The most recent update of this html document occurred: Thu May 10 16:51:38 2018

library(knitr)

library(ggplot2)
library(reshape)
library(DESeq2)

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library(isomiRs)

## Loading required package: DiscriMiner

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library(pheatmap)

knitr::opts_chunk$set(tidy=TRUE, highlight=TRUE, dev="png", fig.width=6,fig.heigh=6,
               cache=FALSE, highlight=TRUE, autodep=TRUE, 
               warning=FALSE, error=FALSE,
               message=FALSE, prompt=TRUE, comment='', fig.cap='', bootstrap.show.code=FALSE)

root_path = "C:/rstudio/mirna/final/"
root_file = file.path(root_path, "srna_out_files")
dir.create(root_file, showWarnings = FALSE)

metadata_fn =  list.files(file.path(root_path), pattern = "summary.csv$",recursive = T, full.names = T)
metadata = read.csv(metadata_fn, row.names="sample_id")
condition = names(metadata)[1]
design = metadata
formula = ~ condition # modify this to get your own formula, it should be a column in your metadata
isde=TRUE # turn this true to make DE ananlysis

Exploratory analysis

In this section we will see descriptive figures about quality of the data, reads with adapter, reads mapped to miRNAs, reads mapped to other small RNAs.

size distribution

After adapter removal, we can plot the size distribution of the small RNAs.

> files = list.files(file.path(root_path), pattern = "trimming_stats", recursive = T)
> isadapter = length(files) > 0

> names(files) = sapply(files, function(x) {
+     gsub("-ready.trimming_stats", "", basename(x))
+ })
> 
> 
> tab = data.frame()
> for (sample in rownames(metadata)) {
+     d = read.table(file.path(root_path, files[sample]), sep = " ")
+     tab = rbind(tab, d %>% mutate(sample = sample, group = metadata[sample, 
+         condition]))
+ }
> 
> 
> reads_adapter = tab %>% group_by(sample, group) %>% summarise(total = sum(V2))
> ggplot(reads_adapter, aes(x = sample, y = total, fill = group)) + geom_bar(stat = "identity", 
+     position = "dodge") + ggtitle("total number of reads with adapter") + ylab("# reads") + 
+     theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1))

> ggplot(tab, aes(x = V1, y = V2, group = sample)) + geom_bar(stat = "identity", 
+     position = "dodge") + facet_wrap(~group, ncol = 2) + ggtitle("size distribution") + 
+     ylab("# reads") + xlab("size") + theme(axis.text.x = element_text(angle = 90, 
+     vjust = 0.5, hjust = 1))

> files = list.files(file.path(root_path), pattern = "mirbase-ready", recursive = T, 
+     full.names = T)
> ismirbase = length(files) > 0
> mirdeep2_files = list.files(file.path(root_path), pattern = "novel-ready", recursive = T, 
+     full.names = T)
> ismirdeep2 = length(mirdeep2_files) > 0

miRNA

total miRNA expression annotated with mirbase

> names(files) = sapply(files, function(x) {
+     gsub("-mirbase-ready.counts", "", basename(x))
+ })
> 
> obj <- IsomirDataSeqFromFiles(files = files[rownames(design)], coldata = design, 
+     header = T, skip = 0)

> ggplot(data.frame(sample = colnames(counts(obj)), total = colSums(counts(obj)))) + 
+     geom_bar(aes(x = sample, y = total), stat = "identity") + theme(axis.text.x = element_text(angle = 90, 
+     vjust = 0.5, hjust = 1))

> mirna_step <- as.data.frame(colSums(counts(obj)))

Distribution of mirna expression

> ggplot(melt(counts(obj))) + geom_boxplot(aes(x = X2, y = value)) + scale_y_log10() + 
+     theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1))

cumulative distribution of miRNAs

> cs <- as.data.frame(apply(counts(obj), 2, function(x) {
+     cumsum(sort(x, decreasing = T))
+ }))
> cs$pos <- 1:nrow(cs)
> 
> ggplot((melt(cs, id.vars = "pos"))) + geom_line(aes(x = pos, y = value, color = variable)) + 
+     scale_y_log10()

Clustering

> counts = counts(obj)
> dds = DESeqDataSetFromMatrix(counts[rowSums(counts > 0) > 3, ], colData = design, 
+     design = ~1)
> vst = rlog(dds)
> 
> # pheatmap(assay(vst), annotation_col = design, show_rownames = F,
> # clustering_distance_cols = 'correlation', clustering_method = 'ward.D')

MDS plot

> # mds(assay(vst), condition = design[,condition])

complexity

Number of miRNAs with > 3 counts.

> kable(as.data.frame(colSums(counts > 10)))

	colSums(counts > 10)
K1P	201
191	209
133	182
198	177
160	180
Kutuzova	258
134	167
147	175
138	124
P40	98
P60	231
131	123
K5P	185
K2P	191
Bardina	241
P42	198
P64	222
144	214
127	184
201	178
154	162
K4P	200
K3P	167
Belaia	328

novel miRNA by mirdeep2

total miRNA expression

> files = mirdeep2_files
> 
> names(files) = sapply(files, function(x) {
+     gsub("-novel-ready.counts", "", basename(x))
+ })
> 
> obj_mirdeep <- IsomirDataSeqFromFiles(files = files[rownames(design)], design = design, 
+     header = T)

> ggplot(data.frame(sample = colnames(counts(obj_mirdeep)), total = colSums(counts(obj_mirdeep)))) + 
+     geom_bar(aes(x = sample, y = total), stat = "identity") + theme(axis.text.x = element_text(angle = 90, 
+     vjust = 0.5, hjust = 1))
> mirna_step <- as.data.frame(colSums(counts(obj)))

Distribution of mirna expression

> ggplot(melt(counts(obj_mirdeep))) + geom_boxplot(aes(x = X2, y = value)) + scale_y_log10() + 
+     theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1))

cumulative distribution of miRNAs

> cs <- as.data.frame(apply(counts(obj_mirdeep), 2, function(x) {
+     cumsum(sort(x, decreasing = T))
+ }))
> cs$pos <- 1:nrow(cs)
> 
> ggplot((melt(cs, id.vars = "pos"))) + geom_line(aes(x = pos, y = value, color = variable)) + 
+     scale_y_log10()

Clustering

> counts = counts(obj_mirdeep)
> dds = DESeqDataSetFromMatrix(counts[rowSums(counts > 0) > 3, ], colData = design, 
+     design = ~1)
> vst = rlog(dds)
> 
> pheatmap(assay(vst), annotation_col = design, show_rownames = F, clustering_distance_cols = "correlation", 
+     clustering_method = "ward.D")

MDS plot

> mds(assay(vst), condition = designi[, condition])

complexity

Number of miRNAs with > 3 counts.

> kable(as.data.frame(colSums(counts > 10)))

Others small RNA

The data was analyzed with seqcluster

This tools used all reads, uniquely mapped and multi-mapped reads. The first step is to cluster sequences in all locations they overlap. The second step is to create meta-clusters: is the unit that merge all clusters that share the same sequences. This way the output are meta-clusters, common sequences that could come from different region of the genome.

genome covered

In this table 1 means % of the genome with at least 1 read, and 0 means % of the genome without reads.

> fn_json = list.files(file.path(root_path), pattern = "seqcluster.json", recursive = T, 
+     full.names = T)
> seq_dir = dirname(fn_json)
> 
> isseqcluster = length(fn_json) > 0
> # cov_stats <- read.table(file.path(root_path, '..', 'align',
> # 'seqs_rmlw.bam_cov.tsv'),sep='\t',check.names = F)
> 
> # kable(cov_stats[cov_stats$V1=='genome',] %>%
> # dplyr::select(coverage=V2,ratio_genome=V5), row.names = FALSE)

The normal value for human data with strong small RNA signal is: 0.0002. This will change for smaller genomes.

classification

Number of reads in the data after each step:

• raw: initial reads
• cluster: after cluster detection
• multimap: after meta-cluster detection

> reads_stats <- read.table(file.path(seq_dir, "read_stats.tsv"), sep = "\t", 
+     check.names = F)
> ggplot(reads_stats, aes(x = V2, y = V1, fill = V3)) + geom_bar(stat = "identity", 
+     position = "dodge") + labs(list(x = "samples", y = "reads")) + scale_fill_brewer("steps", 
+     palette = "Set1") + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, 
+     hjust = 1))

> clus <- read.table(file.path(seq_dir, "counts.tsv"), header = T, sep = "\t", 
+     row.names = 1, check.names = FALSE)
> ann <- clus[, 2]
> toomany <- clus[, 1]
> clus_ma <- clus[, 3:ncol(clus)]
> clus_ma = clus_ma[, row.names(design)]

Check complex meta-clusters: This kind of events happen when there are small RNA over the whole genome, and all repetitive small rnas map to thousands of places and sharing many sequences in many positions. If any meta-cluster is > 40% of the total data, maybe it is worth to add some filters like: minimum number of counts -e or --min--shared in seqcluster prepare

> library(edgeR)
> clus_ma_norm = cpm(DGEList(clus_ma), normalized.lib.sizes = TRUE)
> head(clus_ma_norm[toomany > 0, ])

complexity

Number of miRNAs with > 10 counts.

