<p>It seems like you are missing function "<a href="https://rdrr.io/bioc/BLMA/src/R/PathwayAnalysis.R" rel="nofollow noreferrer">pathwayAnalysis</a>" presumably from package BLMA, i.e. <code>library('BLMA')</code> should resolve your error once you install that package. </p>

<p>-- edit 1 --</p>

<p>Ha, stupid me. Package <code>BLMA</code> contains file "pathwayAnalysis.R", not the function. A package that contains a FUNCTION called <code>pathwayAnalysis</code> is <a href="https://www.rdocumentation.org/packages/IntClust/versions/0.1.0/topics/PathwayAnalysis" rel="nofollow noreferrer">IntClust</a>. You should try that one.</p>

<p>-- edit 2 --</p>

<p>Alright, stupid me again, now I have not checked that IntClust has capitalized <code>PathwayAnalysis</code>, you need <code>pathwayAnalysis</code>. I found <a href="https://www.rdocumentation.org/packages/metaX/versions/1.4.0/topics/pathwayAnalysis" rel="nofollow noreferrer">one more function</a> matching the name form package <code>metaX</code>. However, it could be that it's function defined somewhere in their code. You can try to <a href="https://readwrite.com/2010/11/10/how-to-search-your-source-with/" rel="nofollow noreferrer">search the source code for example using grep</a> or you try to use google to find packages that contain the function of this name (this is exactly what I was doing, many you should be a bit more careful than I was).</p>

CC BY-SA 4.0
2019-07-09T15:57:28.230Z
let me try and see if its working
kcm
CC BY-SA 4.0
2019-07-09T16:04:28.187Z
even after installing and loading the library it doesn;t work may it there is something else. I will look into it
kcm
CC BY-SA 4.0
2019-07-09T16:05:49.980Z
Do you get a new error or the same one? (if it's a new one, edit the question and add there the new error to the end)
Kamil S Jaron
CC BY-SA 4.0
2019-07-09T16:08:23.977Z
the same exact error . "Error in pathwayAnalysis(fit2, pvaluecutoff = 0.05) : 
  could not find function "pathwayAnalysis""
kcm
CC BY-SA 4.0
2019-07-09T16:12:08.747Z
My bad, I was googling too quickly. Check the edit to my answer.
Kamil S Jaron
CC BY-SA 4.0
2019-07-09T16:13:04.587Z
let me give this a try will update ,
kcm
CC BY-SA 4.0
2019-07-09T16:25:21.133Z
and it still doesn;t work same error "  could not find function "pathwayAnalysis"
" after loading that library .May be will have to bit more into the supplementary codes.
kcm
CC BY-SA 4.0
2019-07-09T17:11:07.730Z
Check the new edit. I made another mistake in my quick search, so I have decided to rather give you hints how to resolve your problems by your own (grepping and googling). Hope it help, my time runs out.
Kamil S Jaron
CC BY-SA 4.0
2019-07-09T17:24:20.133Z
will update you
kcm
CC BY-SA 4.0
2019-07-10T06:39:57.007Z
couldn't make it work but anyways thank you for the help ,meanwhile i have written to the paper authors
kcm
CC BY-SA 4.0
2019-07-09T15:56:23.813Z
2019-07-09T17:10:12.887Z
2019-07-09T17:10:12.887Z
Kamil S Jaron
<pre><code>## ------------------------------------------------------------------------
library(limma)
library(statmod)
library(GEOquery)
#library(GEOmetadb)
require(Biobase)
library(ggplot2)
library(rio)
library(affy)
library(affyPLM)
library(reshape)
library(stringr)
library(cowplot)
#library(makecdfenv)
#library(aroma.affymetrix)
#library(RTN)
library(gProfileR)
library(corpcor)
library(viper)
library(rio)
library(dplyr)
library(igraph)
library(RColorBrewer)
library(reshape2)


## ------------------------------------------------------------------------
gse89988 &lt;- getGEO(filename = "GSE89988_family.soft.gz", GSEMatrix = TRUE)


## ------------------------------------------------------------------------
# define samples
samples_nt &lt;- c('GSM2394292', 'GSM2394303')
samples_pam &lt;- c('GSM2394295', 'GSM2394306')
samples_lps &lt;- c('GSM2394296', 'GSM2394307')

# make table with samples metadata
samples &lt;- c(samples_nt, samples_pam, samples_lps)
metadata &lt;- as.data.frame(samples)
colnames(metadata) &lt;- 'sample_id'

