /Users/sasha/Library/R/3.5/library |
| magrittr | magrittr | 1.5 | 1.5 | /Users/sasha/Library/R/3.5/library/magrittr | /Users/sasha/Library/R/3.5/library/magrittr | FALSE | FALSE | 2014-11-22 | CRAN (R 3.5.3) | NA | /Users/sasha/Library/R/3.5/library |
| MASS | MASS | 7.3.51.4 | 7.3-51.4 | /Library/Frameworks/R.framework/Versions/3.5/Resources/library/MASS | /Library/Frameworks/R.framework/Versions/3.5/Resources/library/MASS | FALSE | FALSE | 2019-03-31 | CRAN (R 3.5.2) | NA | /Library/Frameworks/R.framework/Versions/3.5/Resources/library |
| Matrix | Matrix | 1.2.17 | 1.2-17 | /Library/Frameworks/R.framework/Versions/3.5/Resources/library/Matrix | /Library/Frameworks/R.framework/Versions/3.5/Resources/library/Matrix | TRUE | FALSE | 2019-03-22 | CRAN (R 3.5.2) | NA | /Library/Frameworks/R.framework/Versions/3.5/Resources/library |
| matrixStats | matrixStats | 0.54.0 | 0.54.0 | /Users/sasha/Library/R/3.5/library/matrixStats | /Users/sasha/Library/R/3.5/library/matrixStats | TRUE | FALSE | 2018-07-23 | CRAN (R 3.5.3) | NA | /Users/sasha/Library/R/3.5/library |
| memoise | memoise | 1.1.0 | 1.1.0 | /Users/sasha/Library/R/3.5/library/memoise | /Users/sasha/Library/R/3.5/library/memoise | FALSE | FALSE | 2017-04-21 | CRAN (R 3.5.0) | NA | /Users/sasha/Library/R/3.5/library |
| mgcv | mgcv | 1.8.28 | 1.8-28 | /Library/Frameworks/R.framework/Versions/3.5/Resources/library/mgcv | /Library/Frameworks/R.framework/Versions/3.5/Resources/library/mgcv | FALSE | FALSE | 2019-03-21 | CRAN (R 3.5.2) | NA | /Library/Frameworks/R.framework/Versions/3.5/Resources/library |
| minqa | minqa | 1.2.4 | 1.2.4 | /Users/sasha/Library/R/3.5/library/minqa | /Users/sasha/Library/R/3.5/library/minqa | FALSE | FALSE | 2014-10-09 | CRAN (R 3.5.0) | NA | /Users/sasha/Library/R/3.5/library |
| mitools | mitools | 2.4 | 2.4 | /Users/sasha/Library/R/3.5/library/mitools | /Users/sasha/Library/R/3.5/library/mitools | FALSE | FALSE | 2019-04-26 | CRAN (R 3.5.2) | NA | /Users/sasha/Library/R/3.5/library |
| mnormt | mnormt | 1.5.5 | 1.5-5 | /Users/sasha/Library/R/3.5/library/mnormt | /Users/sasha/Library/R/3.5/library/mnormt | FALSE | FALSE | 2016-10-15 | CRAN (R 3.5.3) | NA | /Users/sasha/Library/R/3.5/library |
| modelr | modelr | 0.1.5 | 0.1.5 | /Users/sasha/Library/R/3.5/library/modelr | /Users/sasha/Library/R/3.5/library/modelr | FALSE | FALSE | 2019-08-08 | CRAN (R 3.5.2) | NA | /Users/sasha/Library/R/3.5/library |
| multtest | multtest | 2.38.0 | 2.38.0 | /Users/sasha/Library/R/3.5/library/multtest | /Users/sasha/Library/R/3.5/library/multtest | FALSE | FALSE | 2018-10-30 | Bioconductor | NA | /Users/sasha/Library/R/3.5/library |
