loading data
For each gene, fold-changes were determined using pooled counts. To report the biological effect of targeting each gene, we summarized the collective changes of 6 sgRNA per gene as a Z-score using the Wilcoxon rank-sum test. To determine statistical significance, a permutation p-value was calculated from the background distribution of Z-scores generated by random picks of 6 control sgRNAs out of 1,000 internal to the library. An example can be found here P-value and Z-score calculation.
library("ggplot2")
library("ggrepel")
library("VennDiagram")
library("pheatmap")
library("RColorBrewer")
library("scales")
load("DataSummary/Pooled_MA_Zscore.RData")
attach(Pooled_MA_Zscore)
head(FcM) ## sgRNA-level fold-change Pre_vs_Plasmid Vitro_vs_Plasmid Vivo_vs_Plasmid
MGLibA_00001 0.4368053 0.05901595 2.505846
MGLibA_00002 2.6068334 4.60182632 2.305935
MGLibA_00003 -4.2028304 -8.34872815 -8.348728
MGLibA_00004 -1.2025237 -0.32075123 1.952999
MGLibA_00005 -0.6230805 0.43797214 -2.463307
MGLibA_00006 -0.2139968 0.57372911 1.658612
Vitro_vs_Pre Vivo_vs_Pre
MGLibA_00001 -0.3777894 2.0690406
MGLibA_00002 1.9949929 -0.3008987
MGLibA_00003 -4.1458977 -4.1458977
MGLibA_00004 0.8817725 3.1555224
MGLibA_00005 1.0610526 -1.8402268
MGLibA_00006 0.7877259 1.8726090head(zM) ## gene-level Z-score Pre_vs_Plasmid Vitro_vs_Plasmid Vivo_vs_Plasmid
0610007P14Rik 0.2109059 -0.3610930 -0.3542764
0610009B22Rik -0.8923795 0.7196534 -0.5768965
0610009D07Rik -3.0760999 -3.7051443 -3.1719717
0610009O20Rik 0.1873497 0.9600445 0.3275840
0610010F05Rik -0.7796139 -0.7064171 -0.2322089
0610010K14Rik -1.3236388 0.1289626 -1.3674298
Vitro_vs_Pre Vivo_vs_Pre
0610007P14Rik -0.9686669 -1.1504427
0610009B22Rik 1.6677904 -0.4077684
0610009D07Rik -1.3361115 -0.7648521
0610009O20Rik 1.0606361 -0.1928692
0610010F05Rik 0.2572793 0.8005765
0610010K14Rik 0.8035464 -0.4290117head(pM) ## gene-level permutation p-value Pre_vs_Plasmid Vitro_vs_Plasmid Vivo_vs_Plasmid
0610007P14Rik 0.8388 0.7270 0.7246
0610009B22Rik 0.3762 0.4864 0.5706
0610009D07Rik 0.0020 0.0004 0.0012
0610009O20Rik 0.8560 0.3498 0.7514
0610010F05Rik 0.4390 0.4884 0.8100
0610010K14Rik 0.1916 0.9120 0.1762
Vitro_vs_Pre Vivo_vs_Pre
0610007P14Rik 0.3460 0.2644
0610009B22Rik 0.0892 0.6910
0610009D07Rik 0.1890 0.4548
0610009O20Rik 0.3034 0.8386
0610010F05Rik 0.8064 0.4336
0610010K14Rik 0.4416 0.6766Quality control
As shown below, we could identify genes known to be essential for bone marrow homing (Cxcr4) or integrin signaling (Fermt3 and Tln1) as only depleted in the in vivo arm of the screen. In contrast, known mTORC1 regulators Pten and Tsc1 were significantly enriched and known essential genes such as Myc and Myb were significantly depleted (permutation p-value < 0.01), in both the in vitro and in vivo arms.