# add group information both with the group acronym as a dummy variable -- which
# will be useful for the DEA; also add color for differentiate groups in plots
metadata<span class="math-container">$group &lt;- c(rep(0, 2), rep(1, 2), rep(2, 2))
metadata$</span>group_name &lt;- c(rep('NT', 2), rep('PAM', 2), rep('LPS', 2))
metadata$group_color &lt;- c(rep('gray', 2), rep('tomato', 2), rep('dodgerblue', 2))

# get positions in GSE object
match(samples, names(GSMList(gse89988)))

# with the following I confirm that the samples correspond to the expected samples
# and get that expression values are already normalized and log2-transformed
GSMList(gse89988)[[49]]
GSMList(gse89988)[[60]]
GSMList(gse89988)[[52]]
GSMList(gse89988)[[63]]
GSMList(gse89988)[[53]]
GSMList(gse89988)[[64]]


## ------------------------------------------------------------------------
#First, we need to make sure that all of the GSMs are from the same platform
gsmplatforms &lt;- lapply(GSMList(gse89988),function(x) {Meta(x)$platform_id})
head(gsmplatforms)
# from the output of the command above we see that indeed all GSMs derive from
# the same platform "GPL17543"

# We can filter the original GSMList to include only those GSMs with the GPL96 platform and use this list for further processing
gsmlist = Filter(function(gsm) {Meta(gsm)$platform_id=='GPL17543'},GSMList(gse89988))
length(gsmlist)

# get the probeset ordering
probesets &lt;- Table(GPLList(gse89988)[[1]])$ID

# make the data matrix from the VALUE columns from each GSM
# being careful to match the order of the probesets in the platform
# with those in the GSMs
data.matrix &lt;- do.call('cbind', lapply(gsmlist,function(x) 
                                      {tab &lt;- Table(x)
                                      mymatch &lt;- match(probesets,tab<span class="math-container">$ID_REF)
                                      return(tab$</span>VALUE[mymatch])
                                     }))
data.matrix &lt;- apply(data.matrix, 2, function(x) {as.numeric(as.character(x))})

# go through the necessary steps to make a compliant ExpressionSet
# and filter for the samples to be analyzed
rownames(data.matrix) &lt;- probesets
colnames(data.matrix) &lt;- names(gsmlist)
data.matrix &lt;- data.matrix[, samples]
pdata &lt;- data.frame(samples = samples)
rownames(pdata) &lt;- samples
pheno &lt;- as(pdata,"AnnotatedDataFrame")
eset &lt;- new('ExpressionSet', exprs = data.matrix, phenoData = pheno)


## ------------------------------------------------------------------------
load_gene_annotation &lt;- function() {

  ifile &lt;- 'GPL17543-38936.txt'
  df &lt;- read.delim(ifile, comment.char = '#')

  return(df)

}

gene_annotation &lt;- load_gene_annotation()


## ------------------------------------------------------------------------
apply_dea &lt;- function(eset, fc_cutoff = 2, adj_p_cutoff = 0.05) {

  # output directory
  ODIR &lt;- 'results/step1/'
  dir.create(ODIR, showWarnings = FALSE, recursive = TRUE)

  # make design matrix
  design &lt;- model.matrix(~ 0 + factor(c(0, 0, 1, 1, 2, 2)))
  colnames(design) &lt;- c("group0", "group1", "group2")

  # plot with the normalized array weights
  aWeights &lt;- arrayWeights(eset, design = design)
  ofile &lt;- paste0(ODIR, 'array_weights_gse89988_samples_used_in_dea.pdf')
  pdf(ofile, width = 10, height = 5)
  dotchart(aWeights, labels = samples,
           groups = as.factor(metadata<span class="math-container">$group_name),
           pch = 19,
           color = metadata$</span>group_color,
           main = "Array Weights")  
  dev.off()

  # linear model fit
  fit &lt;- lmFit(eset, design)
  contrast.matrix &lt;- makeContrasts(group1-group0, group2-group0,
                                   levels = design)
  fit2 &lt;- contrasts.fit(fit, contrast.matrix)
  fit2 &lt;- eBayes(fit2)


  # save 
  ODIR='deliverables/'
  dir.create(ODIR, showWarnings = FALSE)
  ofile=paste0(ODIR, 'fit_for_figures_7_and_8.rds')
  saveRDS(fit2, ofile)