| munsell | munsell | 0.5.0 | 0.5.0 | /Users/sasha/Library/R/3.5/library/munsell | /Users/sasha/Library/R/3.5/library/munsell | FALSE | FALSE | 2018-06-12 | CRAN (R 3.5.3) | NA | /Users/sasha/Library/R/3.5/library |
| mvnfast | mvnfast | 0.2.5 | 0.2.5 | /Users/sasha/Library/R/3.5/library/mvnfast | /Users/sasha/Library/R/3.5/library/mvnfast | FALSE | FALSE | 2018-01-31 | CRAN (R 3.5.0) | NA | /Users/sasha/Library/R/3.5/library |
| mvtnorm | mvtnorm | 1.0.11 | 1.0-11 | /Users/sasha/Library/R/3.5/library/mvtnorm | /Users/sasha/Library/R/3.5/library/mvtnorm | FALSE | FALSE | 2019-06-19 | CRAN (R 3.5.2) | NA | /Users/sasha/Library/R/3.5/library |
| nlme | nlme | 3.1.141 | 3.1-141 | /Library/Frameworks/R.framework/Versions/3.5/Resources/library/nlme | /Library/Frameworks/R.framework/Versions/3.5/Resources/library/nlme | TRUE | FALSE | 2019-08-01 | CRAN (R 3.5.2) | NA | /Library/Frameworks/R.framework/Versions/3.5/Resources/library |
| nloptr | nloptr | 1.2.1 | 1.2.1 | /Users/sasha/Library/R/3.5/library/nloptr | /Users/sasha/Library/R/3.5/library/nloptr | FALSE | FALSE | 2018-10-03 | CRAN (R 3.5.0) | NA | /Users/sasha/Library/R/3.5/library |
| nnet | nnet | 7.3.12 | 7.3-12 | /Library/Frameworks/R.framework/Versions/3.5/Resources/library/nnet | /Library/Frameworks/R.framework/Versions/3.5/Resources/library/nnet | FALSE | FALSE | 2016-02-02 | CRAN (R 3.5.3) | NA | /Library/Frameworks/R.framework/Versions/3.5/Resources/library |
| performance | performance | 0.3.0 | 0.3.0 | /Users/sasha/Library/R/3.5/library/performance | /Users/sasha/Library/R/3.5/library/performance | FALSE | FALSE | 2019-08-05 | CRAN (R 3.5.2) | NA | /Users/sasha/Library/R/3.5/library |
| permute | permute | 0.9.5 | 0.9-5 | /Users/sasha/Library/R/3.5/library/permute | /Users/sasha/Library/R/3.5/library/permute | TRUE | FALSE | 2019-03-12 | CRAN (R 3.5.2) | NA | /Users/sasha/Library/R/3.5/library |
| phyloseq | phyloseq | 1.26.1 | 1.26.1 | /Users/sasha/Library/R/3.5/library/phyloseq | /Users/sasha/Library/R/3.5/library/phyloseq | TRUE | FALSE | 2019-01-04 | Bioconductor | NA | /Users/sasha/Library/R/3.5/library |
| pillar | pillar | 1.4.2 | 1.4.2 | /Users/sasha/Library/R/3.5/library/pillar | /Users/sasha/Library/R/3.5/library/pillar | FALSE | FALSE | 2019-06-29 | CRAN (R 3.5.2) | NA | /Users/sasha/Library/R/3.5/library |
| pkgbuild | pkgbuild | 1.0.5 | 1.0.5 | /Users/sasha/Library/R/3.5/library/pkgbuild | /Users/sasha/Library/R/3.5/library/pkgbuild | FALSE | FALSE | 2019-08-26 | CRAN (R 3.5.2) | NA | /Users/sasha/Library/R/3.5/library |
