Weiyan 4/3/2020
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library("tibble")
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library(pheatmap)
library(fgsea)## Loading required package: Rcpp
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library(export)
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library(DT)
library(org.Hs.eg.db)## Loading required package: AnnotationDbi
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FUS_norm_raw <- read.csv("AllResultsOfQuantification_FUS.csv", header = TRUE)
FUS_PSMs_raw <- read.csv("PSMs_FUS.csv", header = TRUE)
FUS_PSMs_raw <-FUS_PSMs_raw%>%
dplyr::mutate(PSMs_ave =rowMeans(dplyr::select(FUS_PSMs_raw,contains("PSMs")),na.rm = FALSE) )%>%
dplyr::mutate(Unique_ave =rowMeans(dplyr::select(FUS_PSMs_raw,contains("Unique")),na.rm = FALSE))
FUS_norm <- FUS_norm_raw %>%
mutate(WT_Mean = rowMeans(dplyr::select(FUS_norm_raw,contains("U2OS")),na.rm = TRUE)) %>%
mutate(KO_Mean = rowMeans(dplyr::select(FUS_norm_raw,contains("clone110")),na.rm = TRUE))%>%
mutate(fc_log2 = WT_Mean-KO_Mean)%>%
dplyr::rename(pvalue_log=X.LOG.P.value.,clone110_bio1_r1=Intensity_03.14.18_clone110_bio1_rep1.calib,clone110_bio1_r2=Intensity_03.14.18_clone110_bio1_rep2.calib,
clone110_bio2_r1=Intensity_03.14.18_clone110_bio2_rep1.calib,clone110_bio2_r2=Intensity_03.14.18_clone110_bio2_rep2.calib,
clone110_bio3_r1=Intensity_04.02.18_clone110_bio3_rep1.calib,clone110_bio3_r2=Intensity_04.02.18_clone110_bio3_rep2.calib,
U2OS_bio1_r1=Intensity_03.14.18_U2OS_bio1_rep1.calib, U2OS_bio1_r2=Intensity_03.14.18_U2OS_bio1_rep2.calib,
U2OS_bio2_r1=Intensity_03.14.18_U2OS_bio2_rep1.calib, U2OS_bio2_r2=Intensity_03.14.18_U2OS_bio2_rep2.calib,
U2OS_bio3_r1=Intensity_04.02.18_U2OS_bio3_rep1.calib,U2OS_bio3_r2=Intensity_04.02.18_U2OS_bio3_rep2.calib)%>%
dplyr::select(Protein.Accession,U2OS_bio1_r1,U2OS_bio1_r2,U2OS_bio2_r1,U2OS_bio2_r2,U2OS_bio3_r1,U2OS_bio3_r2, clone110_bio1_r1,
clone110_bio1_r2,clone110_bio2_r1,clone110_bio2_r2, clone110_bio3_r1, clone110_bio3_r2,WT_Mean, KO_Mean,fc_log2, Fold.Change, pvalue_log)
FUS_norm_1 <- FUS_norm %>%
mutate(U2OS_bio1 = rowMeans(dplyr::select(FUS_norm,contains("U2OS_bio1")),na.rm = TRUE),
U2OS_bio2 = rowMeans(dplyr::select(FUS_norm,contains("U2OS_bio2")),na.rm = TRUE),
U2OS_bio3 = rowMeans(dplyr::select(FUS_norm,contains("U2OS_bio3")),na.rm = TRUE),
clone110_bio1 = rowMeans(dplyr::select(FUS_norm,contains("clone110_bio1")),na.rm = TRUE),
clone110_bio2 = rowMeans(dplyr::select(FUS_norm,contains("clone110_bio2")),na.rm = TRUE),
clone110_bio3 = rowMeans(dplyr::select(FUS_norm,contains("clone110_bio3")),na.rm = TRUE)) %>%
dplyr::select(Protein.Accession,U2OS_bio1,U2OS_bio2,U2OS_bio3,clone110_bio1,clone110_bio2, clone110_bio3,
U2OS_bio1_r1,U2OS_bio1_r2,U2OS_bio2_r1,U2OS_bio2_r2,U2OS_bio3_r1,U2OS_bio3_r2, clone110_bio1_r1,
clone110_bio1_r2,clone110_bio2_r1,clone110_bio2_r2, clone110_bio3_r1, clone110_bio3_r2,WT_Mean, KO_Mean,fc_log2, Fold.Change, pvalue_log)%>%
distinct()keytypes(org.Hs.eg.db)## [1] "ACCNUM" "ALIAS" "ENSEMBL" "ENSEMBLPROT" "ENSEMBLTRANS"
## [6] "ENTREZID" "ENZYME" "EVIDENCE" "EVIDENCEALL" "GENENAME"
## [11] "GO" "GOALL" "IPI" "MAP" "OMIM"
## [16] "ONTOLOGY" "ONTOLOGYALL" "PATH" "PFAM" "PMID"
## [21] "PROSITE" "REFSEQ" "SYMBOL" "UCSCKG" "UNIGENE"
## [26] "UNIPROT"
UNIPORT2symbol <- AnnotationDbi::select(org.Hs.eg.db,
key=as.character(FUS_norm_1$Protein.Accession),
columns="SYMBOL",
keytype="UNIPROT")## 'select()' returned 1:many mapping between keys and columns
UNIPORT2symbol <- as.tibble(UNIPORT2symbol)## Warning: `as.tibble()` is deprecated, use `as_tibble()` (but mind the new semantics).
