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R/New_codes_MDPD.txt

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+#1. Quality trimming of raw fastq data
+
+trim_galore --paired --quality 20 --length 20 *.fastq.gz -o ./Filtered/ -j 10 --no_report_file
+
+#2. Amplicon sequence data analysis using DADA2
+
+setwd("/media/dibakar/New Volume1/MDPD_V2/IPF_DADA2/PRJNA357063/Filtered/")
+library(dada2)
+fnFs <- sort(list.files(pattern="_1.fastq.gz", full.names = TRUE))
+fnRs <- sort(list.files(pattern="_2.fastq.gz", full.names = TRUE))
+sample.names <- sapply(strsplit(basename(fnFs), "_"), `[`, 1)
+filtFs <- file.path("filtered", paste0(sample.names, "_F_filt.fastq.gz"))
+filtRs <- file.path("filtered", paste0(sample.names, "_R_filt.fastq.gz"))
+names(filtFs) <- sample.names
+names(filtRs) <- sample.names
+out <- filterAndTrim(fnFs, filtFs, fnRs, filtRs, maxN=0, maxEE=c(2,2), truncQ=2, rm.phix=TRUE,
+                     compress=TRUE, multithread=TRUE)
+head(out)
+errF <- learnErrors(filtFs, multithread=TRUE)
+errR <- learnErrors(filtRs, multithread=TRUE)
+dadaFs <- dada(filtFs, err=errF, multithread=TRUE)
+dadaRs <- dada(filtRs, err=errR, multithread=TRUE)
+dadaFs[[1]]
+mergers <- mergePairs(dadaFs, filtFs, dadaRs, filtRs, verbose=TRUE)
+# head(mergers[[1]])
+seqtab <- makeSequenceTable(mergers)
+# dim(seqtab)
+# table(nchar(getSequences(seqtab)))
+seqtab.nochim <- removeBimeraDenovo(seqtab, method="consensus", multithread=TRUE, verbose=TRUE)
+# dim(seqtab.nochim)
+# sum(seqtab.nochim)/sum(seqtab)
+getN <- function(x) sum(getUniques(x))
+track <- cbind(out, sapply(dadaFs, getN), sapply(dadaRs, getN), sapply(mergers, getN), rowSums(seqtab.nochim))
+colnames(track) <- c("input", "filtered", "denoisedF", "denoisedR", "merged", "nonchim")
+rownames(track) <- sample.names
+# head(track)
+taxa <- assignTaxonomy(seqtab.nochim, "silva_nr99_v138.1_train_set.fa.gz", multithread=TRUE)
+taxa.print <- taxa
+rownames(taxa.print) <- NULL
+# head(taxa.print)
+library(phyloseq)
+metadata = read.csv("PRJNA357063_metadta.csv", row.names = 1, header = TRUE)
+ps <- phyloseq(otu_table(seqtab.nochim, taxa_are_rows=FALSE), sample_data(metadata), tax_table(taxa))
+ps
+dna <- Biostrings::DNAStringSet(taxa_names(ps))
+names(dna) <- taxa_names(ps)
+ps <- merge_phyloseq(ps, dna)
+taxa_names(ps) <- paste0("ASV", seq(ntaxa(ps)))
+asv_seqs <- colnames(seqtab.nochim)
+asv_headers <- vector(dim(seqtab.nochim)[2], mode="character")
+for (i in 1:dim(seqtab.nochim)[2]) {
+  asv_headers[i] <- paste(">ASV", i, sep="_")
+}
+Asv_fasta <- c(rbind(asv_headers, asv_seqs))
+write(Asv_fasta, "ASVs.fa")
+asv_tab <- t(seqtab.nochim)
+row.names(asv_tab) <- sub(">", "", asv_headers)
+write.table(asv_tab, "ASVs_counts.tsv", sep="\t", quote=F, col.names=NA)
+
+#Krona Plot
+library(psadd)
+plot_krona(ps, "PRJNA357063", "SubGroup", trim = T)
+
+#Downstream analysis
+library(microeco)
+library(file2meco)
+
+#Phyloseq to meco obejct
+meco_object <- phyloseq2meco(ps)
+meco_object$tidy_dataset()
+
+#Filter pollution
+meco_object$filter_pollution(taxa = c("mitochondria", "chloroplast"))
+meco_object$tidy_dataset()