> kable(as.data.frame(colSums(clus_ma > 10)))

Contribution by class

> rRNA <- colSums(clus_ma[grepl("rRNA", ann) & grepl("miRNA", ann) == F, ])
> miRNA <- colSums(clus_ma[grepl("miRNA", ann), ])
> tRNA <- colSums(clus_ma[grepl("tRNA", ann) & grepl("rRNA", ann) == F & grepl("ncRNA", 
+     ann) == F & grepl("miRNA", ann) == F, ])
> rmsk <- colSums(clus_ma[grepl("ncRNA", ann) & grepl("rRNA", ann) == F & grepl("miRNA", 
+     ann) == F, ])
> total <- colSums(clus_ma)
> 
> dd <- data.frame(samples = names(rRNA), rRNA = rRNA, miRNA = miRNA, tRNA = tRNA, 
+     ncRNA = rmsk, total = total)
> ggplot(melt(dd)) + geom_bar(aes(x = samples, y = value, fill = variable), stat = "identity", 
+     position = "dodge") + scale_fill_brewer(palette = "Set1") + theme(axis.text.x = element_text(angle = 90, 
+     vjust = 0.5, hjust = 1))
> 
> dd_norm = dd
> dd_norm[, 2:5] = sweep(dd[, 2:5], 1, dd[, 6], "/")
> ggplot(melt(dd_norm[, 1:5])) + geom_bar(aes(x = samples, y = value, fill = variable), 
+     stat = "identity", position = "dodge") + scale_fill_brewer(palette = "Set1") + 
+     theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1)) + 
+     labs(list(title = "relative proportion of small RNAs", y = "% reads"))

> # size_clus <- read.table(file.path(root_path, '..', 'seqcluster',
> # 'cluster', 'size_counts.tsv'),sep='\t',check.names = F)

Differential expression

DESeq2 is used for this analysis.

> library(DESeq2)
> # library(DEGreport)
> library(vsn)

> #' save file
> save_file <- function(dat, fn, basedir = ".") {
+     tab <- cbind(id = data.frame(id = row.names(dat)), as.data.frame(dat))
+     write.table(tab, file.path(basedir, fn), quote = F, sep = "\t", row.names = F)
+ }
> 
> filter_handle <- function(res) {
+     res_nona <- res[!is.na(res$padj), ]
+     keep <- res_nona$padj < 0.1
+     res_nona[keep, ]
+ }
> 
> handle_deseq2 = function(dds, summarydata, column, prefix, all_combs = NULL) {
+     if (is.null(all_combs)) 
+         all_combs = combn(levels(summarydata[, column]), 2, simplify = FALSE)
+     all_results = list()
+     contrast_strings = list()
+     rlog = rlog(dds)
+     for (comb in all_combs) {
+         contrast_string = paste(comb, collapse = "_vs_")
+         cat("\n\n## Comparison: ", contrast_string, "\n")
+         contrast = c(column, comb)
+         res = results(dds, contrast = contrast)
+         res = res[order(res$padj), ]
+         all_results = c(all_results, res)
+         contrast_strings = c(contrast_strings, contrast_string)
+         print_out(dds, rlog, res, paste0(prefix, "_", contrast_string))
+     }
+     names(all_results) = contrast_strings
+     return(all_results)
+ }
> 
> do_de = function(raw, summarydata, condition, minc = 3) {
+     dss = DESeqDataSetFromMatrix(countData = raw[rowMeans(raw) > minc, ], colData = summarydata, 
+         design = ~condition)
+     dss = DESeq(dss)
+     dss
+ }
> 
> do_norm = function(dss, path, prefix) {
+     rlog_ma = assay(rlog(dss))
+     count_ma = counts(dss, normalized = TRUE)
+     raw = counts(dss, normalized = FALSE)
+     
+     fn_log = paste0(prefix, "_log_matrix.txt")
+     save_file(rlog_ma, fn_log, path)
+     
+     fn_count = paste0(prefix, "_norm_matrix.txt")
+     save_file(count_ma, fn_count, path)
+     
+     fn_raw = paste0(prefix, "_raw_matrix.txt")
+     save_file(raw, fn_raw, path)
+ }
> 
> print_out = function(dss, rlog = NULL, res = NULL, prefix = "standard_") {
+     plotDispEsts(dss)
+     if (is.null(res)) 
+         res = results(dss)
+     if (is.null(rlog)) 
+         rlog = rlog(dss)
+     
+     rlogmat = assay(rlog)
+     design = as.data.frame(colData(dss)[, names(colData(dss)) != "sizeFactor", 
+         drop = FALSE])
+     
+     out_df = as.data.frame(res)
+     out_df = out_df[!is.na(out_df$padj), ]
+     out_df = out_df[order(out_df$padj), ]
+     do_norm(dss, root_file, prefix)
+     
+     cat("\n", paste(capture.output(summary(res))[1:8], collapse = "<br>"), "\n")
+     cat("\n\n### MA plot plot\n\n")
+     DESeq2::plotMA(res)
+     
+     cat("\n\n### Top DE miRNAs\n\n")
+     print(kable(head(out_df, 500)))
+     fn = paste(prefix, ".tsv", sep = "")
+     save_file(out_df, fn, root_file)
+     
+     sign = row.names(out_df)[out_df$padj < 0.05 & !is.na(out_df$padj) & abs(out_df$log2FoldChange) > 
+         0.5]
+     
+     cat("\n\n### Heatmap most significand(", length(sign), "), padj<0.05 and log2FC > 0.5\n")
+     if (length(sign) < 2) {
+         cat("Too few genes to plot.")
+     } else {
+         pheatmap(rlogmat[sign, ], show_rownames = F, annotation_col = design, 
+             clustering_distance_cols = "correlation", clustering_method = "ward.D2")
+         mds(rlogmat[sign, ], condition = design[, condition])
+     }
+     
+ }

Analysis for miRNA

> counts = counts(obj)
> dss = DESeqDataSetFromMatrix(countData = counts[rowSums(counts > 0) > 3, ], 
+     colData = design, design = ~batch)
> dss = DESeq(dss)
> all_results = handle_deseq2(dss, design, condition, "mirna_")

Comparison: Case_vs_Control

out of 309 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 0, 0%
LFC < 0 (down) : 0, 0%
outliers [1] : 0, 0%
low counts [2] : 0, 0%
(mean count < 1)