## This warning is displayed once per session.
head(UNIPORT2symbol)## # A tibble: 6 x 2
## UNIPROT SYMBOL
## <chr> <chr>
## 1 P35637 FUS
## 2 P21695 GPD1
## 3 Q9UNE7 STUB1
## 4 O75190 DNAJB6
## 5 P22307 SCP2
## 6 P79522 PRR3
FUS_norm_2 <-dplyr::left_join(FUS_norm_1,UNIPORT2symbol, by =c("Protein.Accession"="UNIPROT") )%>%
distinct()%>%
drop_na()## Warning: Column `Protein.Accession`/`UNIPROT` joining factor and character
## vector, coercing into character vector
FUS_norm_3<- dplyr::rename(FUS_norm_2,UNIPROT=Protein.Accession)%>%
arrange(-fc_log2)
FUS_norm_x <-dplyr::left_join(FUS_norm_3,FUS_PSMs_raw, by =c("UNIPROT"="UNIPROT") )%>%
distinct()## Warning: Column `UNIPROT` joining character vector and factor, coercing into
## character vector
head(FUS_norm_x)## UNIPROT U2OS_bio1 U2OS_bio2 U2OS_bio3 clone110_bio1 clone110_bio2
## 1 P35637 29.74965 27.48805 29.69695 26.07085 23.75670
## 2 P21695 21.54020 26.29075 20.63860 22.10670 22.00370
## 3 Q9UNE7 21.24710 22.63160 22.92830 18.22540 20.79270
## 4 O75190 19.51480 20.19670 20.29265 17.38645 15.92305
## 5 P22307 21.42170 20.29275 21.34495 17.45670 19.56995
## 6 P79522 18.70005 15.19845 19.49775 15.78210 13.57910
## clone110_bio3 U2OS_bio1_r1 U2OS_bio1_r2 U2OS_bio2_r1 U2OS_bio2_r2
## 1 25.29080 29.7519 29.7474 27.5277 27.4484
## 2 16.60970 16.6569 26.4235 25.8362 26.7453
## 3 20.41325 25.9666 16.5276 20.9405 24.3227
## 4 19.35090 20.5323 18.4973 20.4094 19.9840
## 5 18.84445 21.2390 21.6044 20.3556 20.2299
## 6 17.42285 18.6407 18.7594 13.4979 16.8990
## U2OS_bio3_r1 U2OS_bio3_r2 clone110_bio1_r1 clone110_bio1_r2 clone110_bio2_r1
## 1 29.5324 29.8615 25.3294 26.8123 23.7861
## 2 25.1951 16.0821 22.0244 22.1890 27.0480
## 3 19.5059 26.3507 16.9629 19.4879 20.6540
## 4 20.2828 20.3025 17.0662 17.7067 16.7439
## 5 21.4416 21.2483 16.1087 18.8047 19.5359
## 6 19.6205 19.3750 15.4503 16.1139 13.4524
## clone110_bio2_r2 clone110_bio3_r1 clone110_bio3_r2 WT_Mean KO_Mean fc_log2
## 1 23.7273 25.4082 25.1734 28.97822 25.03945 3.938767
## 2 16.9594 15.8078 17.4116 22.82318 20.24003 2.583150
## 3 20.9314 20.7026 20.1239 22.26900 19.81045 2.458550
## 4 15.1022 19.5247 19.1771 20.00138 17.55347 2.447917
## 5 19.6040 18.8780 18.8109 21.01980 18.62370 2.396100
## 6 13.7058 17.6572 17.1885 17.79875 15.59468 2.204067
## Fold.Change pvalue_log SYMBOL Gene PSMs_bio3 Peptides_bio3
## 1 15.3350 3.8176 FUS FUS 111 18
## 2 5.9924 0.4427 GPD1 GPD1 NA NA
## 3 5.4966 