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Hi! I cleaned up your code for you! #1

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Hi! I cleaned up your code for you! #1

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20 changes: 20 additions & 0 deletions .gitignore
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Original file line number Diff line number Diff line change
@@ -0,0 +1,20 @@
# Compiled source #
###################
*.com
*.class
*.dll
*.exe
*.o
*.so
*.pyc

# Logs and databases #
######################
*.log

# OS generated files #
######################
.DS_Store*
ehthumbs.db
Icon?
Thumbs.db
2 changes: 1 addition & 1 deletion pca_me.R
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Original file line number Diff line number Diff line change
Expand Up @@ -76,7 +76,7 @@ if (num_cases==0)
end <- samples_in_test + count
for (j in start:end)
{
color[j:j, 1] <- as.character(samples[i:i, 2])
color[j:j, 1] <- as.character(samples[i:i, 2])
}
start <- end + 1
count <- count + samples_in_test
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16 changes: 8 additions & 8 deletions poisson.R
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Original file line number Diff line number Diff line change
@@ -1,4 +1,4 @@
# get from input
# get from input
data <- commandArgs(trailingOnly=TRUE)[1] # get the file name with the counts
mode <- commandArgs(trailingOnly=TRUE)[2] # do you want to use the raw counts or the percent of total reads? pertotal or count
num_reads <- commandArgs(trailingOnly=TRUE)[3] # file with the number of reads
Expand All @@ -10,16 +10,16 @@ data <- read.delim(data, header=T, sep="\t", row.names=1)
names <- row.names(data)
attach(data)

# define some variables
# define some variables
row <- length(data[,1]) # number of functions or OTUs
col <- length(data[1,]) # number of samples
pvalc <- col + 2
qvalc <- col + 3
treatment <- gl(2,4,col) # which samples are cases and controls ?
treatment <- gl(2,4,col) # which samples are cases and controls ?
outcome <- gl(4,1,col) # which samples are paired ?

# make matricies
counts <- matrix(0, nrow=row, ncol=col)
# make matricies
counts <- matrix(0, nrow=row, ncol=col)
pvalue <- array(0, dim=row)
pertotal <- matrix(0, nrow=row, ncol=col)
qvalue <- matrix(0, nrow=row, ncol=1)
Expand All @@ -31,16 +31,16 @@ if (mode=="pertotal")
for (i in 1:row)
{
for (j in 1:col)
{
{
pertotal[i:i, j:j] <- (data[i:i, j:j]/num_reads[j:j, 1])*100
}
}
}
data <- pertotal
}

# poisson!
for (i in 1: row)
{
{
counts[i,] <- t(data[i,])
model <- glmer(counts[i,] ~ treatment +(0+treatment|outcome),family=poisson())
pvalue[i] <- summary(model)@coefs[2,4]
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26 changes: 13 additions & 13 deletions standardizeR.R
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Expand Up @@ -40,7 +40,7 @@ if (method==0)
for (i in 1:row)
{
for (j in 1:col)
{
{
pertotal[i:i, j:j] <- ((data[i:i, j:j])/(reads[j,1]))*100
#pertotal_adj[i:i, j:j] <- pertotal[i:i, j:j]*1000000
#trans[i:i, j:j] <- log(pertotal_adj[i:i, j:j]+1, 2)
Expand All @@ -57,7 +57,7 @@ if (method==1)
for (i in 1:row)
{
for (j in 1:col)
{
{
pertotal[i:i, j:j] <- ((data[i:i, j:j])/(reads[j,1]))*100
trans[i:i, j:j] <- log(pertotal[i:i, j:j]+1, 10)
sdev_case[i,1] <- sd(trans[i:i, 1:num_cases])
Expand All @@ -74,7 +74,7 @@ if (method==2)
for (i in 1:row)
{
for (j in 1:col)
{
{
pertotal[i:i, j:j] <- ((data[i:i, j:j])/(reads[j,1]))*100
trans[i:i, j:j] <- log(pertotal[i:i, j:j]+1, 10)
sdev[i,1] <- sd(trans[i:i, 1:col])
Expand All @@ -83,27 +83,27 @@ if (method==2)
}
}

## method 3: Log transform the raw counts. Calculate standard deviation and average for cases and controls, separately, across columns.
## method 3: Log transform the raw counts. Calculate standard deviation and average for cases and controls, separately, across columns.
if (method==3)
{
for (i in 1:row)
{
for (j in 1:col)
{
{
trans[i:i, j:j] <- log(data[i:i, j:j]+1, 2)
sdev[1, j:j] <- sd(trans[1:row, j:j])
average[1, j:j] <- mean(trans[1:row, j:j])
}
}
}

## method 4: Log transform the raw counts. Calculate standard deviation and average for cases and controls, together, across rows.
## method 4: Log transform the raw counts. Calculate standard deviation and average for cases and controls, together, across rows.
if (method==4)
{
for (i in 1:row)
{
for (j in 1:col)
{
{
trans[i:i, j:j] <- log(data[i:i, j:j]+1, 10)
sdev[i,1] <- sd(trans[i:i, 1:col])
average[i,1] <- mean(trans[i:i, 1:col])
Expand All @@ -117,7 +117,7 @@ if (method==5)
for (i in 1:row)
{
for (j in 1:col)
{
{
pertotal[i:i, j:j] <- ((data[i:i, j:j])/(reads[j,1]))*100
}
}
Expand Down Expand Up @@ -198,7 +198,7 @@ if (method==5)
stand <- pertotal
}

#multiple sample scaling
#multiple sample scaling
if (mss=="true")
{
allstand <- matrix(0, nrow=row*col, ncol=1)
Expand Down Expand Up @@ -254,7 +254,7 @@ if (ttest=="notpaired")
# if doing a ttest
if (ttest!="none")
{
#make more matricies
#make more matricies
qvalues <- matrix(0, nrow=row, ncol=1)
qval <- qvalue(ttest[,1])$qvalues
qvalues <- as.matrix(qval)
Expand All @@ -268,7 +268,7 @@ if (ttest!="none")
table[, qvalc] <- qvalues
print <- data.frame(table, row.names=annotations)
print <- print[order(print[,ttestc], print[,qvalc]),]

#plot some data
boxplot(data, main="raw counts")
boxplot(pertotal, main="raw percent of total reads")
Expand All @@ -288,12 +288,12 @@ if (ttest!="none")

#if not doing a ttest
if (ttest=="none")
{
{
#make the table
table <- matrix(0, nrow=row, ncol=col)
table[,1:col] <- as.matrix(stand)
print <- data.frame(table, row.names=annotations)

#plot some data
boxplot(data, main="raw counts")
boxplot(pertotal, main="raw percent of total reads")
Expand Down