MA plot plot

Top DE miRNAs

	baseMean	log2FoldChange	lfcSE	stat	pvalue	padj
hsa-miR-4446-3p	1.173117e+02	-1.7510782	0.5408969	-3.2373607	0.0012064	0.3727802
hsa-miR-215-5p	5.550623e+01	-3.0409954	1.0877430	-2.7956928	0.0051789	0.8001339
hsa-let-7a-5p	2.876612e+03	-0.4247325	0.2649001	-1.6033689	0.1088532	0.9897340
hsa-let-7b-3p	9.132790e+00	0.9088302	1.7257141	0.5266400	0.5984436	0.9897340
hsa-let-7b-5p	1.305819e+03	-0.6020900	0.3225220	-1.8668183	0.0619270	0.9897340
hsa-let-7c-5p	6.673520e+01	-0.1479359	0.5613053	-0.2635569	0.7921214	0.9897340
hsa-let-7d-3p	2.216936e+02	0.0976425	0.3554953	0.2746662	0.7835727	0.9897340
hsa-let-7d-5p	2.668899e+02	-0.4713611	0.3841130	-1.2271415	0.2197694	0.9897340
hsa-let-7e-5p	1.318999e+02	-0.6773710	0.4511700	-1.5013654	0.1332611	0.9897340
hsa-let-7f-5p	3.927285e+03	-0.3550943	0.2604211	-1.3635388	0.1727128	0.9897340
hsa-let-7g-5p	6.413566e+02	-0.1312342	0.2316340	-0.5665587	0.5710140	0.9897340
hsa-let-7i-3p	4.067703e+00	1.0853977	2.5889128	0.4192485	0.6750346	0.9897340
hsa-let-7i-5p	4.386132e+03	-0.0473766	0.1722311	-0.2750759	0.7832580	0.9897340
hsa-miR-100-5p	2.058425e+02	-0.8028406	0.9313030	-0.8620617	0.3886536	0.9897340
hsa-miR-101-3p	1.048724e+03	0.1516663	0.2264770	0.6696765	0.5030640	0.9897340
hsa-miR-103a-3p	7.231298e+02	-0.1528991	0.2196632	-0.6960613	0.4863904	0.9897340
hsa-miR-106a-5p	5.649848e+00	-1.1476738	2.4322164	-0.4718634	0.6370243	0.9897340
hsa-miR-106b-3p	6.048039e+02	0.1457586	0.2403926	0.6063356	0.5442919	0.9897340
hsa-miR-106b-5p	5.747069e+01	0.4658792	0.8137734	0.5724925	0.5669884	0.9897340
hsa-miR-107	3.128301e+02	0.1938901	0.2256864	0.8591131	0.3902781	0.9897340
hsa-miR-10b-5p	9.282338e+03	0.1422749	0.2551230	0.5576720	0.5770684	0.9897340
hsa-miR-1180-3p	1.652681e+01	-0.8773711	1.2677634	-0.6920622	0.4888983	0.9897340
hsa-miR-122-5p	1.263745e+02	-0.9503060	1.0781193	-0.8814479	0.3780754	0.9897340
hsa-miR-1228-3p	1.703067e+00	2.4237955	2.9334506	0.8262609	0.4086561	0.9897340
hsa-miR-1233-3p	3.418357e+00	1.9230723	2.8485518	0.6751053	0.4996089	0.9897340
hsa-miR-1237-3p	7.417869e-01	0.4811011	2.9598228	0.1625439	0.8708776	0.9897340
hsa-miR-1249-3p	8.371246e+00	-0.3079099	1.8410116	-0.1672504	0.8671730	0.9897340
hsa-miR-125a-5p	4.212327e+02	0.0251176	0.4111365	0.0610930	0.9512851	0.9897340
hsa-miR-125b-1-3p	5.156065e+00	-0.2773576	2.1418981	-0.1294915	0.8969688	0.9897340
hsa-miR-125b-2-3p	1.889228e+01	-1.2191909	1.0516253	-1.1593397	0.2463177	0.9897340
hsa-miR-125b-5p	4.873373e+01	-1.2018740	1.4335622	-0.8383829	0.4018157	0.9897340
hsa-miR-126-3p	1.500232e+02	0.1341668	0.3296809	0.4069596	0.6840376	0.9897340
hsa-miR-126-5p	2.779631e+03	0.0665617	0.1471843	0.4522335	0.6511008	0.9897340
hsa-miR-1260a	1.931979e+01	0.1865530	1.0845346	0.1720120	0.8634281	0.9897340
hsa-miR-1260b	2.408625e+01	-0.5245016	1.0162188	-0.5161305	0.6057632	0.9897340
hsa-miR-127-3p	3.531496e+01	-0.1797581	0.7578457	-0.2371961	0.8125047	0.9897340
hsa-miR-1273g-3p	3.230204e+00	2.6346357	2.9152198	0.9037520	0.3661269	0.9897340
hsa-miR-1273h-3p	3.112916e+01	-0.5657286	1.1983308	-0.4720972	0.6368574	0.9897340
hsa-miR-1273h-5p	3.910070e+00	-4.8723286	2.8786561	-1.6925706	0.0905372	0.9897340
hsa-miR-128-1-5p	1.218457e+00	-2.7073653	2.9560272	-0.9158797	0.3597300	0.9897340
hsa-miR-128-3p	1.522878e+02	0.5382110	0.4591864	1.1720972	0.2411580	0.9897340
hsa-miR-1285-3p	5.657626e+01	0.7135496	0.6257323	1.1403434	0.2541433	0.9897340
hsa-miR-1294	2.816216e+00	-0.8930137	2.9161317	-0.3062323	0.7594278	0.9897340
hsa-miR-1301-3p	3.163295e+00	-0.5248523	2.5410743	-0.2065474	0.8363633	0.9897340
hsa-miR-1304-3p	2.256719e+01	0.1650978	1.0707122	0.1541944	0.8774564	0.9897340
hsa-miR-1306-5p	8.916681e+00	-0.5987065	1.7952763	-0.3334899	0.7387645	0.9897340
hsa-miR-1307-3p	1.393569e+02	-0.6316801	0.4509834	-1.4006726	0.1613120	0.9897340
hsa-miR-1307-5p	2.651325e+01	-0.4064155	0.7805608	-0.5206713	0.6025958	0.9897340
hsa-miR-130a-3p	2.158241e+02	0.2056033	0.4162873	0.4938977	0.6213784	0.9897340
hsa-miR-130b-3p	1.587200e+02	0.1890519	0.3466552	0.5453600	0.5855059	0.9897340
hsa-miR-130b-5p	7.291546e+00	-0.5285137	2.1440268	-0.2465052	0.8052912	0.9897340
hsa-miR-132-3p	7.373781e+00	-0.2115144	2.0046482	-0.1055120	0.9159695	0.9897340
hsa-miR-133a-3p	1.249593e+01	0.5826984	1.3551121	0.4300001	0.6671956	0.9897340
hsa-miR-134-5p	1.358842e+01	-1.9331193	1.9635839	-0.9844852	0.3248770	0.9897340
hsa-miR-1343-3p	1.059861e+00	-0.1524086	2.9442227	-0.0517653	0.9587157	0.9897340
hsa-miR-136-3p	2.514263e+00	-1.8731492	2.9336569	-0.6385032	0.5231462	0.9897340
hsa-miR-140-3p	2.890638e+02	-0.0712124	0.2242614	-0.3175419	0.7508324	0.9897340
hsa-miR-140-5p	1.155672e+00	0.7762916	2.9357396	0.2644279	0.7914502	0.9897340
hsa-miR-141-3p	1.049924e+01	-0.5676896	1.7191711	-0.3302112	0.7412404	0.9897340
hsa-miR-142-3p	3.811039e+01	-0.7335872	0.7479948	-0.9807384	0.3267218	0.9897340
hsa-miR-142-5p	2.165204e+03	0.1733325	0.1845051	0.9394455	0.3475020	0.9897340
hsa-miR-143-3p	1.021126e+03	0.0739971	0.2840853	0.2604751	0.7944973	0.9897340
hsa-miR-144-3p	1.396974e+02	-0.0471555	0.3792923	-0.1243250	0.9010580	0.9897340
hsa-miR-144-5p	1.429147e+02	0.0420010	0.3594477	0.1168486	0.9069800	0.9897340
hsa-miR-1468-5p	1.361331e+01	-0.5304961	1.4250202	-0.3722727	0.7096898	0.9897340
hsa-miR-146a-5p	2.200380e+02	-0.9296366	0.4823450	-1.9273274	0.0539388	0.9897340
hsa-miR-146b-3p	2.596720e+01	0.7803109	0.9876686	0.7900534	0.4294966	0.9897340
hsa-miR-146b-5p	1.320040e+02	-0.9852987	0.5825334	-1.6914029	0.0907599	0.9897340
hsa-miR-148a-3p	9.282941e+02	-0.2325730	0.2161405	-1.0760273	0.2819151	0.9897340
hsa-miR-148a-5p	1.345230e+01	-0.7477516	1.3358479	-0.5597580	0.5756445	0.9897340
hsa-miR-148b-3p	1.255292e+02	-0.1894917	0.5992074	-0.3162372	0.7518225	0.9897340
hsa-miR-148b-5p	5.590322e+00	-0.2253330	2.0787889	-0.1083963	0.9136813	0.9897340
hsa-miR-150-3p	2.151292e+01	-0.7003392	0.9904781	-0.7070718	0.4795218	0.9897340
hsa-miR-150-5p	2.334093e+03	0.1905607	0.2282471	0.8348877	0.4037810	0.9897340
hsa-miR-151a-3p	1.593568e+03	-0.4717284	0.2476443	-1.9048630	0.0567979	0.9897340
hsa-miR-151a-5p	3.781893e+02	-0.5487185	0.3302217	-1.6616668	0.0965796	0.9897340