0.7408 STUB1 STUB1 NA NA
## 4 5.4563 2.1095 DNAJB6 DNAJB6 NA NA
## 5 5.2638 2.7127 SCP2 SCP2 NA NA
## 6 4.6077 1.0360 PRR3 PRR3 NA NA
## Unique.Peptides_bio3 PSMs_bio2 Peptides_bio2 Unique.Peptides_bio2 PSMs_bio1
## 1 16 51 14 10 106
## 2 NA NA NA NA NA
## 3 NA NA NA NA NA
## 4 NA NA NA NA NA
## 5 NA NA NA NA NA
## 6 NA NA NA NA NA
## Peptides_bio1 Unique.Peptides_bio1 PSMs_ave Unique_ave
## 1 15 13 89.33333 13
## 2 NA NA NA NA
## 3 NA NA NA NA
## 4 NA NA NA NA
## 5 NA NA NA NA
## 6 NA NA NA NA
# write.csv(FUS_norm_x,file = "results/FUS_norm_x.csv")FUS_norm_x%>%
group_by(UNIPROT) %>% dplyr::filter(n()>1) %>% summarize(n=n())## # A tibble: 28 x 2
## UNIPROT n
## <chr> <int>
## 1 P0C0L4 2
## 2 P0C0L5 2
## 3 P22392 2
## 4 P31941 2
## 5 P35226 2
## 6 P47929 2
## 7 P49674 2
## 8 P62805 14
## 9 P63162 2
## 10 P68431 10
## # … with 18 more rows
res2 <- FUS_norm_x %>%
dplyr::select(SYMBOL, fc_log2) %>%
drop_na() %>%
distinct() %>%
group_by(SYMBOL) %>%
summarize(fc_log2=mean(fc_log2))
head(res2)## # A tibble: 6 x 2
## SYMBOL fc_log2
## <chr> <dbl>
## 1 AAAS -0.552
## 2 AACS -0.790
## 3 AAK1 -2.08
## 4 AAMP -0.782
## 5 AAR2 1.68
## 6 AARS -0.514
ranks <- deframe(res2)
head(ranks, 20)## AAAS AACS AAK1 AAMP AAR2 AARS AARSD1
## -0.5518333 -0.7899000 -2.0754000 -0.7820333 1.6765167 -0.5141000 -0.5699833
## AASDHPPT AATF ABCA13 ABCE1 ABCF1 ABCF2 ABCF3
## 0.2305167 0.1565833 -0.0794500 -0.4435667 -0.1688000 0.1522333 -0.7264667
## ABHD14B ABI1 ABLIM1 ABRAXAS2 ABT1 ACAA2
## -0.7919333 -0.8162167 -1.4589333 0.4161000 0.9545833 -1.4528167
barplot(sort(ranks, decreasing = T))
set.seed(12)
pathways.GO.BP <- gmtPathways("MSigDB/c5.bp.v6.2.symbols.gmt")
FUS_GSEA_BP<-fgsea(pathways=gmtPathways("MSigDB/c5.bp.v6.2.symbols.gmt"), ranks,
nperm=100000,
minSize = 15,
maxSize = 200
)%>%
arrange(padj)## Warning in fgsea(pathways = gmtPathways("MSigDB/c5.bp.v6.2.symbols.gmt"), : There are ties in the preranked stats (1.89% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
FUS_GSEA_BP_tab<-FUS_GSEA_BP%>%
as_tibble() %>%
arrange(desc(NES))%>%
dplyr::select(-ES,pval,-nMoreExtreme)%>%
dplyr::filter(NES >0)
as_tibble() %>%
arrange(padj)## # A tibble: 0 x 0
Go_BP_01<- FUS_GSEA_BP_tab %>%
dplyr::filter(padj <0.01)%>%
mutate(nLeadingEdge=lengths(leadingEdge),GeneRatio=lengths(leadingEdge)/size, term= substring(pathway,4))%>%
distinct()%>%
arrange(-NES)## Warning: distinct() does not fully support columns of type `list`.