  # extract results, annotate and visualize DEA results

  extract_annotate_and_viz_dea_results &lt;- function(coef_index, ref, treat) {

    # Extract DEA results for a given comparison
    df &lt;- topTable(fit2, coef = coef_index, adjust = "BH", number = Inf)
    df<span class="math-container">$reference &lt;- ref
    df$</span>treatment &lt;- treat

    # remove genes with missing values
    df &lt;- df[complete.cases(df), ]

    # annotate with gene information -- gene annotation has many fields which
    # are not relevant here so I will make a reduced gene information version
    fields_included &lt;- c('ID', 'Species', 'Source', 'Entrez_Gene_ID', 'Symbol')
    df &lt;- merge(df, gene_annotation[fields_included], by.x = 0, by.y = 'ID',
                sort = FALSE)
    colnames(df)[1] &lt;- 'probe_id'

    # prepare data for visualization through volcano plot
    cond1 &lt;- df<span class="math-container">$adj.P.Val &lt; adj_p_cutoff
    cond2d &lt;- df$</span>logFC &lt; -log2(fc_cutoff)
    cond2u &lt;- df<span class="math-container">$logFC &gt; log2(fc_cutoff)
    log10p &lt;- -log10(df$</span>adj.P.Val)
    down &lt;- nrow(df[cond1 &amp; cond2d, ])
    up &lt;- nrow(df[cond1 &amp; cond2u, ])
    df<span class="math-container">$category &lt;- ifelse(cond1 &amp; cond2d, 'down-regulated',
                          ifelse(cond1 &amp; cond2u, 'up-regulated', 'non-regulated'))
    abs_max_logfc &lt;- max(abs(df$</span>logFC))

    # volcano plot
    g &lt;- ggplot(df, aes(x = logFC, y = -log10(adj.P.Val), colour = category)) +
          geom_point(alpha = 0.4, size = 1.75) +
          scale_color_manual(values = c('dodgerblue', 'gray', 'tomato')) +
          xlim(-abs_max_logfc, abs_max_logfc) +
          ylim(c(0, max(-log10(df$adj.P.Val)))) +
          xlab("log2 fold change") +
          ylab("-log10(adjusted p-value)") +
          annotate("text", x = -4, y = 3, size =3, 
                   label = paste0(down, "\ndown-regulated DEGs"),
                   color = 'dodgerblue') +
          annotate("text", x = 4, y = 3, size = 3,
                   label = paste0(up, "\nup-regulated DEGs"),
                   color = 'tomato') +
          annotate("text", x = -abs_max_logfc, y = 0, size = 2,
                   label = paste0("DEGs defined with:\nAdj. p-value &lt; ",
                                  adj_p_cutoff,
                                  '\nFold change &gt; ',
                                  fc_cutoff),
                   color = 'black', hjust = 0) +
          ggtitle(paste0(ref, ' vs. ', treat)) +
          theme(plot.title = element_text(hjust = 0.5))

    # save plot to file
    ofile &lt;- paste0(ODIR, 'volcano_plot_', tolower(ref), '_vs_', tolower(treat),
                    '.pdf')
    ggsave(ofile, width = 7, height = 5)

    return(df)

  }

  res &lt;- rbind(extract_annotate_and_viz_dea_results(1, 'NT', 'PAM'),
               extract_annotate_and_viz_dea_results(2, 'NT', 'LPS'))


  # export table with results
  ofile &lt;- paste0(ODIR, 'differential_expression_analysis_results_combined.xlsx')
  export(res, ofile)

}


## ------------------------------------------------------------------------
apply_dea(eset)
</code></pre>

<p>After all these i have to run this </p>

<p><code>gsca &lt;- pathwayAnalysis(fit2, pvaluecutoff=0.05)</code>,where fit2 is made after running <code>apply_dea(eset)</code> </p>

<p>As I run the above code i get this </p>

<blockquote>
  <p>Error in pathwayAnalysis(fit2, pvaluecutoff = 0.05) :    could not
  find function "pathwayAnalysis"</p>
</blockquote>

<p>I would share the .rdata file for reproduciblty<br>
<a href="https://drive.google.com/file/d/1TGHa0TKV9J6glq7Q5JhoFMUM94IXa0eN/view?usp=sharing" rel="nofollow noreferrer">rdata</a></p>

<p>The gse &amp; the annotation <a href="https://drive.google.com/open?id=1QkxCaIlzNwa4BoBZ_KEhaVYocUrJNbuD" rel="nofollow noreferrer">files</a></p>

