<jupyter_start><jupyter_text>This is the less than .00001 data  For paper analysis look below for "greater than .00001"<jupyter_code>data = read.table(file= "T5_filtered_less_000001.csv", header = T, sep = ',', check.names=FALSE, row.names=1)
data
mapping = read.table(file = "Deseq2_T5_mapping.txt", header = T, sep="\t")
mapping
dds = DESeqDataSetFromMatrix(countData = data, colData = mapping, design = ~ Treatment)
dds
dds$Treatment = relevel(dds$Treatment, ref = "Let")
dds = DESeq(dds, fitType="local")
dds
result = results(dds)
res = result[order(result$padj),]
res
write.table(res, file = "T5_filtered_less_000001_PvL_deseq.csv", sep = ",", quote = FALSE, row.names = TRUE, col.names = NA)
LvCoL = results(dds, c("Treatment", "CoL", "Let"))
LvCoL = LvCoL[order(result$padj),]
write.table(LvCoL, file = "T5_filtered_less_000001_CoLvL_deseq.csv", sep = ",", quote = FALSE, row.names = TRUE, col.names = NA)<jupyter_output><empty_output><jupyter_text>Greater than .00001<jupyter_code>data = read.table(file= "T5_filtered_greater_00001.csv", header = T, sep = ',', check.names=FALSE, row.names=1)
data
dds = DESeqDataSetFromMatrix(countData = data, colData = mapping, design = ~ Treatment)
dds
dds$Treatment = relevel(dds$Treatment, ref = "Let")
dds = DESeq(dds, fitType="local")
dds
result = results(dds)
res = result[order(result$padj),]
res
write.table(res, file = "T5_filtered_greater_00001_PvL_deseq.csv", sep = ",", quote = FALSE, row.names = TRUE, col.names = NA)
LvCoL = results(dds, c("Treatment", "CoL", "Let"))
LvCoL = LvCoL[order(result$padj),]
write.table(LvCoL, file = "T5_filtered_greater_00001_CoLvL_deseq.csv", sep = ",", quote = FALSE, row.names = TRUE, col.names = NA)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.16037187681580475, 'fraction_numerical': 0.0337013364323068, 'mean_word_length': 6.58590308370044, 'pattern_counts': {'":': 0, '<': 9, '<?xml version=': 0, '>': 9, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '606185', 'license': 'MIT', 'n_tokens_mistral': 726, 'n_tokens_neox': 661, 'n_words': 137, 'path': 'DESEQ2/WGS/T5/T5_filtered_deseq2.ipynb', 'repo_name': 'bryansho/PCOS_WGS_16S_metabolome', 'size': '1721'}