## List elements are compared by reference, see ?distinct for details.
## This affects the following columns:
## - `leadingEdge`
# fwrite(Go_BP_01, file="results/Go_BP_01.txt", sep="\t", sep2=c("", " ", ""))Go_BP_01$log10_padj <- -log10(Go_BP_01$padj)go_dot<- ggplot(Go_BP_01, aes(NES,term))
go_dot+
geom_point(aes(color=padj, size=GeneRatio))+
scale_color_viridis()+
theme_bw()
graph2pdf(file="results/GSEA_GO_BP_All.pdf", width=10, aspectr=sqrt(2),font = "Arial",bg = "transparent")## Exported graph as results/GSEA_GO_BP_All.pdf
FUS_replication <- Go_BP_01 %>%
dplyr::filter(str_detect(term,"REPLICATION"))FUS_repair <- Go_BP_01 %>%
dplyr::filter(str_detect(term,"REPAIR"))library(export)
go_dot_replication<- ggplot(FUS_replication, aes(NES,term))
go_dot_replication+
geom_point(aes(color=padj, size=GeneRatio))+
scale_color_viridis_c()+#option="plasma","magma", "cividis","inferno","viridis"
theme_bw()
graph2pdf(file="results/DNA_replication.pdf", width=8, aspectr=sqrt(2),font = "Arial",bg = "transparent")## Exported graph as results/DNA_replication.pdf
go_dot_repair<- ggplot(FUS_repair, aes(NES,term))
go_dot_repair+
geom_point(aes(color=padj, size=GeneRatio))+
scale_color_viridis_c()+#option="plasma","magma", "cividis","inferno","viridis"
theme_bw()
graph2pdf(file="results/DNA_repair.pdf", width=8, aspectr=sqrt(2),font = "Arial",bg = "transparent")## Exported graph as results/DNA_repair.pdf
DNA_REPLICATION.Leading<-Go_BP_01 %>%
dplyr::filter(pathway == "GO_DNA_REPLICATION")%>%
dplyr::select(leadingEdge)%>%
unnest()%>%
dplyr::rename(SYMBOL=leadingEdge)%>%
inner_join(FUS_norm_x, by="SYMBOL")%>%
distinct()## Warning: `cols` is now required.
## Please use `cols = c(leadingEdge)`
DNA_REPLICATION.Leading.all<-DNA_REPLICATION.Leading%>%
dplyr::mutate(GO_Terms="DNA_REPLICATION")%>%
column_to_rownames(var ="SYMBOL")
### Filter genes based on padj<0.05
DNA_REPLICATION.Leading.sig <-DNA_REPLICATION.Leading %>%
dplyr::mutate(GO_Terms="DNA_REPLICATION")%>%
dplyr::filter(fc_log2>= 0.42)%>%
# dplyr::filter(WT_Mean >= 23.5)%>% ##(WT_Mean: 23.5 cutoff based on PCNA)
dplyr::filter(PSMs_ave >= 5)%>%
column_to_rownames(var ="SYMBOL")dna.replication.out.all<- pheatmap(DNA_REPLICATION.Leading.all[2:7],
color = inferno(12),
clustering_method = "ward.D2",
cluster_rows = T,
cluster_cols = F,
show_rownames =T ,
border_color = NA,
fontsize = 10,
scale = "row",
cutree_rows = 3,
cutree_cols = 3,
fontsize_row = 6,
height = 20)
mat<- DNA_REPLICATION.Leading.sig[2:7]
dna.replication.out.sig<- pheatmap(mat,
color = inferno(12), ## use color from viridis package: the value 10 represents 10 different colors; options: viridis, magma, plasma, inferno, cividis.
clustering_method = "ward.D2",
# mat = log2(mat),
cluster_rows = T,
cluster_cols = F,
show_rownames =T ,
border_color = NA,
fontsize = 10,
scale = "row",
cutree_cols = 3,
fontsize_row = 6,
main = "DNA Replication Related Proteins",
height = 20)
graph2pdf(file="results/DNA_replication_heatmap_sig.pdf", width=8, aspectr=sqrt(2),font = "Arial",bg = "transparent")## Exported graph as results/DNA_replication_heatmap_sig.pdf
DNA_REPAIR.Leading<-Go_BP_01 %>%
dplyr::filter(pathway == "GO_DNA_REPAIR")%>%
dplyr::select(leadingEdge)%>%
unnest()%>%
dplyr::rename(SYMBOL=leadingEdge)%>%
inner_join(FUS_norm_x, by="SYMBOL")%>%
distinct()## Warning: `cols` is now required.