| pkgconfig | pkgconfig | 2.0.2 | 2.0.2 | /Users/sasha/Library/R/3.5/library/pkgconfig | /Users/sasha/Library/R/3.5/library/pkgconfig | FALSE | FALSE | 2018-08-16 | CRAN (R 3.5.3) | NA | /Users/sasha/Library/R/3.5/library |
| pkgload | pkgload | 1.0.2 | 1.0.2 | /Users/sasha/Library/R/3.5/library/pkgload | /Users/sasha/Library/R/3.5/library/pkgload | FALSE | FALSE | 2018-10-29 | CRAN (R 3.5.0) | NA | /Users/sasha/Library/R/3.5/library |
| plyr | plyr | 1.8.4 | 1.8.4 | /Users/sasha/Library/R/3.5/library/plyr | /Users/sasha/Library/R/3.5/library/plyr | FALSE | FALSE | 2016-06-08 | CRAN (R 3.5.3) | NA | /Users/sasha/Library/R/3.5/library |
| prettyunits | prettyunits | 1.0.2 | 1.0.2 | /Users/sasha/Library/R/3.5/library/prettyunits | /Users/sasha/Library/R/3.5/library/prettyunits | FALSE | FALSE | 2015-07-13 | CRAN (R 3.5.3) | NA | /Users/sasha/Library/R/3.5/library |
| processx | processx | 3.4.1 | 3.4.1 | /Users/sasha/Library/R/3.5/library/processx | /Users/sasha/Library/R/3.5/library/processx | FALSE | FALSE | 2019-07-18 | CRAN (R 3.5.2) | NA | /Users/sasha/Library/R/3.5/library |
| ps | ps | 1.3.0 | 1.3.0 | /Users/sasha/Library/R/3.5/library/ps | /Users/sasha/Library/R/3.5/library/ps | FALSE | FALSE | 2018-12-21 | CRAN (R 3.5.3) | NA | /Users/sasha/Library/R/3.5/library |
| psych | psych | 1.8.12 | 1.8.12 | /Users/sasha/Library/R/3.5/library/psych | /Users/sasha/Library/R/3.5/library/psych | FALSE | FALSE | 2019-01-12 | CRAN (R 3.5.2) | NA | /Users/sasha/Library/R/3.5/library |
| purrr | purrr | 0.3.2 | 0.3.2 | /Users/sasha/Library/R/3.5/library/purrr | /Users/sasha/Library/R/3.5/library/purrr | TRUE | FALSE | 2019-03-15 | CRAN (R 3.5.3) | NA | /Users/sasha/Library/R/3.5/library |
| qiime2R | qiime2R | 0.99.11 | 0.99.11 | /Users/sasha/Library/R/3.5/library/qiime2R | /Users/sasha/Library/R/3.5/library/qiime2R | TRUE | FALSE | 2019-06-10 | Github (jbisanz/qiime2R@afd7cdd) | NA | /Users/sasha/Library/R/3.5/library |
| R6 | R6 | 2.4.0 | 2.4.0 | /Users/sasha/Library/R/3.5/library/R6 | /Users/sasha/Library/R/3.5/library/R6 | FALSE | FALSE | 2019-02-14 | CRAN (R 3.5.3) | NA | /Users/sasha/Library/R/3.5/library |
| RColorBrewer | RColorBrewer | 1.1.2 | 1.1-2 | /Users/sasha/Library/R/3.5/library/RColorBrewer | /Users/sasha/Library/R/3.5/library/RColorBrewer | TRUE | FALSE | 2014-12-07 | CRAN (R 3.5.3) | NA | /Users/sasha/Library/R/3.5/library |
| Rcpp | Rcpp | 1.0.2 | 1.0.2 | /Users/sasha/Library/R/3.5/library/Rcpp | /Users/sasha/Library/R/3.5/library/Rcpp | FALSE | FALSE | 2019-07-25 | CRAN (R 3.5.2) | NA | /Users/sasha/Library/R/3.5/library |
