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v0.3.2
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David Cohen committed Jul 8, 2021
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2 changes: 1 addition & 1 deletion README.md
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## **V**isualiz**IRR** (**V**isualized **I**mmune **R**epertoire **R**eport)

### v0.2.0
### v0.3.2

An in-browser immune repertoire report, incorporating popular web development libraries, including jQuery, Bootstrap, and plotly.js, in order to make immune repertoire analysis results simple to navigate and understand for the end user on their local machine or on a server.
These reports are structured to dynamically display the results of whatever cohort you run TCR/BCR analysis on.
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186 changes: 93 additions & 93 deletions r/immuneRepProcess.R
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Expand Up @@ -772,107 +772,107 @@ if (sample_level_run == TRUE || intracohort_run == TRUE || db_run == TRUE) {

if (nrow(chain_table_div) > 0){

if (input_format == "RHTCRSEQ" || cdr3nt_skip == TRUE){
chain_table_cdr3 <- chain_table_div[c("read_fragment_count", "CDR3_AA")]
names(chain_table_cdr3)[names(chain_table_cdr3) == 'CDR3_AA'] <- 'CDR3'
} else {
chain_table_cdr3 <- chain_table_div[c("read_fragment_count", "CDR3")]
}

chain_table_cdr3 <- dplyr::filter(chain_table_cdr3, !grepl("\\_",CDR3))
chain_table_cdr3 <- dplyr::filter(chain_table_cdr3, !grepl("\\?",CDR3))

if (nrow(chain_table_cdr3) != 1){
chain_table_cdr3$CDR3 <-
data.frame(names = chain_table_cdr3$CDR3,
chr = apply(chain_table_cdr3, 2, nchar))$chr.CDR3
} else {
chain_table_cdr3$CDR3 <- nchar(as.character(chain_table_cdr3$CDR3))
}

names(chain_table_div)[names(chain_table_div) == 'read_fragment_count'] <- 'Clones'
names(chain_table_div)[names(chain_table_div) == 'read_fragment_freq'] <- 'Proportion'
names(chain_table_div)[names(chain_table_div) == 'CDR3'] <- 'CDR3.nt'
names(chain_table_div)[names(chain_table_div) == 'CDR3_AA'] <- 'CDR3.aa'
names(chain_table_div)[names(chain_table_div) == 'V_gene'] <- 'J.name'
names(chain_table_div)[names(chain_table_div) == 'J_gene'] <- 'V.name'

if (length(unique(chain_table_cdr3$CDR3)) != 1) {
if (input_format == "RHTCRSEQ" || cdr3nt_skip == TRUE){
chain_table_cdr3 <- chain_table_div[c("read_fragment_count", "CDR3_AA")]
names(chain_table_cdr3)[names(chain_table_cdr3) == 'CDR3_AA'] <- 'CDR3'
} else {
chain_table_cdr3 <- chain_table_div[c("read_fragment_count", "CDR3")]
}

chain_table_cdr3 <- dplyr::filter(chain_table_cdr3, !grepl("\\_",CDR3))
chain_table_cdr3 <- dplyr::filter(chain_table_cdr3, !grepl("\\?",CDR3))

if (nrow(chain_table_cdr3) != 1){
chain_table_cdr3 <-
aggregate(
chain_table_cdr3$read_fragment_count,
by = list(Category = chain_table_cdr3$CDR3),
FUN = sum
)
chain_table_cdr3$CDR3 <-
data.frame(names = chain_table_cdr3$CDR3,
chr = apply(chain_table_cdr3, 2, nchar))$chr.CDR3
} else {
chain_table_cdr3$CDR3 <- nchar(as.character(chain_table_cdr3$CDR3))
}
}

total_count <- 0
cdr3_aggregate <- 0

if ((nrow(chain_table_cdr3) != 1) && (length(unique(chain_table_cdr3$CDR3)) != 1)){
for (i in 1:nrow(chain_table_cdr3)) {
total_count <- total_count + chain_table_cdr3[i, 2]
cdr3_aggregate <- cdr3_aggregate + (chain_table_cdr3[i, 1]*chain_table_cdr3[i, 2])