## Please use `cols = c(leadingEdge)`
DNA_REPAIR.Leading.all<-DNA_REPAIR.Leading%>%
dplyr::mutate(GO_Terms="DNA_REPAIR")%>%
column_to_rownames(var ="SYMBOL")
### Filter genes based on padj<0.05
DNA_REPAIR.Leading.sig <-DNA_REPAIR.Leading %>%
dplyr::mutate(GO_Terms="DNA_REPAIR")%>%
dplyr::filter(fc_log2>= 0.42)%>%
dplyr::filter(PSMs_ave >= 5)%>% ##(WT_Mean: 23.5 cutoff based on PCNA)
column_to_rownames(var ="SYMBOL")dna.repair.out.sig<- pheatmap(DNA_REPAIR.Leading.sig[2:7],
color = inferno(12), ## use color from viridis package: the value 10 represents 10 different colors; options: viridis, magma, plasma, inferno, cividis.
clustering_method = "ward.D2",
cluster_rows = T,
cluster_cols = F,
show_rownames =T ,
border_color = NA,
fontsize = 10,
scale = "row",
cutree_cols = 3,
fontsize_row = 6,
main = "DNA Repair Related Proteins",
height = 20)
graph2pdf(file="results/DNA_repair_heatmap_sig.pdf", width=8, aspectr=sqrt(2),font = "Arial",bg = "transparent")## Exported graph as results/DNA_repair_heatmap_sig.pdf
plotEnrichment(pathways.GO.BP[["GO_DNA_REPLICATION"]],
ranks) + labs(title="GO_DNA_REPLICATION")
graph2pdf(file="results/GSEA_DNA_Replication.pdf", width=6, aspectr=sqrt(2),font = "Arial",bg = "transparent")## Exported graph as results/GSEA_DNA_Replication.pdf
plotEnrichment(pathways.GO.BP[["GO_DNA_REPAIR"]],
ranks) + labs(title="GO_DNA_REPAIR")
graph2pdf(file="results/GSEA_DNA_Repair.pdf", width=6, aspectr=sqrt(2),font = "Arial",bg = "transparent")## Exported graph as results/GSEA_DNA_Repair.pdf
plotEnrichment(pathways.GO.BP[["GO_SPLICEOSOMAL_COMPLEX_ASSEMBLY"]],
ranks) + labs(title="GO_SPLICEOSOMAL_COMPLEX_ASSEMBLY")
graph2pdf(file="results/GSEA_SPLICEOSOMAL_COMPLEX_ASSEMBLY.pdf", width=6, aspectr=sqrt(2),font = "Arial",bg = "transparent")## Exported graph as results/GSEA_SPLICEOSOMAL_COMPLEX_ASSEMBLY.pdf
DNA_Replication_Repair_sig <- bind_rows(DNA_REPLICATION.Leading.sig,DNA_REPAIR.Leading.sig)
DNA_Replication_Repair_sig<-dplyr::left_join(DNA_Replication_Repair_sig,UNIPORT2symbol, by =c("UNIPROT"="UNIPROT") )
ggscatter(DNA_Replication_Repair_sig, y ="pvalue_log", x= "Fold.Change",
color = "GO_Terms",
palette="npg",
# shape="GO_Terms",
# ellipse = TRUE,
# ellipse.type = "convex",
label = "SYMBOL",
repel = TRUE,
size = "Unique_ave",
alpha = 0.5,
xlab = "Fold Change(WT vs KO)",
ylab = "-Log10(p value)"
) +
xlim(1, 2.5)+
geom_vline(xintercept = 1.3, linetype="dotted",
color = "grey", size=1)+
geom_hline(yintercept=1.30103, linetype="dotted",
color = "grey", size=1)
graph2pdf(file="results/GSEA_Scatterplot.pdf", width=8, aspectr=sqrt(2),font = "Arial",bg = "transparent")## Exported graph as results/GSEA_Scatterplot.pdf
DNA_Replication_Repair_comb <- bind_rows(DNA_REPLICATION.Leading.sig,DNA_REPAIR.Leading.sig)%>%
