Skip to content

i.score

Jump to bottom
Alireza Khodadadi-Jamayran edited this page Nov 29, 2021 · 23 revisions

How to use i.score to rank/score the cells:

This data is from this publication (GEO number: GSE156246 and pubmed id: TBD (accepted in Cancer Discovery))

All the codes here are for reproducibility and a how to guide to run i.score function in iCellR.

Download the samples from here: https://genome.med.nyu.edu/results/external/iCellR/i.score/

Download gene signatures from here: https://genome.med.nyu.edu/results/external/iCellR/i.score/gene_signatures.tar.gz

# load sample gene signature in iCellR

library(iCellR)
G0 <- readLines(system.file('extdata', 'G0.txt', package = 'iCellR'))
G1S <- readLines(system.file('extdata', 'G1S.txt', package = 'iCellR'))
G2M <- readLines(system.file('extdata', 'G2M.txt', package = 'iCellR'))
M <- readLines(system.file('extdata', 'M.txt', package = 'iCellR'))
MG1 <- readLines(system.file('extdata', 'MG1.txt', package = 'iCellR'))
S <- readLines(system.file('extdata', 'S.txt', package = 'iCellR'))

# load all the gene signatures 

Melnick_10_GILMORE_CORE_NFKB_PATHWAY.txt <- readLines("10_GILMORE_CORE_NFKB_PATHWAY.txt")
Melnick_11_HALLMARK_MYC_TARGETS_V1.txt <- readLines("11_HALLMARK_MYC_TARGETS_V1.txt")
Melnick_12_GO_BETA_CATENIN_BINDING.txt <- readLines("12_GO_BETA_CATENIN_BINDING.txt")
Melnick_13_PID_BETA_CATENIN_NUC_PATHWAY.txt <- readLines("13_PID_BETA_CATENIN_NUC_PATHWAY.txt")
Melnick_14_PID_WNT_SIGNALING_PATHWAY.txt <- readLines("14_PID_WNT_SIGNALING_PATHWAY.txt")
Melnick_15_PID_WNT_CANONICAL_PATHWAY.txt <- readLines("15_PID_WNT_CANONICAL_PATHWAY.txt")
Melnick_16_Pribluda_SENESCENCE_INFLAMMATORY_GENES.txt <- readLines("16_Pribluda_SENESCENCE_INFLAMMATORY_GENES.txt")
Melnick_17_FRIDMAN_SENESCENCE_DN.txt <- readLines("17_FRIDMAN_SENESCENCE_DN.txt")
Melnick_18_FRIDMAN_SENESCENCE_UP.txt <- readLines("18_FRIDMAN_SENESCENCE_UP.txt")
Melnick_19_DeJONGE_LSC_TOP50_genes.txt <- readLines("19_DeJONGE_LSC_TOP50_genes.txt")
Melnick_1_AML1566_AraC_UP.txt <- readLines("1_AML1566_AraC_UP.txt")
Melnick_20_GAL_LEUKEMIC_STEM_CELL_UP.txt <- readLines("20_GAL_LEUKEMIC_STEM_CELL_UP.txt")
Melnick_21_GAL_LEUKEMIC_STEM_CELL_DN.txt <- readLines("21_GAL_LEUKEMIC_STEM_CELL_DN.txt")
Melnick_22_EPPERT_CE_HSC_LSC.txt <- readLines("22_EPPERT_CE_HSC_LSC.txt")