| RCurl | RCurl | 1.95.4.12 | 1.95-4.12 | /Users/sasha/Library/R/3.5/library/RCurl | /Users/sasha/Library/R/3.5/library/RCurl | FALSE | FALSE | 2019-03-04 | CRAN (R 3.5.3) | NA | /Users/sasha/Library/R/3.5/library |
| readr | readr | 1.3.1 | 1.3.1 | /Users/sasha/Library/R/3.5/library/readr | /Users/sasha/Library/R/3.5/library/readr | TRUE | FALSE | 2018-12-21 | CRAN (R 3.5.3) | NA | /Users/sasha/Library/R/3.5/library |
| readxl | readxl | 1.3.1 | 1.3.1 | /Users/sasha/Library/R/3.5/library/readxl | /Users/sasha/Library/R/3.5/library/readxl | FALSE | FALSE | 2019-03-13 | CRAN (R 3.5.2) | NA | /Users/sasha/Library/R/3.5/library |
| remotes | remotes | 2.1.0 | 2.1.0 | /Users/sasha/Library/R/3.5/library/remotes | /Users/sasha/Library/R/3.5/library/remotes | FALSE | FALSE | 2019-06-24 | CRAN (R 3.5.2) | NA | /Users/sasha/Library/R/3.5/library |
| reshape2 | reshape2 | 1.4.3 | 1.4.3 | /Users/sasha/Library/R/3.5/library/reshape2 | /Users/sasha/Library/R/3.5/library/reshape2 | FALSE | FALSE | 2017-12-11 | CRAN (R 3.5.3) | NA | /Users/sasha/Library/R/3.5/library |
| rhdf5 | rhdf5 | 2.26.2 | 2.26.2 | /Users/sasha/Library/R/3.5/library/rhdf5 | /Users/sasha/Library/R/3.5/library/rhdf5 | FALSE | FALSE | 2019-01-02 | Bioconductor | NA | /Users/sasha/Library/R/3.5/library |
| Rhdf5lib | Rhdf5lib | 1.4.3 | 1.4.3 | /Users/sasha/Library/R/3.5/library/Rhdf5lib | /Users/sasha/Library/R/3.5/library/Rhdf5lib | FALSE | FALSE | 2019-03-25 | Bioconductor | NA | /Users/sasha/Library/R/3.5/library |
| rlang | rlang | 0.4.0 | 0.4.0 | /Users/sasha/Library/R/3.5/library/rlang | /Users/sasha/Library/R/3.5/library/rlang | FALSE | FALSE | 2019-06-25 | CRAN (R 3.5.2) | NA | /Users/sasha/Library/R/3.5/library |
| rmarkdown | rmarkdown | 1.15 | 1.15 | /Users/sasha/Library/R/3.5/library/rmarkdown | /Users/sasha/Library/R/3.5/library/rmarkdown | FALSE | FALSE | 2019-08-21 | CRAN (R 3.5.2) | NA | /Users/sasha/Library/R/3.5/library |
| rpart | rpart | 4.1.15 | 4.1-15 | /Library/Frameworks/R.framework/Versions/3.5/Resources/library/rpart | /Library/Frameworks/R.framework/Versions/3.5/Resources/library/rpart | FALSE | FALSE | 2019-04-12 | CRAN (R 3.5.2) | NA | /Library/Frameworks/R.framework/Versions/3.5/Resources/library |
| rprojroot | rprojroot | 1.3.2 | 1.3-2 | /Users/sasha/Library/R/3.5/library/rprojroot | /Users/sasha/Library/R/3.5/library/rprojroot | FALSE | FALSE | 2018-01-03 | CRAN (R 3.5.3) | NA | /Users/sasha/Library/R/3.5/library |
| RSQLite | RSQLite | 2.1.2 | 2.1.2 | /Users/sasha/Library/R/3.5/library/RSQLite | /Users/sasha/Library/R/3.5/library/RSQLite | FALSE | FALSE | 2019-07-24 | CRAN (R 3.5.2) | NA | /Users/sasha/Library/R/3.5/library |