MA cell line
fc = FcM[, 2]
aT_MA_vitro = aggregate(fc, by = list(factor(annT$Plate)), FUN="median")
colnames(aT_MA_vitro) = c("ID", "log2FC")
rownames(aT_MA_vitro) = as.character(aT_MA_vitro[,1])
Kgenes = c("Myc","Myb","Hmgcs1","Fermt3","Cxcr4","Pten", "Tsc1", "Tsc2", "Tln1")
gene = rownames(zM)
mT_MA_vitro = data.frame(gene, log2FC = as.numeric(aT_MA_vitro[gene, 2]), score = zM[gene, 2], pvalue = -log10(pM[gene, 2]))
mT1 = filter(mT_MA_vitro, pvalue > -log10(0.05))
mT2 = filter(mT_MA_vitro, pvalue < -log10(0.05))
mT3 = filter(mT_MA_vitro, gene %in% Kgenes)
p <- ggplot(mT2, aes(x = log2FC, y = pvalue))
p <- p + theme_bw() + labs(x = "Log2 fold-change", y = "-log10 Permutation P-value", title = "In vitro hits (MA)") + xlim(-10,10)
p <- p + theme(plot.title = element_text(hjust = 0.5)) + geom_point(size = 0.1, color = "grey")
p <- p + geom_point(data = mT1, aes(x = log2FC, y = pvalue), color = "#99cfe0", size = 0.2)
p <- p + geom_point(data = mT3, aes(x = log2FC, y = pvalue), color = "red", size = 0.4)
p <- p + geom_text_repel(data = mT3, aes(x = log2FC, y = pvalue, label = gene), size = 4,
box.padding = unit(0.35, "lines"), point.padding = unit(0.3, "lines"), segment.size = 0.2, color = "black", nudge_y = -0.1)
print(p)
fc = FcM[, 3]
aT_MA_vivo = aggregate(fc, by = list(factor(annT$Plate)), FUN="median")
colnames(aT_MA_vivo) = c("ID", "log2FC")
rownames(aT_MA_vivo) = as.character(aT_MA_vivo[,1])
Kgenes = c("Myc","Myb","Hmgcs1","Fermt3","Cxcr4","Pten", "Tsc1", "Tsc2", "Tln1")
gene = rownames(zM)
mT_MA_vivo = data.frame(gene, log2FC = as.numeric(aT_MA_vivo[gene, 2]), score = zM[gene, 3], pvalue = -log10(pM[gene, 3]))
mT1 = filter(mT_MA_vivo, pvalue > -log10(0.05))
mT2 = filter(mT_MA_vivo, pvalue < -log10(0.05))
mT3 = filter(mT_MA_vivo, gene %in% Kgenes)
p <- ggplot(mT2, aes(x = log2FC, y = pvalue))
p <- p + theme_bw() + labs(x = "Log2FC", y = "-log10 Permutation P-value", title = "In vivo hits (MA)") + xlim(-10,10)
p <- p + theme(plot.title = element_text(hjust = 0.5)) + geom_point(size = 0.1, color = "grey")
p <- p + geom_point(data = mT1, aes(x = log2FC, y = pvalue), color = "#99cfe0", size = 0.2)
p <- p + geom_point(data = mT3, aes(x = log2FC, y = pvalue), color = "red", size = 0.4)
p <- p + geom_text_repel(data = mT3, aes(x = log2FC, y = pvalue, label = gene), size = 4,
box.padding = unit(0.35, "lines"), point.padding = unit(0.3, "lines"),
segment.size = 0.2, color = "black", nudge_y = -0.1)
print(p)
HM cell line
fc = FcM[, 2]
aT_HM_vitro = aggregate(fc, by = list(factor(annT$Plate)), FUN="median")
colnames(aT_HM_vitro) = c("ID", "log2FC")
rownames(aT_HM_vitro) = as.character(aT_HM_vitro[,1])
Kgenes = c("Myc","Myb","Hmgcs1","Fermt3","Cxcr4","Pten", "Tsc1", "Tsc2", "Tln1")
gene = rownames(zM)
mT_HM_vitro = data.frame(gene, log2FC = as.numeric(aT_HM_vitro[gene, 2]), score = zM[gene, 2], pvalue = -log10(pM[gene, 2]))