hsa-miR-151b	2.858610e+02	-0.5672863	0.3234855	-1.7536684	0.0794873	0.9897340
hsa-miR-152-3p	9.750665e+00	-0.0922655	1.6936312	-0.0544779	0.9565544	0.9897340
hsa-miR-1538	8.383211e+00	0.9681386	2.0924233	0.4626877	0.6435882	0.9897340
hsa-miR-15a-5p	1.348105e+03	0.1833167	0.2563525	0.7150960	0.4745497	0.9897340
hsa-miR-15b-3p	7.970897e+00	0.9699628	1.7403483	0.5573383	0.5772963	0.9897340
hsa-miR-15b-5p	1.792181e+01	0.7768396	1.2605972	0.6162473	0.5377313	0.9897340
hsa-miR-16-2-3p	2.794029e+02	0.2336089	0.3514999	0.6646058	0.5063027	0.9897340
hsa-miR-16-5p	1.376560e+04	0.2954330	0.2138520	1.3814836	0.1671303	0.9897340
hsa-miR-17-3p	6.707430e+00	-1.8010936	1.8415192	-0.9780477	0.3280507	0.9897340
hsa-miR-17-5p	5.779895e+01	0.6565203	0.6545037	1.0030810	0.3158218	0.9897340
hsa-miR-181a-3p	1.630429e+01	-1.1485026	1.0547986	-1.0888360	0.2762262	0.9897340
hsa-miR-181a-5p	5.625357e+03	-0.4630207	0.1982523	-2.3355123	0.0195167	0.9897340
hsa-miR-181b-5p	3.032245e+02	-0.3203888	0.3000526	-1.0677753	0.2856219	0.9897340
hsa-miR-181c-3p	1.005727e+01	-0.7625968	1.6565651	-0.4603482	0.6452663	0.9897340
hsa-miR-181c-5p	4.454172e+01	-0.8766300	0.8924377	-0.9822870	0.3259585	0.9897340
hsa-miR-181d-5p	1.885782e+01	-0.7351017	1.4943220	-0.4919299	0.6227689	0.9897340
hsa-miR-182-5p	1.095136e+03	-0.2544514	0.3240845	-0.7851390	0.4323721	0.9897340
hsa-miR-183-5p	9.132330e+01	0.5023397	0.4567371	1.0998442	0.2714000	0.9897340
hsa-miR-184	5.216767e+00	-3.3177553	2.7777263	-1.1944140	0.2323160	0.9897340
hsa-miR-185-3p	6.671866e+00	-1.4463760	1.7200623	-0.8408858	0.4004119	0.9897340
hsa-miR-186-5p	7.976962e+02	0.2437897	0.1921742	1.2685868	0.2045885	0.9897340
hsa-miR-18a-3p	2.644800e+01	1.0313411	1.0722672	0.9618322	0.3361339	0.9897340
hsa-miR-18a-5p	2.480559e+00	5.2795146	2.9534547	1.7875726	0.0738450	0.9897340
hsa-miR-1908-3p	2.694095e+00	-1.8185790	2.9298815	-0.6207005	0.5347967	0.9897340
hsa-miR-191-3p	5.230849e+00	-0.4588651	2.1153578	-0.2169208	0.8282701	0.9897340
hsa-miR-191-5p	4.840738e+03	-0.5493475	0.2664710	-2.0615659	0.0392491	0.9897340
hsa-miR-1910-5p	4.493855e+00	-0.9018063	2.5030972	-0.3602762	0.7186406	0.9897340
hsa-miR-192-5p	2.646883e+03	-0.1119305	0.2736814	-0.4089810	0.6825536	0.9897340
hsa-miR-193b-5p	4.151194e+00	0.8850283	1.9426884	0.4555689	0.6487000	0.9897340
hsa-miR-194-5p	1.060741e+01	0.2331826	1.5727139	0.1482676	0.8821316	0.9897340
hsa-miR-196b-5p	8.128046e-01	3.6719407	2.9709486	1.2359489	0.2164776	0.9897340
hsa-miR-197-3p	7.782014e+01	-0.6376089	0.6294896	-1.0128982	0.3111088	0.9897340
hsa-miR-1976	5.586227e+00	-0.6867224	1.8023733	-0.3810101	0.7031958	0.9897340
hsa-miR-199a-3p	1.725521e+02	-0.1624746	0.3895302	-0.4171041	0.6766023	0.9897340
hsa-miR-199a-5p	2.730352e+01	-1.7031942	1.7921664	-0.9503549	0.3419319	0.9897340
hsa-miR-199b-3p	1.725521e+02	-0.1624746	0.3895302	-0.4171041	0.6766023	0.9897340
hsa-miR-199b-5p	3.249812e+00	3.4658323	2.9261974	1.1844151	0.2362488	0.9897340
hsa-miR-19a-3p	2.825061e+01	-0.3840865	0.9741336	-0.3942852	0.6933705	0.9897340
hsa-miR-19b-3p	1.387703e+02	0.1494692	0.3442957	0.4341302	0.6641939	0.9897340
hsa-miR-203a-3p	1.383410e+00	-0.5077005	2.9354157	-0.1729569	0.8626853	0.9897340
hsa-miR-204-5p	2.003461e+01	-1.1138680	1.0871385	-1.0245870	0.3055581	0.9897340
hsa-miR-205-5p	9.121066e-01	3.8366515	2.9681489	1.2926075	0.1961468	0.9897340
hsa-miR-20a-5p	3.122981e+01	1.0267148	0.7486714	1.3713824	0.1702558	0.9897340
hsa-miR-20b-5p	3.955695e+00	1.6499796	2.7599022	0.5978399	0.5499468	0.9897340
hsa-miR-21-3p	1.332583e+02	-0.4956899	0.5065591	-0.9785432	0.3278057	0.9897340
hsa-miR-21-5p	1.840423e+03	-0.1662228	0.1941064	-0.8563489	0.3918048	0.9897340
hsa-miR-210-3p	1.024451e+02	0.9881714	0.5568114	1.7746967	0.0759480	0.9897340
hsa-miR-2110	6.652628e+00	-0.1218213	1.6716368	-0.0728755	0.9419052	0.9897340
hsa-miR-22-3p	1.285651e+04	0.1402509	0.1533084	0.9148286	0.3602816	0.9897340
hsa-miR-221-3p	3.715524e+02	-0.0722097	0.2865120	-0.2520302	0.8010177	0.9897340
hsa-miR-222-3p	2.611339e+02	-0.9029614	0.6930709	-1.3028413	0.1926289	0.9897340
hsa-miR-223-3p	7.680330e+01	-0.4219456	0.5356766	-0.7876872	0.4308797	0.9897340
hsa-miR-223-5p	6.485655e+00	0.1653469	1.9159280	0.0863012	0.9312270	0.9897340
hsa-miR-224-5p	1.219155e+01	1.5096088	1.6334031	0.9242108	0.3553766	0.9897340
hsa-miR-23a-3p	7.475403e+01	-0.2781625	0.5912128	-0.4704947	0.6380016	0.9897340
hsa-miR-23b-3p	2.202882e+01	0.5754719	0.9314667	0.6178127	0.5366988	0.9897340
hsa-miR-24-3p	2.148575e+01	-0.4309232	1.0005803	-0.4306733	0.6667059	0.9897340
hsa-miR-25-3p	7.570001e+03	0.2640869	0.1777734	1.4855256	0.1374046	0.9897340
hsa-miR-25-5p	1.513128e+01	-0.5227497	1.3402922	-0.3900267	0.6965168	0.9897340
hsa-miR-26a-5p	1.955023e+03	-0.4799220	0.2804747	-1.7111064	0.0870615	0.9897340
hsa-miR-26b-3p	1.209176e+01	0.3309729	1.6454153	0.2011486	0.8405824	0.9897340
hsa-miR-26b-5p	2.982410e+02	-0.0383537	0.3448156	-0.1112295	0.9114343	0.9897340
hsa-miR-27a-3p	1.497695e+02	-0.1161732	0.4954996	-0.2344566	0.8146305	0.9897340
hsa-miR-27b-3p	2.577087e+03	-0.1632347	0.2962178	-0.5510630	0.5815905	0.9897340
hsa-miR-28-3p	1.213709e+03	-0.5490768	0.3062768	-1.7927469	0.0730134	0.9897340
hsa-miR-28-5p	1.555767e+01	-0.6202232	1.4323412	-0.4330136	0.6650049	0.9897340
hsa-miR-29a-3p	3.683222e+01	-0.5594061	0.9447917	-0.5920947	0.5537872	0.9897340
hsa-miR-29c-3p	1.480118e+01	0.6541013	1.4228419	0.4597147	0.6457210	0.9897340
hsa-miR-301a-3p	3.757443e+01	0.4348916	0.8202081	0.5302210	0.5959587	0.9897340
hsa-miR-301b-3p	3.248279e+00	0.6279382	2.7125981	0.2314896	0.8169345	0.9897340
hsa-miR-30a-3p	2.031430e+01	-0.3639608	1.0822030	-0.3363147	0.7366335	0.9897340
hsa-miR-30a-5p	6.498145e+02	0.0513273	0.2738684	0.1874159	0.8513346	0.9897340
hsa-miR-30b-5p	2.803222e+01	0.1502682	0.9879367	0.1521030	0.8791057	0.9897340
hsa-miR-30c-1-3p	6.382324e+00	0.6986072	2.0490109	0.3409485	0.7331423	0.9897340
hsa-miR-30c-5p	1.286042e+02	-0.3868808	0.5357312	-0.7221546	0.4701994	0.9897340
hsa-miR-30d-3p	8.447367e+00	0.6934669	1.6338854	0.4244281	0.6712536	0.9897340
hsa-miR-30d-5p	2.970298e+03	-0.4234799	0.2046038	-2.0697555	0.0384752	0.9897340
hsa-miR-30e-3p	7.027729e+01	0.0528754	0.5875705	0.0899899	0.9282952	0.9897340