+#Filter based on abundance and detection
+meco_object$filter_taxa(rel_abund = 0.0001, freq = 0.05)
+meco_object$tidy_dataset()
+
+#Box plot Phylum
+t1 <- trans_abund$new(dataset = meco_object, taxrank = "Phylum", ntaxa = 10)
+t1$plot_box(group = "Group", xtext_angle = 30)
+write.csv(t1$data_abund, file = "Phylum_box.csv")
+#Bar plot Genus
+t2 <- trans_abund$new(dataset = meco_object, groupmean = "SubGroup", taxrank = "Genus")face = "bold"), axis.text.x = element_text(size = 16, face = "bold"),panel.background = element_blank(), axis.title = element_text(size = 12, face = "bold"), axis.line = element_line(colour = "black"), legend.text = element_text(size = 12))
+g2 <- t2$plot_bar(others_color = "grey70", bar_full = FALSE, legend_text_italic = TRUE) + theme(axis.text.y = element_text(size = 12, vjust = 0.5, face = "bold"), axis.text.x = element_text(size = 16, face = "bold"),panel.background = element_blank(), axis.title = element_text(size = 12, face = "bold"), axis.line = element_line(colour = "black"), legend.text = element_text(size = 12))
+g2
+write.csv(g2$data, file = "Genus_Bar.csv")
+
+
+#Heatmap run wise
+t3 <- trans_abund$new(dataset = meco_object, taxrank = "Genus", ntaxa = 30)
+g3 <- t3$plot_heatmap(facet = "Group", xtext_keep = FALSE, withmargin = FALSE, plot_breaks = c(0.01, 0.1, 1, 10))
+g3
+g3 + theme(axis.text.y = element_text(face = 'italic'))st_method = "none")
+write.csv(g3$data, file = "Heatmap_run.csv")
+
+#Differential
+#Lefse
+t4 <- trans_diff$new(dataset = meco_object, method = "lefse", group = "SubGroup", alpha = 0.05, lefse_subgroup = NULL, taxa_level = "Genus", p_adjust_method = "none")
+# see t1$res_diff for the result
+# From v0.8.0, threshold is used for the LDA score selection.
+t4$plot_diff_bar(threshold = 2)
+# we show 20 taxa with the highest LDA (log10)
+t4$plot_diff_bar(use_number = 1:30, width = 0.8)
+write.csv(t4$res_diff, file = "LDA_Bar.csv")
+
+library(Maaslin2)
+# input_data <- as.data.frame(t(read.delim(system.file('extdata','HMP2_taxonomy.tsv', package="Maaslin2"), row.names = 1)))
+# input_metadata <- read.delim(system.file('extdata','HMP2_metadata.tsv', package="Maaslin2"), row.names = 1)
+# library(microeco)
+# d1 <- microtable$new(input_data, sample_table = input_metadata)
+meco_object$taxa_abund$Genus <- meco_object$otu_table
+t5 <- trans_diff$new(dataset = meco_object, method = "maaslin2",
+                     fixed_effects = c('SubGroup', ''),
+                     random_effects = 'NONE',
+                     normalization = 'NONE',
+                     reference = 'NONE',
+                     standardize = FALSE,
+                     taxa_level = "Genus", filter_thres = 0.001,
+                     tmp_input_maaslin2 = "maaslin2_tmp_input3", tmp_output_maaslin2 = "maaslin2_tmp_output3")
+View(t5$res_diff)
+# use name column as x axis; delete the Env column. it is a minor bug in v1.5.0. Fixed in the v1.6.0
+t5$res_diff <- t5$res_diff[, -3]
+t5$plot_diff_bar(heatmap_cell = "coef", heatmap_lab_fill = "Coef", heatmap_x = "name", heatmap_y = "Taxa", keep_prefix = TRUE, add_sig = FALSE)
+write.csv(t5$res_diff, file = "Maaslin2_Heatmap.csv")