mT1 = filter(mT_HM_vitro, pvalue > -log10(0.05))
mT2 = filter(mT_HM_vitro, pvalue < -log10(0.05))
mT3 = filter(mT_HM_vitro, gene %in% Kgenes)
p <- ggplot(mT2, aes(x = log2FC, y = pvalue))
p <- p + theme_bw() + labs(x = "Log2 fold-change", y = "-log10 Permutation P-value", title = "In vitro hits (HM)") + xlim(-10,10)
p <- p + theme(plot.title = element_text(hjust = 0.5)) + geom_point(size = 0.1, color = "grey")
p <- p + geom_point(data = mT1, aes(x = log2FC, y = pvalue), color = "#99cfe0", size = 0.2)
p <- p + geom_point(data = mT3, aes(x = log2FC, y = pvalue), color = "red", size = 0.4)
p <- p + geom_text_repel(data = mT3, aes(x = log2FC, y = pvalue, label = gene), size = 4,
box.padding = unit(0.35, "lines"), point.padding = unit(0.3, "lines"), segment.size = 0.2, color = "black", nudge_y = -0.1)
print(p)
fc = FcM[, 3]
aT_HM_vivo = aggregate(fc, by = list(factor(annT$Plate)), FUN="median")
colnames(aT_HM_vivo) = c("ID", "log2FC")
rownames(aT_HM_vivo) = as.character(aT_HM_vivo[,1])
Kgenes = c("Myc","Myb","Hmgcs1","Fermt3","Cxcr4","Pten", "Tsc1", "Tsc2", "Tln1")
gene = rownames(zM)
mT_HM_vivo = data.frame(gene, log2FC = as.numeric(aT_HM_vivo[gene, 2]), score = zM[gene, 3], pvalue = -log10(pM[gene, 3]))
mT1 = filter(mT_HM_vivo, pvalue > -log10(0.05))
mT2 = filter(mT_HM_vivo, pvalue < -log10(0.05))
mT3 = filter(mT_HM_vivo, gene %in% Kgenes)
p <- ggplot(mT2, aes(x = log2FC, y = pvalue))
p <- p + theme_bw() + labs(x = "Log2FC", y = "-log10 Permutation P-value", title = "In vivo hits (HM)") + xlim(-10,10)
p <- p + theme(plot.title = element_text(hjust = 0.5)) + geom_point(size = 0.1, color = "grey")
p <- p + geom_point(data = mT1, aes(x = log2FC, y = pvalue), color = "#99cfe0", size = 0.2)
p <- p + geom_point(data = mT3, aes(x = log2FC, y = pvalue), color = "red", size = 0.4)
p <- p + geom_text_repel(data = mT3, aes(x = log2FC, y = pvalue, label = gene), size = 4,
box.padding = unit(0.35, "lines"), point.padding = unit(0.3, "lines"),
segment.size = 0.2, color = "black", nudge_y = -0.1)
print(p)
Venn diagram
rm(list = ls())
load("DataSummary/CRISPR_Public.RData")
load("DataSummary/WG_Deplected.RData")
ma_vitro = maList[["Vitro_vs_Plasmid"]]
ma_vivo = maList[["Vivo_vs_Pre"]]
hm_vitro = hmList[["Vitro_vs_Plasmid"]]
hm_vivo = hmList[["Vivo_vs_Pre"]]
xGene = unique(c(ma_vitro, ma_vivo, hm_vitro, hm_vivo))
mm = cbind(ma_vitro = xGene %in% ma_vitro,
hm_vitro = xGene %in% hm_vitro,
ma_vivo = xGene %in% ma_vivo,
hm_vivo = xGene %in% hm_vivo) + 0
vitro = rowSums(mm[, c(1,2)])
vivo = rowSums(mm[, c(3,4)])
group = rep("shared", length(xGene))
group[vitro == 0] = "vivo_only"
group[vivo == 0] = "vitro_only"
oTable = data.frame(xGene = paste("Mm", xGene), mm, group)
write.table(oTable, file = "out/WG_Venn.txt", row.names = FALSE, col.names = TRUE, sep = "\t")