hsa-miR-30e-5p	1.507557e+03	0.1378198	0.1582988	0.8706308	0.3839558	0.9897340
hsa-miR-31-5p	2.095653e+00	0.8492943	2.9158592	0.2912672	0.7708469	0.9897340
hsa-miR-3127-5p	4.277260e+00	-0.2981661	2.3524457	-0.1267473	0.8991404	0.9897340
hsa-miR-3158-3p	5.708525e+01	-0.4102718	0.6948529	-0.5904441	0.5548930	0.9897340
hsa-miR-3173-5p	4.280044e+00	0.1353291	2.0297308	0.0666734	0.9468417	0.9897340
hsa-miR-3196	6.458071e+00	1.1034429	2.1937072	0.5030037	0.6149616	0.9897340
hsa-miR-32-5p	1.121812e+01	-1.4371261	1.5702640	-0.9152131	0.3600798	0.9897340
hsa-miR-3200-5p	1.465153e+00	2.8375169	2.9475673	0.9626640	0.3357162	0.9897340
hsa-miR-320a	2.037335e+03	-0.1913208	0.2136474	-0.8954979	0.3705210	0.9897340
hsa-miR-320b	9.145408e+02	-0.0366483	0.2487234	-0.1473456	0.8828593	0.9897340
hsa-miR-320c	3.479381e+01	-0.4545042	0.8714153	-0.5215702	0.6019696	0.9897340
hsa-miR-320d	1.220930e+01	1.6816780	1.7756718	0.9470658	0.3436053	0.9897340
hsa-miR-324-3p	2.150629e+01	-0.0475586	1.1488997	-0.0413949	0.9669811	0.9897340
hsa-miR-326	8.957743e+00	0.8103217	1.6136031	0.5021816	0.6155398	0.9897340
hsa-miR-328-3p	7.070030e+01	-0.0633368	0.6372985	-0.0993832	0.9208340	0.9897340
hsa-miR-330-3p	7.914241e+00	-0.1378907	1.9689479	-0.0700327	0.9441676	0.9897340
hsa-miR-335-3p	1.960384e+00	-0.8420371	2.8373891	-0.2967648	0.7666461	0.9897340
hsa-miR-335-5p	1.262981e+01	0.4033391	1.3685245	0.2947255	0.7682036	0.9897340
hsa-miR-338-3p	5.232882e-01	1.6702469	2.9863896	0.5592863	0.5759663	0.9897340
hsa-miR-339-3p	4.626098e+01	-0.3315925	0.6520497	-0.5085386	0.6110757	0.9897340
hsa-miR-339-5p	3.048542e+00	-2.9565738	2.9466101	-1.0033814	0.3156769	0.9897340
hsa-miR-33b-5p	1.535911e+01	-0.2861842	1.4686742	-0.1948589	0.8455034	0.9897340
hsa-miR-340-5p	3.965905e+01	-0.0795026	0.7301363	-0.1088873	0.9132919	0.9897340
hsa-miR-342-3p	1.336017e+02	0.4714261	0.3343927	1.4097981	0.1585993	0.9897340
hsa-miR-342-5p	2.748378e+01	-1.8750157	0.7629805	-2.4574884	0.0139912	0.9897340
hsa-miR-345-5p	5.525524e+01	-0.6674935	0.6964793	-0.9583824	0.3378700	0.9897340
hsa-miR-3605-3p	2.739963e+01	-0.2823643	0.8575261	-0.3292778	0.7419457	0.9897340
hsa-miR-3605-5p	1.028969e+01	-0.6977263	1.5831150	-0.4407300	0.6594085	0.9897340
hsa-miR-361-3p	2.830992e+01	0.3356424	0.8716505	0.3850654	0.7001890	0.9897340
hsa-miR-361-5p	1.372664e+01	-0.6065232	1.1722752	-0.5173898	0.6048841	0.9897340
hsa-miR-3614-5p	5.292936e+00	-1.7187838	2.4027514	-0.7153398	0.4743991	0.9897340
hsa-miR-3615	4.779854e+02	-0.0102152	0.2296682	-0.0444779	0.9645234	0.9897340
hsa-miR-363-3p	4.649931e+02	-0.0343408	0.2639227	-0.1301167	0.8964741	0.9897340
hsa-miR-3677-3p	2.086193e+01	0.4909018	1.3993677	0.3508026	0.7257365	0.9897340
hsa-miR-3688-3p	7.864955e+00	0.0889661	1.8601439	0.0478275	0.9618537	0.9897340
hsa-miR-371b-5p	3.303231e+00	0.2842401	2.6987150	0.1053242	0.9161185	0.9897340
hsa-miR-374a-3p	1.307472e+00	-1.8786461	2.9538910	-0.6359903	0.5247827	0.9897340
hsa-miR-374a-5p	4.659765e+00	0.8081933	2.5019221	0.3230290	0.7466733	0.9897340
hsa-miR-374b-5p	2.223384e+00	-4.1008763	2.9527496	-1.3888331	0.1648835	0.9897340
hsa-miR-375	1.304481e+03	0.1922964	0.3796579	0.5064993	0.6125062	0.9897340
hsa-miR-378a-3p	6.467668e+02	0.2346783	0.2597371	0.9035225	0.3662487	0.9897340
hsa-miR-381-3p	1.479046e+01	-2.7279405	1.6410542	-1.6623098	0.0964507	0.9897340
hsa-miR-3928-3p	1.211848e+00	-1.5768183	2.9538714	-0.5338142	0.5934701	0.9897340
hsa-miR-409-3p	1.892605e+02	-1.1129786	0.8649053	-1.2868214	0.1981565	0.9897340
hsa-miR-410-3p	3.710803e+01	-0.4186121	1.1378783	-0.3678883	0.7129565	0.9897340
hsa-miR-411-5p	9.732762e+00	-0.5137241	1.7987873	-0.2855947	0.7751886	0.9897340
hsa-miR-421	6.692935e+01	0.3257584	0.4631269	0.7033892	0.4818133	0.9897340
hsa-miR-423-3p	1.243240e+03	-0.5273062	0.3443577	-1.5312745	0.1257016	0.9897340
hsa-miR-423-5p	4.625370e+04	0.0126829	0.1892032	0.0670335	0.9465551	0.9897340
hsa-miR-424-3p	6.775740e+01	-0.4556744	0.5756065	-0.7916421	0.4285694	0.9897340
hsa-miR-425-5p	4.392180e+02	-0.1596340	0.2548945	-0.6262748	0.5311347	0.9897340
hsa-miR-4286	2.939606e+00	3.0509505	2.9227595	1.0438596	0.2965504	0.9897340
hsa-miR-431-5p	1.280598e+01	-1.8479239	1.5689462	-1.1778122	0.2388715	0.9897340
hsa-miR-432-5p	6.177993e+00	-0.3939220	2.2370815	-0.1760874	0.8602252	0.9897340
hsa-miR-433-3p	3.782774e+00	-1.3192023	2.7964888	-0.4717352	0.6371158	0.9897340
hsa-miR-4433b-3p	6.587907e+00	0.3730779	2.0419556	0.1827062	0.8550286	0.9897340
hsa-miR-4433b-5p	1.353361e+02	-1.3179077	0.6774306	-1.9454505	0.0517208	0.9897340
hsa-miR-4451	3.859930e+00	-1.4155699	2.9134831	-0.4858686	0.6270603	0.9897340
hsa-miR-4508	1.135427e+01	1.5068167	1.4542484	1.0361481	0.3001331	0.9897340
hsa-miR-450b-5p	1.192782e+00	-3.2616107	2.9557002	-1.1034985	0.2698107	0.9897340
hsa-miR-4511	2.798610e+00	2.4105233	2.9166233	0.8264774	0.4085333	0.9897340
hsa-miR-451a	6.537374e+03	0.2096704	0.2484723	0.8438381	0.3987599	0.9897340
hsa-miR-454-3p	4.999364e+00	1.8084260	2.3004359	0.7861232	0.4317953	0.9897340
hsa-miR-454-5p	2.566508e+00	-0.8479704	2.9180107	-0.2905988	0.7713582	0.9897340
hsa-miR-4687-5p	1.765193e+00	2.3947650	2.9315274	0.8169001	0.4139855	0.9897340
hsa-miR-4732-3p	1.068189e+03	0.3155200	0.2708447	1.1649483	0.2440399	0.9897340
hsa-miR-4732-5p	2.549542e+01	-0.1147880	0.8487202	-0.1352484	0.8924155	0.9897340
hsa-miR-4738-3p	1.360550e+00	1.5066808	2.9323647	0.5138108	0.6073843	0.9897340
hsa-miR-4745-5p	1.005848e+00	-1.2520813	2.9558701	-0.4235914	0.6718638	0.9897340
hsa-miR-4746-5p	2.663552e+00	0.8093988	2.9105936	0.2780872	0.7809454	0.9897340
hsa-miR-483-3p	1.390569e+01	1.1632583	1.3477800	0.8630921	0.3880868	0.9897340
hsa-miR-483-5p	5.592367e+00	1.5005195	2.0783580	0.7219735	0.4703107	0.9897340
hsa-miR-484	1.251099e+03	0.1306619	0.2189319	0.5968155	0.5506306	0.9897340
hsa-miR-485-3p	2.684594e+00	-3.8410854	2.9516614	-1.3013300	0.1931455	0.9897340
hsa-miR-485-5p	2.411888e+01	-0.6499574	1.2897554	-0.5039385	0.6143046	0.9897340
hsa-miR-486-3p	6.675870e+02	0.0194743	0.2062300	0.0944302	0.9247674	0.9897340
hsa-miR-486-5p	1.909366e+06	-0.1041456	0.1983772	-0.5249878	0.5995917	0.9897340
hsa-miR-497-5p	2.196988e+00	-4.0832525	2.9528432	-1.3828206	0.1667199	0.9897340