<p>I would be really glad if i get to know what am i doing wrong.</p>

CC BY-SA 4.0
2019-07-09T15:30:22.150Z
What package is `pathwayAnalysis` a part of? Or is it a custom function you wrote?
Ram RS
CC BY-SA 4.0
2019-07-09T15:33:34.493Z
Note: Sharing RDA file and custom annotation files does not make our jobs easier. It's not fair to expect us to download and use large datasets just so *we* can solve *your* problem.
Ram RS
CC BY-SA 4.0
2019-07-09T15:53:42.417Z
Please, before posing another insanely long question, read [this page](https://stackoverflow.com/help/minimal-reproducible-example). The reproducible example should be *minimal*.
Kamil S Jaron
CC BY-SA 4.0
2019-07-09T15:53:56.020Z
that is absolutely correct regarding large files , regarding  pathwayAnalysis i m actually trying to replicate  this study https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1002223 the codes is part of its supplementary data.i have loaded all the library mentioned but im not sure what is going wrong
kcm
CC BY-SA 4.0
2019-07-09T15:55:50.700Z
@krushnachChandra ha, then my best guess is that they forgot to mention this library.
Kamil S Jaron
CC BY-SA 4.0
2019-07-09T15:56:33.720Z
I made it an answer.
Kamil S Jaron
<r><microarray>
R error: could not find function "pathwayAnalysis"
# R error: could not find function "pathwayAnalysis"

(Asked by: username_0 on 2019-07-09)

## ------------------------------------------------------------------------
library(limma)
library(statmod)
library(GEOquery)
#library(GEOmetadb)
require(Biobase)
library(ggplot2)
library(rio)
library(affy)
library(affyPLM)
library(reshape)
library(stringr)
library(cowplot)
#library(makecdfenv)
#library(aroma.affymetrix)
#library(RTN)
library(gProfileR)
library(corpcor)
library(viper)
library(rio)
library(dplyr)
library(igraph)
library(RColorBrewer)
library(reshape2)


## ------------------------------------------------------------------------
gse89988 <- getGEO(filename = "GSE89988_family.soft.gz", GSEMatrix = TRUE)


## ------------------------------------------------------------------------
# define samples
samples_nt <- c('GSM2394292', 'GSM2394303')
samples_pam <- c('GSM2394295', 'GSM2394306')
samples_lps <- c('GSM2394296', 'GSM2394307')

# make table with samples metadata
samples <- c(samples_nt, samples_pam, samples_lps)
metadata <- as.data.frame(samples)
colnames(metadata) <- 'sample_id'

# add group information both with the group acronym as a dummy variable -- which
# will be useful for the DEA; also add color for differentiate groups in plots
metadata$group <- c(rep(0, 2), rep(1, 2), rep(2, 2))
metadata$group_name <- c(rep('NT', 2), rep('PAM', 2), rep('LPS', 2))
metadata$group_color <- c(rep('gray', 2), rep('tomato', 2), rep('dodgerblue', 2))

# get positions in GSE object
match(samples, names(GSMList(gse89988)))

# with the following I confirm that the samples correspond to the expected samples
# and get that expression values are already normalized and log2-transformed
GSMList(gse89988)[[49]]
GSMList(gse89988)[[60]]
GSMList(gse89988)[[52]]
GSMList(gse89988)[[63]]
GSMList(gse89988)[[53]]
GSMList(gse89988)[[64]]


## ------------------------------------------------------------------------
#First, we need to make sure that all of the GSMs are from the same platform
gsmplatforms <- lapply(GSMList(gse89988),function(x) {Meta(x)$platform_id})
head(gsmplatforms)
# from the output of the command above we see that indeed all GSMs derive from
# the same platform "GPL17543"

# We can filter the original GSMList to include only those GSMs with the GPL96 platform and use this list for further processing
gsmlist = Filter(function(gsm) {Meta(gsm)$platform_id=='GPL17543'},GSMList(gse89988))
length(gsmlist)

# get the probeset ordering
probesets <- Table(GPLList(gse89988)[[1]])$ID

# make the data matrix from the VALUE columns from each GSM
# being careful to match the order of the probesets in the platform
# with those in the GSMs
data.matrix <- do.call('cbind', lapply(gsmlist,function(x) 
                                      {tab <- Table(x)
                                      mymatch <- match(probesets,tab$ID_REF)
                                      return(tab$VALUE[mymatch])
                                     }))
data.matrix <- apply(data.matrix, 2, function(x) {as.numeric(as.character(x))})