groupColor = c("#08519c", "#ce1256", "#54278f", "#006d2c")
venn.plot <- draw.quad.venn(
area1 = sum(mm[,1]),
area2 = sum(mm[,2]),
area3 = sum(mm[,3]),
area4 = sum(mm[,4]),
n12 = sum(rowSums(mm[, c(1,2)]) == 2),
n13 = sum(rowSums(mm[, c(1,3)]) == 2),
n14 = sum(rowSums(mm[, c(1,4)]) == 2),
n23 = sum(rowSums(mm[, c(2,3)]) == 2),
n24 = sum(rowSums(mm[, c(2,4)]) == 2),
n34 = sum(rowSums(mm[, c(3,4)]) == 2),
n123 = sum(rowSums(mm[, c(1,2,3)]) == 3),
n124 = sum(rowSums(mm[, c(1,2,4)]) == 3),
n134 = sum(rowSums(mm[, c(1,3,4)]) == 3),
n234 = sum(rowSums(mm[, c(2,3,4)]) == 3),
n1234 = sum(rowSums(mm[, c(1,2,3,4)]) == 4),
category = colnames(mm),
fill = c("white", "white", "white", "white"),
lty = "solid",
lwd = 3,
cex = 2,
cat.cex = 1,
col = groupColor,
cat.col = groupColor,
euler = TRUE)
Comparison in vivo/in vitro
rm(list = ls())
load("DataSummary/Pooled_MA_Zscore.RData")
attach(Pooled_MA_Zscore)
fc = FcM[, "Vitro_vs_Plasmid"]
aT_MA_vitro = aggregate(fc, by = list(factor(annT$Plate)), FUN="median")
colnames(aT_MA_vitro) = c("ID", "log2FC")
rownames(aT_MA_vitro) = as.character(aT_MA_vitro[,1])
fc = FcM[, "Vivo_vs_Pre"]
aT_MA_vivo = aggregate(fc, by = list(factor(annT$Plate)), FUN="median")
colnames(aT_MA_vivo) = c("ID", "log2FC")
rownames(aT_MA_vivo) = as.character(aT_MA_vivo[,1])
load("DataSummary/Pooled_HM_Zscore.RData")
attach(Pooled_HM_Zscore)
fc = FcM[, "Vitro_vs_Plasmid"]
aT_HM_vitro = aggregate(fc, by = list(factor(annT$Plate)), FUN="median")
colnames(aT_HM_vitro) = c("ID", "log2FC")
rownames(aT_HM_vitro) = as.character(aT_HM_vitro[,1])
fc = FcM[, "Vivo_vs_Pre"]
aT_HM_vivo = aggregate(fc, by = list(factor(annT$Plate)), FUN="median")
colnames(aT_HM_vivo) = c("ID", "log2FC")
rownames(aT_HM_vivo) = as.character(aT_HM_vivo[,1])
gene = rownames(zM)
Kgenes = c("Fermt3","Cxcr4","Pten", "Tsc1", "Tsc2", "Tln1")
load("DataSummary/WG_Deplected.RData")
ma_vitro = maList[["Vitro_vs_Plasmid"]]
ma_vivo = maList[["Vivo_vs_Pre"]]
hm_vitro = hmList[["Vitro_vs_Plasmid"]]
hm_vivo = hmList[["Vivo_vs_Pre"]]
ma_vivo_only = setdiff(ma_vivo, ma_vitro)
hm_vivo_only = setdiff(hm_vivo, hm_vitro)
wgTable = read.table(file = "out/WG_Venn.txt", row.names = NULL, header = TRUE)
vivo = gsub("Mm ", "", as.character(filter(wgTable, group == "vivo_only")$xGene))
vitro = gsub("Mm ", "", as.character(filter(wgTable, group == "vitro_only")$xGene))
mT = data.frame(gene,
log2FC_HM_vivo = as.numeric(aT_HM_vivo[gene, 2]),
log2FC_HM_vitro = as.numeric(aT_HM_vitro[gene, 2]),
log2FC_MA_vivo = as.numeric(aT_MA_vivo[gene, 2]),
log2FC_MA_vitro = as.numeric(aT_MA_vitro[gene, 2])
)
mT1 = filter(mT, gene %in% vivo)
mT2 = filter(mT, gene %in% vitro)
mT3 = filter(mT, gene %in% Kgenes)
mT4 = filter(mT, gene %in% ma_vivo_only)
mT5 = filter(mT, gene %in% hm_vivo_only)
##HM in vivo vs in vitro
p <- ggplot(mT, aes(x = log2FC_HM_vivo, y = log2FC_HM_vitro))