hsa-miR-500a-3p	5.620478e+01	-0.6742100	0.7981697	-0.8446951	0.3982811	0.9897340
hsa-miR-501-3p	1.817807e+02	-0.2868875	1.1706862	-0.2450593	0.8064105	0.9897340
hsa-miR-5010-5p	4.985653e+01	-0.2788303	0.6039004	-0.4617157	0.6442852	0.9897340
hsa-miR-502-3p	1.803619e+01	1.2181845	1.5426246	0.7896830	0.4297129	0.9897340
hsa-miR-5189-5p	2.842364e+01	-1.1539018	1.1093986	-1.0401147	0.2982866	0.9897340
hsa-miR-5196-3p	5.621802e+00	1.2037958	2.4136874	0.4987373	0.6179645	0.9897340
hsa-miR-532-3p	4.383215e+00	1.0159665	2.5318309	0.4012774	0.6882159	0.9897340
hsa-miR-532-5p	2.772389e+02	0.5075031	0.3077581	1.6490326	0.0991410	0.9897340
hsa-miR-548k	1.676482e+00	-3.7168550	2.9540019	-1.2582439	0.2083035	0.9897340
hsa-miR-548o-3p	1.033866e+00	-1.8144975	2.9571193	-0.6136031	0.5394777	0.9897340
hsa-miR-550a-3p	3.090212e+01	0.7077796	0.9298579	0.7611697	0.4465557	0.9897340
hsa-miR-550a-5p	8.835303e+00	0.9045736	1.6938053	0.5340482	0.5933082	0.9897340
hsa-miR-574-5p	2.942832e+00	-0.3791866	2.7789430	-0.1364499	0.8914656	0.9897340
hsa-miR-576-5p	3.899148e+00	-1.5316744	2.5565790	-0.5991109	0.5490989	0.9897340
hsa-miR-584-5p	3.483011e+02	-0.3910877	0.4162928	-0.9394534	0.3474980	0.9897340
hsa-miR-589-3p	3.685529e+00	-1.2201479	2.4023817	-0.5078909	0.6115298	0.9897340
hsa-miR-589-5p	2.804317e+01	0.3599112	0.9271927	0.3881730	0.6978880	0.9897340
hsa-miR-6087	3.161886e+01	1.7043125	1.1451949	1.4882292	0.1366905	0.9897340
hsa-miR-627-5p	1.301832e+00	0.5589500	2.9308894	0.1907100	0.8487528	0.9897340
hsa-miR-628-3p	1.147239e+01	-0.2764541	1.6776230	-0.1647891	0.8691099	0.9897340
hsa-miR-629-5p	6.414754e+00	0.9538570	2.0439639	0.4666702	0.6407359	0.9897340
hsa-miR-636	2.137268e+01	2.0986653	1.1024194	1.9036904	0.0569505	0.9897340
hsa-miR-6503-3p	1.572075e+00	0.2594081	2.9249560	0.0886879	0.9293300	0.9897340
hsa-miR-6509-5p	1.289602e+00	-2.3463780	2.9553125	-0.7939526	0.4272230	0.9897340
hsa-miR-6511a-3p	2.892965e+01	-0.4789805	0.8732946	-0.5484753	0.5833656	0.9897340
hsa-miR-6511b-3p	3.373870e+01	0.8470853	0.9392779	0.9018474	0.3671379	0.9897340
hsa-miR-652-3p	1.322567e+02	-0.1038462	0.4737583	-0.2191966	0.8264969	0.9897340
hsa-miR-654-3p	2.312164e+01	-0.9764409	1.3774721	-0.7088644	0.4784086	0.9897340
hsa-miR-654-5p	1.924416e+00	-0.5553582	2.9235546	-0.1899599	0.8493405	0.9897340
hsa-miR-659-5p	9.945557e-01	-2.0954963	2.9577943	-0.7084659	0.4786560	0.9897340
hsa-miR-660-5p	6.746059e+01	0.2555450	0.4389813	0.5821318	0.5604779	0.9897340
hsa-miR-664a-5p	8.652952e-01	-2.8406580	2.9578231	-0.9603881	0.3368599	0.9897340
hsa-miR-671-3p	4.651973e+01	-0.3661999	0.8847247	-0.4139139	0.6789371	0.9897340
hsa-miR-6737-3p	1.078302e+00	-3.1295836	2.9563010	-1.0586147	0.2897753	0.9897340
hsa-miR-6741-3p	8.743621e+00	-0.8179357	1.6938832	-0.4828761	0.6291837	0.9897340
hsa-miR-6769b-3p	1.770185e+00	-0.2876322	2.9240939	-0.0983663	0.9216415	0.9897340
hsa-miR-6777-3p	1.543755e+01	1.1749886	1.1834426	0.9928564	0.3207799	0.9897340
hsa-miR-6798-3p	1.359214e+00	-0.2560602	2.9336623	-0.0872835	0.9304462	0.9897340
hsa-miR-6803-3p	1.150009e+01	-0.1712318	1.4417254	-0.1187687	0.9054586	0.9897340
hsa-miR-6819-3p	1.220527e+01	-0.1596336	1.5515493	-0.1028866	0.9180530	0.9897340
hsa-miR-6842-3p	6.655726e+00	-2.8694994	2.2352600	-1.2837430	0.1992319	0.9897340
hsa-miR-6852-5p	1.454415e+01	-1.4688653	1.1636409	-1.2623012	0.2068404	0.9897340
hsa-miR-744-5p	6.405701e+02	-0.9226496	0.5152411	-1.7907141	0.0733392	0.9897340
hsa-miR-760	9.865218e-01	-3.0101741	2.9568936	-1.0180191	0.3086689	0.9897340
hsa-miR-766-3p	1.650886e+00	-3.6967013	2.9540663	-1.2513941	0.2107907	0.9897340
hsa-miR-769-5p	4.932999e+01	-0.1738586	0.7658347	-0.2270185	0.8204094	0.9897340
hsa-miR-7706	9.451624e+01	0.4263884	0.4483035	0.9511154	0.3415458	0.9897340
hsa-miR-7849-3p	5.134284e+00	-2.1134400	2.5780610	-0.8197789	0.4123421	0.9897340
hsa-miR-7975	2.551770e+00	5.3188490	2.9532239	1.8010314	0.0716979	0.9897340
hsa-miR-7976	1.019890e+00	3.4740443	2.9663246	1.1711612	0.2415340	0.9897340
hsa-miR-7977	2.551893e+00	-1.7232206	2.9301374	-0.5881023	0.5564636	0.9897340
hsa-miR-874-3p	1.352271e+01	-0.0639682	1.3741463	-0.0465512	0.9628709	0.9897340
hsa-miR-877-5p	1.270339e+02	-0.5218205	0.5107527	-1.0216694	0.3069374	0.9897340
hsa-miR-885-5p	5.508751e+00	0.3718536	2.4211717	0.1535842	0.8779376	0.9897340
hsa-miR-889-3p	4.126692e+00	-1.0762568	2.5391789	-0.4238602	0.6716678	0.9897340
hsa-miR-9-5p	1.340156e+00	-1.3682049	2.9492682	-0.4639133	0.6427098	0.9897340
hsa-miR-92a-1-5p	2.517878e+00	-4.2793440	2.9518592	-1.4497114	0.1471390	0.9897340
hsa-miR-92a-3p	1.728223e+05	0.0067479	0.1646683	0.0409788	0.9673128	0.9897340
hsa-miR-92b-3p	4.576181e+03	0.0571806	0.1840029	0.3107594	0.7559835	0.9897340
hsa-miR-93-3p	1.269422e+01	0.8749392	1.6642474	0.5257266	0.5990782	0.9897340
hsa-miR-93-5p	1.353325e+03	0.1733048	0.2546588	0.6805370	0.4961645	0.9897340
hsa-miR-937-3p	3.191172e+00	0.1100142	2.5219438	0.0436228	0.9652051	0.9897340
hsa-miR-939-5p	4.958757e+00	-1.0282659	2.3028713	-0.4465147	0.6552255	0.9897340
hsa-miR-941	2.055334e+02	-0.4877183	0.4196487	-1.1622060	0.2451518	0.9897340
hsa-miR-942-5p	2.290622e+01	-0.0998242	0.9256959	-0.1078370	0.9141250	0.9897340
hsa-miR-96-5p	1.744215e+01	0.1377251	1.5346617	0.0897430	0.9284915	0.9897340
hsa-miR-98-5p	1.096868e+02	-0.6926702	0.4449413	-1.5567676	0.1195257	0.9897340
hsa-miR-99a-5p	3.671583e+01	0.3464107	1.2639303	0.2740742	0.7840276	0.9897340
hsa-miR-99b-3p	4.332216e+00	-1.3905753	2.5017276	-0.5558460	0.5783161	0.9897340
hsa-miR-99b-5p	6.174730e+02	-0.6832382	0.6926595	-0.9863983	0.3239377	0.9897340
hsa-miR-10a-5p	2.959196e+03	0.0073377	0.2863180	0.0256278	0.9795542	0.9984588
hsa-miR-155-5p	4.747784e+01	-0.0015208	0.7873443	-0.0019316	0.9984588	0.9984588
hsa-miR-181a-2-3p	3.656009e+01	-0.0167221	0.8103890	-0.0206347	0.9835371	0.9984588
hsa-miR-296-5p	1.557199e+00	-0.0090390	2.9264885	-0.0030887	0.9975356	0.9984588
hsa-miR-4326	8.683314e-01	-0.0290974	2.9522482	-0.0098560	0.9921362	0.9984588
hsa-miR-574-3p	9.432827e+00	-0.0125430	1.6856453	-0.0074411	0.9940630	0.9984588
hsa-miR-625-3p	1.883334e+01	0.0174548	1.1953129	0.0146027	0.9883492	0.9984588