names(chain_table_div)[names(chain_table_div) == 'read_fragment_count'] <- 'Clones'
names(chain_table_div)[names(chain_table_div) == 'read_fragment_freq'] <- 'Proportion'
names(chain_table_div)[names(chain_table_div) == 'CDR3'] <- 'CDR3.nt'
names(chain_table_div)[names(chain_table_div) == 'CDR3_AA'] <- 'CDR3.aa'
names(chain_table_div)[names(chain_table_div) == 'V_gene'] <- 'J.name'
names(chain_table_div)[names(chain_table_div) == 'J_gene'] <- 'V.name'

if (length(unique(chain_table_cdr3$CDR3)) != 1) {
if (nrow(chain_table_cdr3) != 1){
chain_table_cdr3 <-
aggregate(
chain_table_cdr3$read_fragment_count,
by = list(Category = chain_table_cdr3$CDR3),
FUN = sum
)
}
}
}

suppressMessages(intracohort_values[1, 3] <- round(repDiversity(chain_table_div, "div", process_col),4))
if (nrow(chain_table_cdr3) != 1){

ent_range <- aggregate(. ~ CDR3.aa, data=chain_table_div[c('Clones','CDR3.aa')], FUN=sum)[['Clones']]
total_count <- 0
cdr3_aggregate <- 0

if ((nrow(chain_table_cdr3) != 1) && (length(unique(chain_table_cdr3$CDR3)) != 1)){
for (i in 1:nrow(chain_table_cdr3)) {
total_count <- total_count + chain_table_cdr3[i, 2]
cdr3_aggregate <- cdr3_aggregate + (chain_table_cdr3[i, 1]*chain_table_cdr3[i, 2])
}
}

suppressMessages(intracohort_values[1, 3] <- round(repDiversity(chain_table_div, "div", process_col),4))
if (nrow(chain_table_cdr3) != 1){

suppressMessages(intracohort_values[1, 4] <- round(entropy(ent_range),4))
suppressMessages(intracohort_values[1, 5] <- round(1/entropy(ent_range),4))
suppressMessages(intracohort_values[1, 6] <- round(entropy(ent_range,.norm=TRUE),4))
suppressMessages(intracohort_values[1, 7] <- round(repDiversity(chain_table_div, "gini", process_col),4))
suppressMessages(intracohort_values[1, 8] <- round(repDiversity(chain_table_div, "gini.simp", process_col),4))
suppressMessages(intracohort_values[1, 9] <- round(repDiversity(chain_table_div, "inv.simp", process_col),4))
suppressMessages(intracohort_values[1, 10] <- round(repDiversity(chain_table_div, "chao1", process_col)[1],4))
intracohort_values[1, 11] <- round((length(unique(chain_table_div$CDR3.aa))/sum(chain_table_div$Clones))*1000,4)
}

clonality_top <- repClonality(chain_table_div, "top")
suppressMessages(intracohort_values[1, 12] <- round(clonality_top[1],4))
suppressMessages(intracohort_values[1, 13] <- round(clonality_top[2],4))
suppressMessages(intracohort_values[1, 14] <- round(clonality_top[3],4))
suppressMessages(intracohort_values[1, 15] <- round(repClonality(chain_table_div, "clonal.prop",.perc=10)[1],4))
suppressMessages(intracohort_values[1, 16] <- round(repClonality(chain_table_div, "clonal.prop",.perc=50)[1],4))

if (nrow(chain_table_cdr3) != 1){
suppressMessages(intracohort_values[1, 17] <- round(1-(entropy(ent_range)/log2(length(ent_range))),4))
}

if ((nrow(chain_table_cdr3) != 1) && (length(unique(chain_table_cdr3$CDR3)) != 1)){
intracohort_values[1, 18] <- round(cdr3_aggregate/total_count,4)
} else {
intracohort_values[1, 18] <- chain_table_cdr3[1, 2]
}

if (input_format == "RHTCRSEQ" || cdr3nt_skip == TRUE){
intracohort_values[1, 18] <- intracohort_values[1, 19]*3
}