# go through the necessary steps to make a compliant ExpressionSet
# and filter for the samples to be analyzed
rownames(data.matrix) <- probesets
colnames(data.matrix) <- names(gsmlist)
data.matrix <- data.matrix[, samples]
pdata <- data.frame(samples = samples)
rownames(pdata) <- samples
pheno <- as(pdata,"AnnotatedDataFrame")
eset <- new('ExpressionSet', exprs = data.matrix, phenoData = pheno)


## ------------------------------------------------------------------------
load_gene_annotation <- function() {

  ifile <- 'GPL17543-38936.txt'
  df <- read.delim(ifile, comment.char = '#')

  return(df)

}

gene_annotation <- load_gene_annotation()


## ------------------------------------------------------------------------
apply_dea <- function(eset, fc_cutoff = 2, adj_p_cutoff = 0.05) {

  # output directory
  ODIR <- 'results/step1/'
  dir.create(ODIR, showWarnings = FALSE, recursive = TRUE)

  # make design matrix
  design <- model.matrix(~ 0 + factor(c(0, 0, 1, 1, 2, 2)))
  colnames(design) <- c("group0", "group1", "group2")

  # plot with the normalized array weights
  aWeights <- arrayWeights(eset, design = design)
  ofile <- paste0(ODIR, 'array_weights_gse89988_samples_used_in_dea.pdf')
  pdf(ofile, width = 10, height = 5)
  dotchart(aWeights, labels = samples,
           groups = as.factor(metadata$group_name),
           pch = 19,
           color = metadata$group_color,
           main = "Array Weights")  
  dev.off()

  # linear model fit
  fit <- lmFit(eset, design)
  contrast.matrix <- makeContrasts(group1-group0, group2-group0,
                                   levels = design)
  fit2 <- contrasts.fit(fit, contrast.matrix)
  fit2 <- eBayes(fit2)


  # save 
  ODIR='deliverables/'
  dir.create(ODIR, showWarnings = FALSE)
  ofile=paste0(ODIR, 'fit_for_figures_7_and_8.rds')
  saveRDS(fit2, ofile)


  # extract results, annotate and visualize DEA results

  extract_annotate_and_viz_dea_results <- function(coef_index, ref, treat) {

    # Extract DEA results for a given comparison
    df <- topTable(fit2, coef = coef_index, adjust = "BH", number = Inf)
    df$reference <- ref
    df$treatment <- treat

    # remove genes with missing values
    df <- df[complete.cases(df), ]

    # annotate with gene information -- gene annotation has many fields which
    # are not relevant here so I will make a reduced gene information version
    fields_included <- c('ID', 'Species', 'Source', 'Entrez_Gene_ID', 'Symbol')
    df <- merge(df, gene_annotation[fields_included], by.x = 0, by.y = 'ID',
                sort = FALSE)
    colnames(df)[1] <- 'probe_id'

    # prepare data for visualization through volcano plot
    cond1 <- df$adj.P.Val < adj_p_cutoff
    cond2d <- df$logFC < -log2(fc_cutoff)
    cond2u <- df$logFC > log2(fc_cutoff)
    log10p <- -log10(df$adj.P.Val)
    down <- nrow(df[cond1 & cond2d, ])
    up <- nrow(df[cond1 & cond2u, ])
    df$category <- ifelse(cond1 & cond2d, 'down-regulated',
                          ifelse(cond1 & cond2u, 'up-regulated', 'non-regulated'))
    abs_max_logfc <- max(abs(df$logFC))

    # volcano plot
    g <- ggplot(df, aes(x = logFC, y = -log10(adj.P.Val), colour = category)) +
          geom_point(alpha = 0.4, size = 1.75) +
          scale_color_manual(values = c('dodgerblue', 'gray', 'tomato')) +
          xlim(-abs_max_logfc, abs_max_logfc) +
          ylim(c(0, max(-log10(df$adj.P.Val)))) +
          xlab("log2 fold change") +
          ylab("-log10(adjusted p-value)") +
          annotate("text", x = -4, y = 3, size =3, 
                   label = paste0(down, "\ndown-regulated DEGs"),
                   color = 'dodgerblue') +
          annotate("text", x = 4, y = 3, size = 3,
                   label = paste0(up, "\nup-regulated DEGs"),
                   color = 'tomato') +
          annotate("text", x = -abs_max_logfc, y = 0, size = 2,
                   label = paste0("DEGs defined with:\nAdj. p-value < ",
                                  adj_p_cutoff,
                                  '\nFold change > ',
                                  fc_cutoff),
                   color = 'black', hjust = 0) +
          ggtitle(paste0(ref, ' vs. ', treat)) +
          theme(plot.title = element_text(hjust = 0.5))