Heatmap most significand( 0 ), padj<0.05 and log2FC > 0.5

Too few genes to plot.

> DESeq2::plotCounts(dds, put_gene_name_here)

Analysis for novel miRNA

> counts = counts(obj_mirdeep)
> dss_mirdeep2 = DESeqDataSetFromMatrix(countData = counts[rowSums(counts > 0) > 
+     3, ], colData = design, design = ~batch)
> 
> dss = DESeq(dss)
> all_results = handle_deseq2(dss_mirdeep2, design, condition, "mirdeep2_")

Analysis for isomiRs

> counts_iso = counts(isoCounts(obj, ref = T, iso5 = T, iso3 = T, add = T, subs = T))
> dss_iso = DESeqDataSetFromMatrix(countData = counts_iso[rowSums(counts_iso > 
+     0) > 3, ], colData = design, design = ~batch)
> 
> dss_iso = DESeq(dss_iso)
> all_results = handle_deseq2(dss_iso, design, condition, "isomirs_")

Comparison: Case_vs_Control

out of 1452 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 0, 0%
LFC < 0 (down) : 0, 0%
outliers [1] : 0, 0%
low counts [2] : 0, 0%
(mean count < 0)

MA plot plot

Top DE miRNAs

	baseMean	log2FoldChange	lfcSE	stat	pvalue	padj
hsa-let-7a-5p.iso.t5:0.t3:0.ad:A.mm:0	9.588277e+01	-0.6020388	0.6191190	-0.9724121	0.3308456	0.9996504
hsa-let-7a-5p.iso.t5:0.t3:gtt.ad:0.mm:0	1.728808e+02	-0.1803453	0.4682051	-0.3851845	0.7001008	0.9996504
hsa-let-7a-5p.iso.t5:0.t3:t.ad:0.mm:0	5.735042e+02	-0.2056926	0.3387824	-0.6071527	0.5437496	0.9996504
hsa-let-7a-5p.iso.t5:0.t3:T.ad:0.mm:0	5.128363e+01	0.1090441	0.6925134	0.1574613	0.8748813	0.9996504
hsa-let-7a-5p.iso.t5:0.t3:t.ad:A.mm:0	2.059645e+01	0.7516674	1.0797526	0.6961478	0.4863363	0.9996504
hsa-let-7a-5p.iso.t5:0.t3:t.ad:AA.mm:0	1.495145e-01	-0.5206976	2.9896766	-0.1741652	0.8617356	0.9996504
hsa-let-7a-5p.iso.t5:0.t3:tt.ad:0.mm:0	5.473327e+02	0.0127997	0.3495922	0.0366132	0.9707934	0.9996504
hsa-let-7a-5p.iso.t5:0.t3:tt.ad:A.mm:0	1.173307e+01	-0.0567773	1.3494220	-0.0420752	0.9664387	0.9996504
hsa-let-7a-5p.iso.t5:G.t3:0.ad:0.mm:0	1.476116e+00	1.6484097	2.9300879	0.5625803	0.5737207	0.9996504
hsa-let-7a-5p.iso.t5:t.t3:0.ad:0.mm:0	3.655656e+00	1.7779908	2.5066811	0.7093008	0.4781379	0.9996504
hsa-let-7a-5p.ref.t5:0.t3:0.ad:0.mm:0	1.455230e+03	-0.7981226	0.3988148	-2.0012363	0.0453669	0.9996504
hsa-let-7b-3p.iso.t5:0.t3:c.ad:0.mm:0	1.593853e+00	-3.6342555	2.9541740	-1.2302104	0.2186183	0.9996504
hsa-let-7b-3p.iso.t5:0.t3:c.ad:T.mm:0	5.713709e+00	2.1918694	2.1307763	1.0286718	0.3036339	0.9996504
hsa-let-7b-5p.iso.t5:0.t3:0.ad:A.mm:0	3.361454e+01	-1.0163194	1.0500458	-0.9678811	0.3331038	0.9996504
hsa-let-7b-5p.iso.t5:0.t3:0.ad:AA.mm:0	6.210503e+00	-3.3612349	2.1977475	-1.5293999	0.1261653	0.9996504
hsa-let-7b-5p.iso.t5:0.t3:0.ad:AT.mm:0	2.431846e+01	-1.2851234	1.4312565	-0.8978987	0.3692396	0.9996504
hsa-let-7b-5p.iso.t5:0.t3:gtt.ad:0.mm:0	4.215716e+00	-1.1083073	2.4270932	-0.4566398	0.6479300	0.9996504
hsa-let-7b-5p.iso.t5:0.t3:t.ad:0.mm:0	3.825639e+02	-0.5574198	0.3957617	-1.4084731	0.1589910	0.9996504
hsa-let-7b-5p.iso.t5:0.t3:T.ad:0.mm:0	5.117385e+01	-1.3369962	0.8749063	-1.5281593	0.1264730	0.9996504
hsa-let-7b-5p.iso.t5:0.t3:t.ad:A.mm:0	1.154634e+01	0.4162085	1.5141905	0.2748719	0.7834146	0.9996504
hsa-let-7b-5p.iso.t5:0.t3:t.ad:AA.mm:0	4.465789e+00	-0.1367581	2.4204074	-0.0565021	0.9549418	0.9996504
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hsa-miR-181b-5p.ref.t5:0.t3:0.ad:0.mm:0	8.712468e+01	-0.6113906	0.5858492	-1.0435972	0.2966718	0.9996504
hsa-miR-181c-3p.iso.t5:a.t3:0.ad:T.mm:0	2.888070e+00	-4.4729621	2.9505996	-1.5159502	0.1295319	0.9996504
hsa-miR-181c-3p.iso.t5:a.t3:C.ad:0.mm:0	4.070887e+00	0.8038690	2.3080326	0.3482919	0.7276210	0.9996504
hsa-miR-181c-5p.iso.t5:0.t3:t.ad:0.mm:0	1.200958e+01	-1.0705486	1.7341846	-0.6173210	0.5370230	0.9996504
hsa-miR-181c-5p.iso.t5:0.t3:T.ad:0.mm:0	1.296518e+01	-0.3025221	1.5307096	-0.1976352	0.8433305	0.9996504
hsa-miR-181c-5p.ref.t5:0.t3:0.ad:0.mm:0	1.660782e+01	-1.0180725	1.3394173	-0.7600861	0.4472031	0.9996504
hsa-miR-181d-5p.iso.t5:0.t3:t.ad:0.mm:0	6.461194e+00	-5.6038983	2.2284609	-2.5146945	0.0119136	0.9996504
hsa-miR-181d-5p.iso.t5:0.t3:T.ad:0.mm:0	2.824301e+00	-1.4743228	2.9224496	-0.5044818	0.6139228	0.9996504
hsa-miR-181d-5p.ref.t5:0.t3:0.ad:0.mm:0	5.200937e+00	-0.3000257	2.3503535	-0.1276513	0.8984249	0.9996504
hsa-miR-182-5p.iso.t5:0.t3:act.ad:0.mm:0	8.422257e+01	0.5350023	0.4064602	1.3162478	0.1880909	0.9996504
hsa-miR-182-5p.iso.t5:0.t3:ct.ad:0.mm:0	6.767172e+02	-0.3085436	0.3877760	-0.7956747	0.4262211	0.9996504
hsa-miR-182-5p.iso.t5:0.t3:G.ad:0.mm:0	8.073504e+00	-0.6309873	1.6701581	-0.3778009	0.7055785	0.9996504
hsa-miR-182-5p.iso.t5:0.t3:G.ad:A.mm:0	6.713747e+00	-0.9878568	1.8061650	-0.5469361	0.5844226	0.9996504
hsa-miR-182-5p.iso.t5:0.t3:G.ad:T.mm:0	4.030252e+00	-1.8516978	2.2529008	-0.8219172	0.4111240	0.9996504
hsa-miR-182-5p.iso.t5:0.t3:GG.ad:0.mm:0	1.514564e+01	-1.1750445	1.2957191	-0.9068667	0.3644773	0.9996504
hsa-miR-182-5p.iso.t5:0.t3:GG.ad:A.mm:0	1.836585e+00	-0.5646717	2.9257073	-0.1930035	0.8469562	0.9996504
hsa-miR-182-5p.iso.t5:0.t3:GGT.ad:0.mm:0	1.518356e+00	-1.6742342	2.9485740	-0.5678115	0.5701630	0.9996504
hsa-miR-182-5p.iso.t5:0.t3:t.ad:0.mm:0	2.464338e+01	0.0646490	1.1051235	0.0584993	0.9533509	0.9996504
hsa-miR-182-5p.ref.t5:0.t3:0.ad:0.mm:0	2.469910e+02	0.0698157	0.3606224	0.1935979	0.8464907	0.9996504
hsa-miR-183-5p.iso.t5:0.t3:ct.ad:0.mm:0	7.994245e+00	1.8625990	2.0203707	0.9219095	0.3565758	0.9996504
hsa-miR-183-5p.iso.t5:0.t3:G.ad:0.mm:0	9.214350e+00	-2.2806715	1.9685239	-1.1585694	0.2466318	0.9996504
hsa-miR-183-5p.iso.t5:0.t3:G.ad:A.mm:0	1.084696e+00	-1.1366887	2.9531242	-0.3849106	0.7003037	0.9996504
hsa-miR-183-5p.iso.t5:t.t3:0.ad:0.mm:0	1.742299e+01	0.9994735	1.3187440	0.7578980	0.4485120	0.9996504
hsa-miR-183-5p.iso.t5:t.t3:G.ad:0.mm:0	1.221511e+01	0.3378343	1.6069977	0.2102270	0.8334905	0.9996504
hsa-miR-183-5p.iso.t5:t.t3:G.ad:A.mm:0	2.036200e+01	1.9869384	1.3001118	1.5282827	0.1264424	0.9996504
hsa-miR-183-5p.iso.t5:t.t3:GT.ad:0.mm:0	6.862700e+00	1.8741042	2.0689034	0.9058443	0.3650183	0.9996504
hsa-miR-183-5p.ref.t5:0.t3:0.ad:0.mm:0	8.127591e+00	-0.7673758	1.6860515	-0.4551319	0.6490144	0.9996504
hsa-miR-185-3p.iso.t5:0.t3:c.ad:0.mm:0	3.328344e+00	-1.4486465	2.3478483	-0.6170103	0.5372280	0.9996504
hsa-miR-186-5p.iso.t5:0.t3:ct.ad:0.mm:0	2.835330e+00	2.4252855	2.8469500	0.8518890	0.3942757	0.9996504
hsa-miR-186-5p.iso.t5:0.t3:t.ad:0.mm:0	2.272897e+02	0.2261395	0.2837446	0.7969825	0.4254612	0.9996504
hsa-miR-186-5p.iso.t5:0.t3:T.ad:0.mm:0	7.900937e+01	-0.5138605	0.7537385	-0.6817490	0.4953977	0.9996504
hsa-miR-186-5p.iso.t5:0.t3:TT.ad:0.mm:0	1.118522e+01	-3.2384838	1.7583116	-1.8418145	0.0655023	0.9996504