if (input_format == "RHTCRSEQ" || cdr3nt_skip == TRUE){
intracohort_values[1, 19] <- length(unique(chain_table_div$CDR3.aa))
} else {
intracohort_values[1, 19] <- length(unique(chain_table_div$CDR3.nt))
intracohort_values[1, 20] <- length(unique(chain_table_div$CDR3.aa))
}

if (length(clonotypeAbundance)>0){
for (i in c(1:length(clonotypeAbundance))){
foundClonotype = sum(chain_table_div[which(chain_table_div$CDR3_AA == clonotypeAbundance[i]), ]$read_fragment_freq)
if (foundClonotype != 0){
foundClonotype = foundClonotype * 1000
ent_range <- aggregate(. ~ CDR3.aa, data=chain_table_div[c('Clones','CDR3.aa')], FUN=sum)[['Clones']]

suppressMessages(intracohort_values[1, 4] <- round(entropy(ent_range),4))
suppressMessages(intracohort_values[1, 5] <- round(1/entropy(ent_range),4))
suppressMessages(intracohort_values[1, 6] <- round(entropy(ent_range,.norm=TRUE),4))
suppressMessages(intracohort_values[1, 7] <- round(repDiversity(chain_table_div, "gini", process_col),4))
suppressMessages(intracohort_values[1, 8] <- round(repDiversity(chain_table_div, "gini.simp", process_col),4))
suppressMessages(intracohort_values[1, 9] <- round(repDiversity(chain_table_div, "inv.simp", process_col),4))
suppressMessages(intracohort_values[1, 10] <- round(repDiversity(chain_table_div, "chao1", process_col)[1],4))
intracohort_values[1, 11] <- round((length(unique(chain_table_div$CDR3.aa))/sum(chain_table_div$Clones))*1000,4)
}

clonality_top <- repClonality(chain_table_div, "top")
suppressMessages(intracohort_values[1, 12] <- round(clonality_top[1],4))
suppressMessages(intracohort_values[1, 13] <- round(clonality_top[2],4))
suppressMessages(intracohort_values[1, 14] <- round(clonality_top[3],4))
suppressMessages(intracohort_values[1, 15] <- round(repClonality(chain_table_div, "clonal.prop",.perc=10)[1],4))
suppressMessages(intracohort_values[1, 16] <- round(repClonality(chain_table_div, "clonal.prop",.perc=50)[1],4))

if (nrow(chain_table_cdr3) != 1){
suppressMessages(intracohort_values[1, 17] <- round(1-(entropy(ent_range)/log2(length(ent_range))),4))
}

if ((nrow(chain_table_cdr3) != 1) && (length(unique(chain_table_cdr3$CDR3)) != 1)){
intracohort_values[1, 18] <- round(cdr3_aggregate/total_count,4)
} else {
intracohort_values[1, 18] <- chain_table_cdr3[1, 2]
}

if (input_format == "RHTCRSEQ" || cdr3nt_skip == TRUE){
intracohort_values[1, 18] <- intracohort_values[1, 19]*3
}

if (input_format == "RHTCRSEQ" || cdr3nt_skip == TRUE){
intracohort_values[1, 19] <- length(unique(chain_table_div$CDR3.aa))
} else {
intracohort_values[1, 19] <- length(unique(chain_table_div$CDR3.nt))
intracohort_values[1, 20] <- length(unique(chain_table_div$CDR3.aa))
}

if (length(clonotypeAbundance)>0){
for (i in c(1:length(clonotypeAbundance))){
foundClonotype = sum(chain_table_div[which(chain_table_div$CDR3_AA == clonotypeAbundance[i]), ]$read_fragment_freq)
if (foundClonotype != 0){
foundClonotype = foundClonotype * 1000
}
#if (foundClonotype > 0){
intracohort_values[1, functionNum+i] <- foundClonotype
#}
}
#if (foundClonotype > 0){
intracohort_values[1, functionNum+i] <- foundClonotype
#}
}
}
}

if (exists("intracohort_table")) {
intracohort_table <- rbind(intracohort_table, intracohort_values)
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