p <- p + theme_bw() + labs(x = "Log2FC in vivo", y = "Log2FC in vitro", title = "HM") + xlim(-10,10)
p <- p + theme(plot.title = element_text(hjust = 0.5)) + geom_point(size = 0.1, color = "grey")
p <- p + geom_point(data = mT5, aes(x = log2FC_HM_vivo, y = log2FC_HM_vitro), color = "#228B22", size = 0.2)
p <- p + geom_point(data = mT3, aes(x = log2FC_HM_vivo, y = log2FC_HM_vitro), color = "red", size = 0.4)
p <- p + geom_text_repel(data = mT3, aes(x = log2FC_HM_vivo, y = log2FC_HM_vitro, label = gene), size = 4,
box.padding = unit(0.35, "lines"), point.padding = unit(0.3, "lines"),
segment.size = 0.2, color = "black")
print(p)
##MA in vivo vs in vitro
p2 <- ggplot(mT, aes(x = log2FC_MA_vivo, y = log2FC_MA_vitro))
p2 <- p2 + theme_bw() + labs(x = "Log2FC in vivo", y = "Log2FC in vitro", title = "MA") + xlim(-10,10)
p2 <- p2 + theme(plot.title = element_text(hjust = 0.5)) + geom_point(size = 0.1, color = "grey")
p2 <- p2 + geom_point(data = mT4, aes(x = log2FC_MA_vivo, y = log2FC_MA_vitro), color = "#228B22", size = 0.2)
p2 <- p2 + geom_point(data = mT3, aes(x = log2FC_MA_vivo, y = log2FC_MA_vitro), color = "red", size = 0.4)
p2 <- p2 + geom_text_repel(data = mT3, aes(x = log2FC_MA_vivo, y = log2FC_MA_vitro, label = gene), size = 4,
box.padding = unit(0.35, "lines"), point.padding = unit(0.3, "lines"),
segment.size = 0.2, color = "black")
print(p2)
##In vivo
p3 <- ggplot(mT, aes(x = log2FC_HM_vivo, y = log2FC_MA_vivo))
p3 <- p3 + theme_bw() + labs(x = "Log2FC (HM)", y = "Log2FC (MA)", title = "In vivo") + xlim(-10,10)
p3 <- p3 + theme(plot.title = element_text(hjust = 0.5)) + geom_point(size = 0.1, color = "grey")
p3 <- p3 + geom_point(data = mT1, aes(x = log2FC_HM_vivo, y = log2FC_MA_vivo), color = "#3182bd", size = 0.2)
p3 <- p3 + geom_point(data = mT3, aes(x = log2FC_HM_vivo, y = log2FC_MA_vivo), color = "red", size = 0.4)
p3 <- p3 + geom_text_repel(data = mT3, aes(x = log2FC_HM_vivo, y = log2FC_MA_vivo, label = gene), size = 4,
box.padding = unit(0.35, "lines"), point.padding = unit(0.3, "lines"),
segment.size = 0.2, color = "black")
print(p3)
##In vitro
p4 <- ggplot(mT, aes(x = log2FC_HM_vitro, y = log2FC_MA_vitro))
p4 <- p4 + theme_bw() + labs(x = "HM", y = "MA", title = "In vitro") + xlim(-10,10)
p4 <- p4 + theme(plot.title = element_text(hjust = 0.5)) + geom_point(size = 0.1, color = "grey")
p4 <- p4 + geom_point(data = mT2, aes(x = log2FC_HM_vitro, y = log2FC_MA_vitro), color = "#3182bd", size = 0.2)
p4 <- p4 + geom_point(data = mT3, aes(x = log2FC_HM_vitro, y = log2FC_MA_vitro), color = "red", size = 0.4)
p4 <- p4 + geom_text_repel(data = mT3, aes(x = log2FC_HM_vitro, y = log2FC_MA_vitro, label = gene), size = 4,
box.padding = unit(0.35, "lines"), point.padding = unit(0.3, "lines"),
segment.size = 0.2, color = "black")
print(p4)
Compared to public data
rm(list = ls())
load("DataSummary/CRISPR_Public.RData")