| rstudioapi | rstudioapi | 0.10 | 0.10 | /Users/sasha/Library/R/3.5/library/rstudioapi | /Users/sasha/Library/R/3.5/library/rstudioapi | FALSE | FALSE | 2019-03-19 | CRAN (R 3.5.3) | NA | /Users/sasha/Library/R/3.5/library |
| rvest | rvest | 0.3.4 | 0.3.4 | /Users/sasha/Library/R/3.5/library/rvest | /Users/sasha/Library/R/3.5/library/rvest | FALSE | FALSE | 2019-05-15 | CRAN (R 3.5.2) | NA | /Users/sasha/Library/R/3.5/library |
| S4Vectors | S4Vectors | 0.20.1 | 0.20.1 | /Users/sasha/Library/R/3.5/library/S4Vectors | /Users/sasha/Library/R/3.5/library/S4Vectors | TRUE | FALSE | 2018-11-09 | Bioconductor | NA | /Users/sasha/Library/R/3.5/library |
| scales | scales | 1.0.0 | 1.0.0 | /Users/sasha/Library/R/3.5/library/scales | /Users/sasha/Library/R/3.5/library/scales | TRUE | FALSE | 2018-08-09 | CRAN (R 3.5.3) | NA | /Users/sasha/Library/R/3.5/library |
| sessioninfo | sessioninfo | 1.1.1 | 1.1.1 | /Users/sasha/Library/R/3.5/library/sessioninfo | /Users/sasha/Library/R/3.5/library/sessioninfo | FALSE | FALSE | 2018-11-05 | CRAN (R 3.5.0) | NA | /Users/sasha/Library/R/3.5/library |
| sjlabelled | sjlabelled | 1.1.0 | 1.1.0 | /Users/sasha/Library/R/3.5/library/sjlabelled | /Users/sasha/Library/R/3.5/library/sjlabelled | FALSE | FALSE | 2019-06-06 | CRAN (R 3.5.2) | NA | /Users/sasha/Library/R/3.5/library |
| sjmisc | sjmisc | 2.8.1 | 2.8.1 | /Users/sasha/Library/R/3.5/library/sjmisc | /Users/sasha/Library/R/3.5/library/sjmisc | FALSE | FALSE | 2019-06-17 | CRAN (R 3.5.2) | NA | /Users/sasha/Library/R/3.5/library |
| sjPlot | sjPlot | 2.7.0 | 2.7.0 | /Users/sasha/Library/R/3.5/library/sjPlot | /Users/sasha/Library/R/3.5/library/sjPlot | TRUE | FALSE | 2019-08-05 | Github (strengejacke/sjPlot@97c08d5) | NA | /Users/sasha/Library/R/3.5/library |
| sjstats | sjstats | 0.17.5 | 0.17.5 | /Users/sasha/Library/R/3.5/library/sjstats | /Users/sasha/Library/R/3.5/library/sjstats | FALSE | FALSE | 2019-06-04 | CRAN (R 3.5.2) | NA | /Users/sasha/Library/R/3.5/library |
| snow | snow | 0.4.3 | 0.4-3 | /Users/sasha/Library/R/3.5/library/snow | /Users/sasha/Library/R/3.5/library/snow | FALSE | FALSE | 2018-09-14 | CRAN (R 3.5.0) | NA | /Users/sasha/Library/R/3.5/library |
| stringi | stringi | 1.4.3 | 1.4.3 | /Users/sasha/Library/R/3.5/library/stringi | /Users/sasha/Library/R/3.5/library/stringi | FALSE | FALSE | 2019-03-12 | CRAN (R 3.5.3) | NA | /Users/sasha/Library/R/3.5/library |