dplyr::select(-GO_Terms)
DNA_Replication_Repair_comb<-dplyr::left_join(DNA_Replication_Repair_comb,UNIPORT2symbol, by =c("UNIPROT"="UNIPROT") )%>%
distinct()
ggscatter(DNA_Replication_Repair_comb, y ="pvalue_log", x= "Fold.Change",
palette="npg",
label = "SYMBOL",
repel = TRUE,
size = "Unique_ave",
alpha = 0.5,
xlab = "Fold Change(WT vs KO)",
ylab = "-Log10(p value)"
) +
xlim(1, 2.5)+
geom_vline(xintercept = 1.3, linetype="dotted",
color = "grey", size=1)+
geom_hline(yintercept=1.30103, linetype="dotted",
color = "grey", size=1)
graph2pdf(file="results/GSEA_Scatterplot.pdf", width=8, aspectr=sqrt(2),font = "Arial",bg = "transparent")## Exported graph as results/GSEA_Scatterplot.pdf
sessionInfo()## R version 3.6.3 (2020-02-29)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS Catalina 10.15.4
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] org.Hs.eg.db_3.8.2 AnnotationDbi_1.46.1 IRanges_2.18.3
## [4] S4Vectors_0.22.1 Biobase_2.44.0 BiocGenerics_0.30.0
## [7] DT_0.12 data.table_1.12.8 export_0.2.2
## [10] viridis_0.5.1 viridisLite_0.3.0 fgsea_1.10.1
## [13] Rcpp_1.0.4 pheatmap_1.0.12 ggpubr_0.2.5
## [16] magrittr_1.5 forcats_0.5.0 stringr_1.4.0
## [19] dplyr_0.8.5 purrr_0.3.3 readr_1.3.1
## [22] tidyr_1.0.2 tibble_2.1.3 ggplot2_3.3.0
## [25] tidyverse_1.3.0
##
## loaded via a namespace (and not attached):
## [1] colorspace_1.4-1 ggsignif_0.6.0 ellipsis_0.3.0
## [4] flextable_0.5.9 base64enc_0.1-3 fs_1.3.2
## [7] rstudioapi_0.11 farver_2.0.3 ggrepel_0.8.2
## [10] bit64_0.9-7 fansi_0.4.1 lubridate_1.7.4
## [13] xml2_1.2.5 knitr_1.28 jsonlite_1.6.1
## [16] broom_0.5.5 dbplyr_1.4.2 shiny_1.4.0.2
## [19] compiler_3.6.3 httr_1.4.1 backports_1.1.5
## [22] assertthat_0.2.1 Matrix_1.2-18 fastmap_1.0.1
## [25] cli_2.0.2 later_1.0.0 htmltools_0.4.0
## [28] tools_3.6.3 gtable_0.3.0 glue_1.3.2
## [31] fastmatch_1.1-0 cellranger_1.1.0 vctrs_0.2.4
## [34] nlme_3.1-145 stargazer_5.2.2 crosstalk_1.1.0.1
## [37] xfun_0.12 openxlsx_4.1.4 rvest_0.3.5
## [40] mime_0.9 miniUI_0.1.1.1 lifecycle_0.2.0
## [43] scales_1.1.0 hms_0.5.3 promises_1.1.0
## [46] RColorBrewer_1.1-2 yaml_2.2.1 memoise_1.1.0
## [49] gridExtra_2.3 gdtools_0.2.1 stringi_1.4.6
## [52] RSQLite_2.2.0 zip_2.0.4 BiocParallel_1.18.1
## [55] manipulateWidget_0.10.1 rlang_0.4.5 pkgconfig_2.0.3
## [58] systemfonts_0.1.1 rgl_0.100.50 evaluate_0.14
## [61] lattice_0.20-40 labeling_0.3 htmlwidgets_1.5.1
## [64] rvg_0.2.4 bit_1.1-15.2 tidyselect_1.0.0
## [67] ggsci_2.9 R6_2.4.1 generics_0.0.2
## [70] DBI_1.1.0 pillar_1.4.3 haven_2.2.0
## [73] withr_2.1.2 modelr_0.1.6 crayon_1.3.4
## [76] uuid_0.1-4 utf8_1.1.4 rmarkdown_2.1
## [79] officer_0.3.8 grid_3.6.3 readxl_1.3.1
## [82] blob_1.2.1 reprex_0.3.0 digest_0.6.25
## [85] webshot_0.5.2 xtable_1.8-4 httpuv_1.5.2
## [88] munsell_0.5.0