Heatmap most significand( 0 ), padj<0.05 and log2FC > 0.5

Too few genes to plot.

> DESeq2::plotCounts(dds, put_gene_name_here)

Analysis for clusters

> dss_clus = DESeqDataSetFromMatrix(countData = clus_ma[rowSums(clus_ma > 0) > 
+     3, ], colData = design, design = ~batch)
> 
> dss_clus = DESeq(dss_clus)
> all_results = handle_deseq2(dss_clus, design, condition, "clusters_")

Files

Files generated contains raw count, normalized counts, log2 normalized counts and DESeq2 results.

R Session Info

(useful if replicating these results)

> sessionInfo()

R version 3.4.4 (2018-03-15)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 16299)

Matrix products: default

locale:
[1] LC_COLLATE=Russian_Russia.1251  LC_CTYPE=Russian_Russia.1251   
[3] LC_MONETARY=Russian_Russia.1251 LC_NUMERIC=C                   
[5] LC_TIME=Russian_Russia.1251    

attached base packages:
[1] parallel  stats4    stats     graphics  grDevices utils     datasets 
[8] methods   base     

other attached packages:
 [1] vsn_3.46.0                 bindrcpp_0.2              
 [3] pheatmap_1.0.8             isomiRs_1.6.0             
 [5] RcppEigen_0.3.3.4.0        TMB_1.7.12                
 [7] DiscriMiner_0.1-29         dplyr_0.7.4               
 [9] devtools_1.13.5            gridExtra_2.3             
[11] gtools_3.5.0               CHBUtils_0.1              
[13] genefilter_1.60.0          DESeq2_1.18.1             
[15] SummarizedExperiment_1.8.1 DelayedArray_0.4.1        
[17] matrixStats_0.53.1         Biobase_2.38.0            
[19] GenomicRanges_1.30.0       GenomeInfoDb_1.14.0       
[21] IRanges_2.12.0             S4Vectors_0.16.0          
[23] BiocGenerics_0.24.0        reshape_0.8.7             
[25] ggplot2_2.2.1              knitr_1.20                

loaded via a namespace (and not attached):
 [1] minqa_1.2.4            colorspace_1.3-2       rprojroot_1.3-2       
 [4] htmlTable_1.11.2       XVector_0.18.0         base64enc_0.1-3       
 [7] rstudioapi_0.7         affyio_1.48.0          bit64_0.9-7           
[10] AnnotationDbi_1.40.0   splines_3.4.4          geneplotter_1.56.0    
[13] Formula_1.2-2          nloptr_1.0.4           annotate_1.56.1       
[16] cluster_2.0.6          gamlss.dist_5.0-4      readr_1.1.1           
[19] compiler_3.4.4         backports_1.1.2        assertthat_0.2.0      
[22] Matrix_1.2-12          lazyeval_0.2.1         limma_3.34.9          
[25] formatR_1.5            acepack_1.4.1          htmltools_0.3.6       
[28] tools_3.4.4            gtable_0.2.0           glue_1.2.0            
[31] GenomeInfoDbData_1.0.0 affy_1.56.0            Rcpp_0.12.16          
[34] gdata_2.18.0           preprocessCore_1.40.0  nlme_3.1-131.1        
[37] stringr_1.3.0          lme4_1.1-15            XML_3.98-1.10         
[40] zlibbioc_1.24.0        MASS_7.3-49            scales_0.5.0          
[43] BiocInstaller_1.28.0   hms_0.4.2              gamlss.data_5.0-0     
[46] RColorBrewer_1.1-2     yaml_2.1.18            memoise_1.1.0         
[49] rpart_4.1-13           latticeExtra_0.6-28    stringi_1.1.7         
[52] RSQLite_2.0            highr_0.6              checkmate_1.8.5       
[55] caTools_1.17.1         BiocParallel_1.12.0    rlang_0.2.0           
[58] pkgconfig_2.0.1        bitops_1.0-6           evaluate_0.10.1       
[61] lattice_0.20-35        purrr_0.2.4            bindr_0.1.1           
[64] htmlwidgets_1.0        labeling_0.3           bit_1.1-12            
[67] GGally_1.3.2           plyr_1.8.4             magrittr_1.5          
[70] R6_2.2.2               gplots_3.0.1           Hmisc_4.1-1           
[73] DBI_0.8                pillar_1.2.1           foreign_0.8-69        
[76] withr_2.1.2            survival_2.41-3        RCurl_1.95-4.10       
[79] nnet_7.3-12            tibble_1.4.2           gamlss_5.0-6          
[82] KernSmooth_2.23-15     rmarkdown_1.9          locfit_1.5-9.1        
[85] grid_3.4.4             data.table_1.10.4-3    blob_1.1.0            
[88] digest_0.6.15          xtable_1.8-2           tidyr_0.8.0           
[91] munsell_0.4.3