| stringr | stringr | 1.4.0 | 1.4.0 | /Users/sasha/Library/R/3.5/library/stringr | /Users/sasha/Library/R/3.5/library/stringr | TRUE | FALSE | 2019-02-10 | CRAN (R 3.5.3) | NA | /Users/sasha/Library/R/3.5/library |
| SummarizedExperiment | SummarizedExperiment | 1.12.0 | 1.12.0 | /Users/sasha/Library/R/3.5/library/SummarizedExperiment | /Users/sasha/Library/R/3.5/library/SummarizedExperiment | TRUE | FALSE | 2018-10-30 | Bioconductor | NA | /Users/sasha/Library/R/3.5/library |
| survey | survey | 3.36 | 3.36 | /Users/sasha/Library/R/3.5/library/survey | /Users/sasha/Library/R/3.5/library/survey | FALSE | FALSE | 2019-04-27 | CRAN (R 3.5.2) | NA | /Users/sasha/Library/R/3.5/library |
| survival | survival | 2.44.1.1 | 2.44-1.1 | /Library/Frameworks/R.framework/Versions/3.5/Resources/library/survival | /Library/Frameworks/R.framework/Versions/3.5/Resources/library/survival | FALSE | FALSE | 2019-04-01 | CRAN (R 3.5.2) | NA | /Library/Frameworks/R.framework/Versions/3.5/Resources/library |
| testthat | testthat | 2.2.1 | 2.2.1 | /Users/sasha/Library/R/3.5/library/testthat | /Users/sasha/Library/R/3.5/library/testthat | FALSE | FALSE | 2019-07-25 | CRAN (R 3.5.2) | NA | /Users/sasha/Library/R/3.5/library |
| tibble | tibble | 2.1.3 | 2.1.3 | /Users/sasha/Library/R/3.5/library/tibble | /Users/sasha/Library/R/3.5/library/tibble | TRUE | FALSE | 2019-06-06 | CRAN (R 3.5.2) | NA | /Users/sasha/Library/R/3.5/library |
| tidyr | tidyr | 0.8.3 | 0.8.3 | /Users/sasha/Library/R/3.5/library/tidyr | /Users/sasha/Library/R/3.5/library/tidyr | TRUE | FALSE | 2019-03-01 | CRAN (R 3.5.3) | NA | /Users/sasha/Library/R/3.5/library |
| tidyselect | tidyselect | 0.2.5 | 0.2.5 | /Users/sasha/Library/R/3.5/library/tidyselect | /Users/sasha/Library/R/3.5/library/tidyselect | FALSE | FALSE | 2018-10-11 | CRAN (R 3.5.3) | NA | /Users/sasha/Library/R/3.5/library |
| tidyverse | tidyverse | 1.2.1 | 1.2.1 | /Users/sasha/Library/R/3.5/library/tidyverse | /Users/sasha/Library/R/3.5/library/tidyverse | TRUE | FALSE | 2017-11-14 | CRAN (R 3.5.0) | NA | /Users/sasha/Library/R/3.5/library |
| TMB | TMB | 1.7.15 | 1.7.15 | /Users/sasha/Library/R/3.5/library/TMB | /Users/sasha/Library/R/3.5/library/TMB | FALSE | FALSE | 2018-11-09 | CRAN (R 3.5.3) | NA | /Users/sasha/Library/R/3.5/library |
| usethis | usethis | 1.5.1 | 1.5.1 | /Users/sasha/Library/R/3.5/library/usethis | /Users/sasha/Library/R/3.5/library/usethis | FALSE | FALSE | 2019-07-04 | CRAN (R 3.5.2) | NA | /Users/sasha/Library/R/3.5/library |
| vctrs | vctrs | 0.2.0 | 0.2.0 | /Users/sasha/Library/R/3.5/library/vctrs | /Users/sasha/Library/R/3.5/library/vctrs | FALSE | FALSE | 2019-07-05 | CRAN (R 3.5.2) | NA | /Users/sasha/Library/R/3.5/library |