load("DataSummary/WG_Deplected.RData")
load("DataSummary/MSigDB.RData")
ma_vitro = maList_HS[["Vitro_vs_Plasmid"]]
ma_vivo = maList_HS[["Vivo_vs_Pre"]]
hm_vitro = hmList_HS[["Vitro_vs_Plasmid"]]
hm_vivo = hmList_HS[["Vivo_vs_Pre"]]
xGene = unique(c(ma_vitro, ma_vivo, hm_vitro, hm_vivo))
xGene = intersect(xGene, rownames(CRISPR_Public))
mm = cbind(ma_vitro = xGene %in% ma_vitro,
hm_vitro = xGene %in% hm_vitro,
ma_vivo = xGene %in% ma_vivo,
hm_vivo = xGene %in% hm_vivo) + 0
vitro = rowSums(mm[, c(1,2)])
vivo = rowSums(mm[, c(3,4)])
group = rep("shared", length(xGene))
group[vitro == 0] = "vivo_only"
group[vivo == 0] = "vitro_only"
humanTable = data.frame(gene = xGene, mm, group)
subM = CRISPR_Public[xGene, 1:20]
mergedT = data.frame(mm, subM, group)
mT1 = mergedT[mergedT$group == "vitro_only", ]
mT1 = mT1[order(rowSums(as.matrix(mT1[, -25]))), ]
mT2 = mergedT[mergedT$group == "shared", ]
mT2 = mT2[order(rowSums(as.matrix(mT2[, -25]))), ]
mT3 = mergedT[mergedT$group == "vivo_only", ]
mT3 = mT3[order(rowSums(as.matrix(mT3[, -25]))), ]
reT = rbind(mT1, mT2, mT3)
reGroup = reT$group
RS_Index = rownames(reT) %in% MSigDB$MSigDB_c2_cp_kegg$KEGG_RIBOSOME
RIBOSOME = rep("NO", length(RS_Index))
RIBOSOME[RS_Index] = "YES"
annotation_row = data.frame(reGroup, RIBOSOME)
rownames(annotation_row) = rownames(reT)
reM = as.matrix(reT[, -25])
ann_colors = list(reGroup = c(vitro_only = "#deebf7", shared = "#9ecae1", vivo_only = "#3182bd"),
RIBOSOME = c(YES = "black", NO = "white"))
pheatmap(reM,
main = "",
annotation_row = annotation_row,
annotation_colors = ann_colors,
border_color = "grey60", color = c("#f5f5f5","#f5f5f5","#dd1c77"),
legend_breaks = c(-1,0,1),
legend_labels = c("NC", "NC", "Depleted"),
fontsize_row = 6,
show_rownames = FALSE, show_colnames = TRUE,
cluster_rows = FALSE, cluster_cols = FALSE)
Let’s check what’s the fraction of genes that have been previously identified in each of the three groups.
FALSE TRUE
shared 65 435
vitro_only 341 1157
vivo_only 266 155We observed that in vitro-only and shared gene sets were largely represented in the previously published studies on AML (PMID:27760321, PMID:28162770 and PMID:29478914). In contrast, only a small fraction of in vivo-only genes has been previously identified.
Pathway enrichment
Details can be found here Pathway enrichment.
Essential genes
Different researchers may define essential genes as different gene sets. Here compared our CRISPR hits to three sets:
- Essential in culture: Genes deemed essential in multiple cultured cell lines based on shRNA screen data.
- Essential in culture: Genes deemed essential in multiple cultured cell lines based on CRISPR/Cas screen data.
- Essential in mice: Genes where homozygous knockout in mice results in pre-, peri- or post-natal lethality. The mouse phenotypes were reported by Jackson Labs.
