The dataset I am working on is Illumina HumanMethylation450 BeadChip GEO- GSE86829 and have too perform a similar normalization on other Illumina HumanMethylation 850k data too. Although normalised files are present the project requires me to do it from scratch to bring uniformity. I have found an R code for normalization of IDAT files but that requires a .bgx manifest file too. Posting the code below.

library(limma)
x <- read.idat(idatfiles, bgxfile)
y <- neqc(data)

My problem is that the above data and many others that I am working on does not have a .bgx file and instead have manifest file as .bpm or .csv provided along with GSE86829_RAW.tar file. I am new to processing microarray data and R libraries. Please help with a code that could work to simply normalize all the IDAT files for every sample in a directory and provide output as a single .txt with every sample in columns. Basically what I need is an equivalent of what ReadAffy does for CEL files, I need a similar one for IDAT files that does not need .bgx and work with .txt/.csv as manifest file.

library(affy)
celpath = "/mnt/store_room/Dataset1/processing/validation/GSE65663_RAW"
data = ReadAffy(celfile.path=celpath)

OR provide a different tool altogether to process IDAT files easily without much coding

It's easy with minfi and GEOquery

> library(GEOquery)
> library(minfi)
> getGEOSuppFiles("GSE86829")
> setwd("GSE86829/")
## minfi won't use gzipped files
> sapply(dir(".", "gz$"), gunzip)
## at this point you could generate a targets file, but that might be more work than helpful. You can just read in however.
> z <- read.metharray.exp(".")
Warning message:
In readChar(con, nchars = n) : truncating string with embedded nuls
> z
class: RGChannelSet 
dim: 622399 15 
metadata(0):
assays(2): Green Red
rownames(622399): 10600313 10600322 ... 74810490 74810492
rowData names(0):
colnames(15): GSM2309154_6264509024_R01C02 GSM2309155_6264509024_R02C02
  ... GSM2309167_200190110117_R01C02 GSM2309168_200190110117_R03C02
colData names(0):
Annotation
  array: IlluminaHumanMethylation450k
  annotation: ilmn12.hg19

## at this point you could add the colData to describe the experiment (probably useful)
> colData(z) <- DataFrame(Sample = rep(c("LNCaP","PrEC","CAF","NAF","Guthrie"), c(2,2,3,3,5)))
> z
class: RGChannelSet 
dim: 622399 15 
metadata(0):
assays(2): Green Red
rownames(622399): 10600313 10600322 ... 74810490 74810492
rowData names(0):
colnames: NULL
colData names(1): Sample
Annotation
  array: IlluminaHumanMethylation450k
  annotation: ilmn12.hg19
> colData(z)
DataFrame with 15 rows and 1 column
         Sample
    <character>
1         LNCaP
2         LNCaP
3          PrEC
4          PrEC
5           CAF
...         ...
11      Guthrie
12      Guthrie
13      Guthrie
14      Guthrie
15      Guthrie

At this point you are at step 2.3 of this workflow, which is a useful explainer for the remaining steps.

The limma code is for Illumina expression arrays, which use bgx files. You are wanting to read Illumina methylation arrays, which use bpm or csv annotation files instead of bgx.

I suggest that you investigate Bioconductor packages designed for methylation arrays, especially the minfi or methylationArrayAnalysis packages.