| vegan | vegan | 2.5.6 | 2.5-6 | /Users/sasha/Library/R/3.5/library/vegan | /Users/sasha/Library/R/3.5/library/vegan | TRUE | FALSE | 2019-09-01 | CRAN (R 3.5.2) | NA | /Users/sasha/Library/R/3.5/library |
| viridisLite | viridisLite | 0.3.0 | 0.3.0 | /Users/sasha/Library/R/3.5/library/viridisLite | /Users/sasha/Library/R/3.5/library/viridisLite | FALSE | FALSE | 2018-02-01 | CRAN (R 3.5.3) | NA | /Users/sasha/Library/R/3.5/library |
| webshot | webshot | 0.5.1 | 0.5.1 | /Users/sasha/Library/R/3.5/library/webshot | /Users/sasha/Library/R/3.5/library/webshot | FALSE | FALSE | 2018-09-28 | CRAN (R 3.5.0) | NA | /Users/sasha/Library/R/3.5/library |
| withr | withr | 2.1.2 | 2.1.2 | /Users/sasha/Library/R/3.5/library/withr | /Users/sasha/Library/R/3.5/library/withr | FALSE | FALSE | 2018-03-15 | CRAN (R 3.5.3) | NA | /Users/sasha/Library/R/3.5/library |
| xfun | xfun | 0.9 | 0.9 | /Users/sasha/Library/R/3.5/library/xfun | /Users/sasha/Library/R/3.5/library/xfun | FALSE | FALSE | 2019-08-21 | CRAN (R 3.5.2) | NA | /Users/sasha/Library/R/3.5/library |
| XML | XML | 3.98.1.20 | 3.98-1.20 | /Users/sasha/Library/R/3.5/library/XML | /Users/sasha/Library/R/3.5/library/XML | FALSE | FALSE | 2019-06-06 | CRAN (R 3.5.2) | NA | /Users/sasha/Library/R/3.5/library |
| xml2 | xml2 | 1.2.2 | 1.2.2 | /Users/sasha/Library/R/3.5/library/xml2 | /Users/sasha/Library/R/3.5/library/xml2 | FALSE | FALSE | 2019-08-09 | CRAN (R 3.5.2) | NA | /Users/sasha/Library/R/3.5/library |
| xtable | xtable | 1.8.4 | 1.8-4 | /Users/sasha/Library/R/3.5/library/xtable | /Users/sasha/Library/R/3.5/library/xtable | FALSE | FALSE | 2019-04-21 | CRAN (R 3.5.2) | NA | /Users/sasha/Library/R/3.5/library |
| XVector | XVector | 0.22.0 | 0.22.0 | /Users/sasha/Library/R/3.5/library/XVector | /Users/sasha/Library/R/3.5/library/XVector | FALSE | FALSE | 2018-10-30 | Bioconductor | NA | /Users/sasha/Library/R/3.5/library |
| yaml | yaml | 2.2.0 | 2.2.0 | /Users/sasha/Library/R/3.5/library/yaml | /Users/sasha/Library/R/3.5/library/yaml | FALSE | FALSE | 2018-07-25 | CRAN (R 3.5.3) | NA | /Users/sasha/Library/R/3.5/library |
| zeallot | zeallot | 0.1.0 | 0.1.0 | /Users/sasha/Library/R/3.5/library/zeallot | /Users/sasha/Library/R/3.5/library/zeallot | FALSE | FALSE | 2018-01-28 | CRAN (R 3.5.0) | NA | /Users/sasha/Library/R/3.5/library |
| zlibbioc | zlibbioc | 1.28.0 | 1.28.0 | /Users/sasha/Library/R/3.5/library/zlibbioc | /Users/sasha/Library/R/3.5/library/zlibbioc | FALSE | FALSE | 2018-10-30 | Bioconductor | NA | /Users/sasha/Library/R/3.5/library |