    # save plot to file
    ofile <- paste0(ODIR, 'volcano_plot_', tolower(ref), '_vs_', tolower(treat),
                    '.pdf')
    ggsave(ofile, width = 7, height = 5)

    return(df)

  }

  res <- rbind(extract_annotate_and_viz_dea_results(1, 'NT', 'PAM'),
               extract_annotate_and_viz_dea_results(2, 'NT', 'LPS'))


  # export table with results
  ofile <- paste0(ODIR, 'differential_expression_analysis_results_combined.xlsx')
  export(res, ofile)

}


## ------------------------------------------------------------------------
apply_dea(eset)

After all these i have to run this 
gsca <- pathwayAnalysis(fit2, pvaluecutoff=0.05),where fit2 is made after running apply_dea(eset) 
As I run the above code i get this 

Error in pathwayAnalysis(fit2, pvaluecutoff = 0.05) :    could not
  find function "pathwayAnalysis"

I would share the .rdata file for reproduciblty
rdata
The gse & the annotation files
I would be really glad if i get to know what am i doing wrong.

**Tags:** r, microarray
> **Comments:**
> **username_1**: What package is `pathwayAnalysis` a part of? Or is it a custom function you wrote?  
> **username_1**: Note: Sharing RDA file and custom annotation files does not make our jobs easier. It's not fair to expect us to download and use large datasets just so *we* can solve *your* problem.  
> **username_2**: Please, before posing another insanely long question, read [this page](https://stackoverflow.com/help/minimal-reproducible-example). The reproducible example should be *minimal*.  
> **username_0**: that is absolutely correct regarding large files , regarding  pathwayAnalysis i m actually trying to replicate  this study https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1002223 the codes is part of its supplementary data.i have loaded all the library mentioned but im not sure what is going wrong  
> **username_2**: @krushnachChandra ha, then my best guess is that they forgot to mention this library.  
> **username_2**: I made it an answer.  

---

## 1 answers

### Answer by username_2 on 2019-07-09. Score: 1

It seems like you are missing function "[pathwayAnalysis](https://rdrr.io/bioc/BLMA/src/R/PathwayAnalysis.R)" presumably from package BLMA, i.e. `library('BLMA')` should resolve your error once you install that package.

-- edit 1 --

Ha, stupid me. Package `BLMA` contains file "pathwayAnalysis.R", not the function. A package that contains a FUNCTION called `pathwayAnalysis` is [IntClust](https://www.rdocumentation.org/packages/IntClust/versions/0.1.0/topics/PathwayAnalysis). You should try that one.

-- edit 2 --

Alright, stupid me again, now I have not checked that IntClust has capitalized `PathwayAnalysis`, you need `pathwayAnalysis`. I found [one more function](https://www.rdocumentation.org/packages/metaX/versions/1.4.0/topics/pathwayAnalysis) matching the name form package `metaX`. However, it could be that it's function defined somewhere in their code. You can try to [search the source code for example using grep](https://readwrite.com/2010/11/10/how-to-search-your-source-with/) or you try to use google to find packages that contain the function of this name (this is exactly what I was doing, many you should be a bit more careful than I was).

> **Comments:**
> **username_0**: let me try and see if its working  
> **username_0**: even after installing and loading the library it doesn;t work may it there is something else. I will look into it  
> **username_2**: Do you get a new error or the same one? (if it's a new one, edit the question and add there the new error to the end)  
> **username_0**: the same exact error . "Error in pathwayAnalysis(fit2, pvaluecutoff = 0.05) :    could not find function "pathwayAnalysis""  
> **username_2**: My bad, I was googling too quickly. Check the edit to my answer.  
> **username_0**: let me give this a try will update ,  
> **username_0**: and it still doesn;t work same error "  could not find function "pathwayAnalysis" " after loading that library .May be will have to bit more into the supplementary codes.  
> **username_2**: Check the new edit. I made another mistake in my quick search, so I have decided to rather give you hints how to resolve your problems by your own (grepping and googling). Hope it help, my time runs out.  
> **username_0**: will update you  
> **username_0**: couldn't make it work but anyways thank you for the help ,meanwhile i have written to the paper authors  

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