Melnick_23_JAATINEN_HEMATOPOIETIC_STEM_CELL_UP.txt <- readLines("23_JAATINEN_HEMATOPOIETIC_STEM_CELL_UP.txt")
Melnick_24_JAATINEN_HEMATOPOIETIC_STEM_CELL_DN.txt <- readLines("24_JAATINEN_HEMATOPOIETIC_STEM_CELL_DN.txt")
Melnick_25_INFLAMMATORY_RESPONSE.txt <- readLines("25_INFLAMMATORY_RESPONSE.txt")
Melnick_26_RAMALHO_STEMNESS_DN.txt <- readLines("26_RAMALHO_STEMNESS_DN.txt")
Melnick_27_RAMALHO_STEMNESS_UP.txt <- readLines("27_RAMALHO_STEMNESS_UP.txt")
Melnick_28_REACTOME_REGULATION_OF_MITOTIC_CELL_CYCLE.txt <- readLines("28_REACTOME_REGULATION_OF_MITOTIC_CELL_CYCLE.txt")
Melnick_2_AML1566_AraC_DN.txt <- readLines("2_AML1566_AraC_DN.txt")
Melnick_3_DUY_CISG_UP.txt <- readLines("3_DUY_CISG_UP.txt")
Melnick_4_DUY_CISG_DN.txt <- readLines("4_DUY_CISG_DN.txt")
Melnick_5_DIAPAUSE_UP_BOROVIAK.txt <- readLines("5_DIAPAUSE_UP_BOROVIAK.txt")
Melnick_6_BOROVIAK_DIAPAUSE_DN.txt <- readLines("6_BOROVIAK_DIAPAUSE_DN.txt")
Melnick_7_SASP_COPPE.txt <- readLines("7_SASP_COPPE.txt")
Melnick_8_SALDIVAR_ATR_SUPPRESSED_TARGETS.txt <- readLines("8_SALDIVAR_ATR_SUPPRESSED_TARGETS.txt")
Melnick_9_BIOCARTA_NFKB_PATHWAY.txt <- readLines("9_BIOCARTA_NFKB_PATHWAY.txt")
diapause_neg.txt <- readLines("diapause_neg.txt")
diapause_pos_and_neg.txt <- readLines("diapause_pos_and_neg.txt")
diapause_pos.txt <- readLines("diapause_pos.txt")
DTP_sig_150_Down.txt <- readLines("DTP_sig_150_Down.txt")
DTP_sig_150_up.txt <- readLines("DTP_sig_150_up.txt")
Lum_uniq_down.txt <- readLines("Lum_uniq_down.txt")
Lum_uniq_up.txt <- readLines("Lum_uniq_up.txt")
Mes_uniq_down.txt <- readLines("Mes_uniq_down.txt")
Mes_uniq_up.txt <- readLines("Mes_uniq_up.txt")
panDTP_DN.txt <- readLines("new_panDTP_DN.txt")
panDTP_up.txt <- readLines("new_panDTP_up.txt")
mes_DTP_included_DEG_DN.txt <- readLines("new_mes_DTP_included_DEG_DN.txt")
mes_DTP_included_DEG_UP.txt <- readLines("new_mes_DTP_included_DEG_UP.txt")
lum_DTP_included_DEG_DN.txt <- readLines("new_lum_DTP_included_DEG_DN.txt")
lum_DTP_included_DEG_UP.txt <- readLines("new_lum_DTP_included_DEG_UP.txt")
lum_DTP_specific_UP_noCC.txt <- readLines("new_lum_DTP_specific_UP_noCC_.txt")
mes_DTP_specific_UP_noCC.txt <- readLines("new_mes_DTP_specific_UP_noCC_.txt")

Group all the signatures in one character object:

All <- c("Melnick_10_GILMORE_CORE_NFKB_PATHWAY.txt","Melnick_11_HALLMARK_MYC_TARGETS_V1.txt","Melnick_12_GO_BETA_CATENIN_BINDING.txt","Melnick_13_PID_BETA_CATENIN_NUC_PATHWAY.txt","Melnick_14_PID_WNT_SIGNALING_PATHWAY.txt","Melnick_15_PID_WNT_CANONICAL_PATHWAY.txt","Melnick_16_Pribluda_SENESCENCE_INFLAMMATORY_GENES.txt","Melnick_17_FRIDMAN_SENESCENCE_DN.txt","Melnick_18_FRIDMAN_SENESCENCE_UP.txt","Melnick_19_DeJONGE_LSC_TOP50_genes.txt","Melnick_1_AML1566_AraC_UP.txt","Melnick_20_GAL_LEUKEMIC_STEM_CELL_UP.txt","Melnick_21_GAL_LEUKEMIC_STEM_CELL_DN.txt","Melnick_22_EPPERT_CE_HSC_LSC.txt","Melnick_23_JAATINEN_HEMATOPOIETIC_STEM_CELL_UP.txt","Melnick_24_JAATINEN_HEMATOPOIETIC_STEM_CELL_DN.txt","Melnick_25_INFLAMMATORY_RESPONSE.txt","Melnick_26_RAMALHO_STEMNESS_DN.txt","Melnick_27_RAMALHO_STEMNESS_UP.txt","Melnick_28_REACTOME_REGULATION_OF_MITOTIC_CELL_CYCLE.txt","Melnick_2_AML1566_AraC_DN.txt","Melnick_3_DUY_CISG_UP.txt","Melnick_4_DUY_CISG_DN.txt","Melnick_5_DIAPAUSE_UP_BOROVIAK.txt","Melnick_6_BOROVIAK_DIAPAUSE_DN.txt","Melnick_7_SASP_COPPE.txt","Melnick_8_SALDIVAR_ATR_SUPPRESSED_TARGETS.txt","Melnick_9_BIOCARTA_NFKB_PATHWAY.txt","diapause_neg.txt","diapause_pos_and_neg.txt","diapause_pos.txt","DTP_sig_150_Down.txt","DTP_sig_150_up.txt","Lum_uniq_down.txt","Lum_uniq_up.txt","Mes_uniq_down.txt","Mes_uniq_up.txt","G0","G1S","G2M","M","MG1","S","panDTP_DN.txt","panDTP_up.txt","mes_DTP_included_DEG_DN.txt","mes_DTP_included_DEG_UP.txt","lum_DTP_included_DEG_DN.txt","lum_DTP_included_DEG_UP.txt","lum_DTP_specific_UP_noCC.txt","mes_DTP_specific_UP_noCC.txt")

Load your sample iCellR object

load("BT474_DTP.Robj")

Score for cell cycle gene signatures

dat1 <- i.score(my.obj, scoring.List = c("G0","G1S","G2M","M","MG1","S") ,scoring.method = "tirosh",return.stats = TRUE, data.type = "raw.data")
write.table(dat1,"tirosh_G0.tsv",sep="\t")

Score for all the other signatures

dat2 <- i.score(my.obj, scoring.List = All ,scoring.method = "tirosh",return.stats = TRUE, data.type = "raw.data")
write.table(dat2,"tirosh_all.tsv",sep="\t")

Prepare data to plot

dir.create("boxplots_tirosh")
setwd("boxplots_tirosh")

data <- read.table("../tirosh_all.tsv",sep="\t",header=T)
dataCC <- read.table("../tirosh_G0.tsv",sep="\t",header=T)

df = as.character(dataCC$assignment.annotation) == "G0"
df[ df == "TRUE" ] <- "GO"
df[ df == "FALSE" ] <- "nonGO"

data <- cbind(cond = rep("sample",length(df)),
    ID = rownames(data),
    assignment.annotation = dataCC$assignment.annotation,
    GO_nonGO = df,
    data)

write.table((data),file="data.xls",sep="\t", row.names =F)

Plot all the signatures individually:

data <- read.table("data.xls",sep="\t",header=T)

g <- head(data)[5:55]
g <- colnames(g)

library(ggpubr)

for(i in g){
    name <- paste("boxplot_",i,".png",sep="")
    png(name,width = 6, height = 4, units = 'in', res = 300)
    print(ggplot(data, aes(x= GO_nonGO,y=data[, i],fill = GO_nonGO, alpha = 0.5)) +
    geom_jitter(size = 0.2, color="black") +
    geom_violin(trim=FALSE, col = "black", alpha = 0.5) +
    geom_boxplot(outlier.color = NA) +
    theme_bw() +
    xlab("Condition") +
    ylab("Signature Score") +
    scale_y_continuous(trans = "log1p") +
    stat_compare_means(aes(group = GO_nonGO), label = "p.signif", label.x = 1.5) +
    theme(axis.text.x = element_blank()))
    dev.off()
}

Example for "lum_DTP_included_DEG_DN.txt"

To see all the plots made as above go to this link: https://genome.med.nyu.edu/results/external/iCellR/i.score/test/boxplots_tirosh/

Clone this wiki locally