These gene sets were curated by Mark Daly’s group at MHG.
core = as.character(read.table(file = "DataSummary/gene_list/core_essentials_hart.tsv", row.names = NULL, header = FALSE, sep = "\t")[,1])
culture = as.character(read.table(file = "DataSummary/gene_list/CEGv2_subset_universe.tsv", row.names = NULL, header = FALSE, sep = "\t")[,1])
mice = as.character(read.table(file = "DataSummary/gene_list/mgi_essential.tsv", row.names = NULL, header = FALSE, sep = "\t")[,1])
humanTable$core = humanTable$gene %in% core
humanTable$culture = humanTable$gene %in% culture
humanTable$mice = humanTable$gene %in% mice
table(humanTable$group, humanTable$core)
FALSE TRUE
shared 457 43
vitro_only 1360 138
vivo_only 417 4table(humanTable$group, humanTable$culture)
FALSE TRUE
shared 381 119
vitro_only 1096 402
vivo_only 410 11table(humanTable$group, humanTable$mice)
FALSE TRUE
shared 379 121
vitro_only 1188 310
vivo_only 332 89So regardless of the definition of essential genes, the fraction of essential genes in shared/in vitro hits is almost 10 times higher than that in in vivo.
Output
oTable = read.table(file = "out/WG_Venn.txt", row.names = 1, header = TRUE, sep = "\t")
knitr::kable(oTable[1:5, ])| ma_vitro | hm_vitro | ma_vivo | hm_vivo | group | |
|---|---|---|---|---|---|
| Mm 0610009D07Rik | 1 | 0 | 0 | 0 | vitro_only |
| Mm 1110004E09Rik | 1 | 1 | 0 | 0 | vitro_only |
| Mm 1110008L16Rik | 1 | 0 | 1 | 0 | shared |
| Mm 1110037F02Rik | 1 | 0 | 0 | 0 | vitro_only |
| Mm 1200014J11Rik | 1 | 0 | 0 | 0 | vitro_only |
shared vitro_only vivo_only
500 1498 421 You can download the results of genome-wide CRISPR screening here Genome-wide_screen_Zscore_pvalue.xlsx.
sessionInfo
R version 4.1.0 (2021-05-18)
Platform: x86_64-conda-linux-gnu (64-bit)
Running under: Red Hat Enterprise Linux Server 7.6 (Maipo)
Matrix products: default
BLAS/LAPACK: /sibcb2/bioinformatics/software/Miniconda3/lib/libopenblasp-r0.3.15.so
locale:
[1] C
attached base packages:
[1] grid stats graphics grDevices utils datasets
[7] methods base
other attached packages:
[1] dplyr_1.1.2 scales_1.2.0 RColorBrewer_1.1-3
[4] pheatmap_1.0.12 VennDiagram_1.6.20 futile.logger_1.4.3
[7] ggrepel_0.9.1 ggplot2_3.4.2 rmarkdown_2.20
loaded via a namespace (and not attached):
[1] Rcpp_1.0.9 highr_0.9 jquerylib_0.1.4
[4] bslib_0.2.5.1 pillar_1.9.0 compiler_4.1.0
[7] formatR_1.11 futile.options_1.0.1 tools_4.1.0
[10] downlit_0.4.2 digest_0.6.29 jsonlite_1.8.7
[13] memoise_2.0.1 evaluate_0.20 lifecycle_1.0.3
[16] tibble_3.2.1 gtable_0.3.0 pkgconfig_2.0.3
[19] rlang_1.1.1 rstudioapi_0.15.0 cli_3.6.1
[22] distill_1.5 yaml_2.3.5 xfun_0.37
[25] fastmap_1.1.0 withr_2.5.0 knitr_1.42
[28] sass_0.4.0 generics_0.1.3 vctrs_0.6.3
[31] tidyselect_1.2.1 glue_1.6.2 R6_2.5.1
[34] fansi_1.0.3 bookdown_0.33 farver_2.1.1
[37] lambda.r_1.2.4 magrittr_2.0.3 htmltools_0.5.2
[40] colorspace_2.0-3 labeling_0.4.2 utf8_1.2.2
[43] munsell_0.5.0 cachem_1.0.6