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3
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105學年(下)課程2017Spring / R packages / Salmon+DeSeq2
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於: 六月 24, 2019, 01:13:59 am
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install.packages("BiocManager")
install.packages("devtools")
devtools::install_github("stephenturner/annotables")
BiocManager::install("SummarizedExperiment")
BiocManager::install("DESeq2")a
BiocManager::install("pathview")
BiocManager::install("gage")
BiocManager::install("genefilter")
BiocManager::install("AnnotationDbi")
BiocManager::install("org.Hs.eg.db")
library("tximport")
library("readr")
library("tximportData")
dir <- system.file("extdata", package="tximportData")
tx2gene <- read_csv(file.path(dir, "tx2gene.gencode.v27.csv"))
curpath <- "I:/06 Salmon/SRP068976-HCC"
setwd(curpath)
files = c("SRR3129887.sf","SRR3129888.sf","SRR3129889.sf","SRR3129890.sf","SRR3129891.sf","SRR3129892.sf", "SRR3129893.sf", "SRR3129894.sf", "SRR3129895.sf",
"SRR3129913.sf","SRR3129921.sf", "SRR3129922.sf", "SRR3129923.sf")
cond = c("A","A","A","A","A","A","A","A", "B","B","B","B","B")
txi <- tximport(files, type="salmon", tx2gene=tx2gene)
samples <- read.table("samples.txt", header=TRUE)
samples$condition <-cond
library("DESeq2")
dds <- DESeqDataSetFromTximport(txi,
colData = samples,
design = ~ condition)
dds <- DESeq(dds)
res <- results(dds)
summary(res)
DESeq2::plotDispEsts(dds, main="Dispersion Estimates")
plotMA(res, main="Differentially Expressed Genes ", ylim=c(-2,2))
library("annotables")
library("AnnotationDbi")
library("org.Hs.eg.db")
eids=gsub("\\..*","",row.names(res))
res$symbol = mapIds(org.Hs.eg.db,
keys=eids,
column="SYMBOL",
keytype="ENSEMBL",
multiVals="first")
res$entrez = mapIds(org.Hs.eg.db,
keys=eids,
column="ENTREZID",
keytype="ENSEMBL",
multiVals="first")
res$name = mapIds(org.Hs.eg.db,
keys=eids,
column="GENENAME",
keytype="ENSEMBL",
multiVals="first")
write.csv(as.data.frame(res),file="SRP068976-HCC-de.csv")
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4
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學術活動 / 研討會資訊 / IEEE DataCom 2019-20191118
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於: 六月 11, 2019, 11:34:32 pm
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The 5th IEEE International Conference on Big Data Intelligence and Computing Kaohsiung, Taiwan, Nov. 18-21, 2019 http://www.cs.ccu.edu.tw/~conference/datacom2019/Research Article (regular track): Paper Submission July 20, 2019 Author Notification August 31, 2019 Poster/Special Session: Paper Submission September 10, 2019 Author Notification September 26, 2019 Registration Due: October 10, 2019 Camera ready submission: October 20, 2019 |
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6
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學術活動 / 期刊資訊 / Special Issue on Generative Adversarial Networks-20190515
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於: 五月 12, 2019, 01:41:47 am
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International Journal of Computer Vision Special Issue on Generative Adversarial Networks for Computer Vision Guest Editors Jun-Yan Zhu, Massachusetts Institute of Technology Hongsheng Li, The Chinese University of Hong Kong Eli Shechtman, Adobe Research Ming-Yu Liu, NVIDIA Research Jan Kautz, NVIDIA Research Antonio Torralba, Massachusetts Institute of Technology Scope Generative Adversarial Networks (GANs) have been at the forefront of research on generative models in the past few years. GANs can approximate real data distribution and synthesize realistic data samples. The concept of GANs is not limited to generating samples from certain data distributions but also has inspired many other research trends, including image generation and editing, feature learning, visual domain adaptation, data generation and augmentation for visual recognition, and many other practical applications, often leading to state of the art results. While GANs have achieved substantial progress for various computer vision applications, many issues remain to be solved and new research problems emerge. For example, what are the appropriate network structures and objective functions for generating visual data (e.g., images, videos, 3D)? What are the proper metrics for evaluating deep generative models? How can we improve the photorealism and resolution of the synthesized data samples? How can the generated data help solve other computer vision tasks? This special issue provides a significant collective contribution to this emerging field of study. Specifically, we aim to solicit original contributions that include the following three areas: Theoretical analysis and foundations: Authors are invited to submit manuscripts on the theoretical considerations of GANs and its variants such as the convergence and the limitations of models. Novel formulations and training methods: We would like to solicit submissions on new network architectures, robust objective functions, and better training procedures that can improve the quality, resolution, and training stability of GANs-based models. New computer vision applications: We welcome new work that explores GANs-based approaches for computer vision applications. We encourage original research in these fields to discuss how they adopt adversarial learning to individual computer vision applications. Besides, we also encourage submissions on solving cross-disciplinary research problems through adversarial learning, such as vision and language as well as robotics and vision http://people.csail.mit.edu/junyanz/ijcvgans.html |
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10
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程式語言摘要 / Cuda+TF / CUDA installation in 16.04
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於: 二月 13, 2019, 02:00:28 pm
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Ubuntu 16.04 安裝 TensorFlow GPU GTX 1060
sudo dpkg -i cuda-repo-ubuntu1604-9-0-local_9.0.176-1_amd64.deb
sudo apt-key add /var/cuda-repo-<version>/7fa2af80.pub
sudo apt-get update
verify CUDA installation in 16.04
nvidia-smi
nvcc --version
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14
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學術活動 / 研討會資訊 / IETAC2019-資訊教育與科技應用研討會
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於: 一月 27, 2019, 01:14:56 am
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會議重要日期規劃 研討會與專題競賽投稿說明： - 論文投稿方式採線上投稿，投稿網址及論文格式說明請參閱http://ietac.hust.edu.tw
論文徵稿開始日期：108年01月20日
論文截稿日期：108年03月18日
論文接受通知：108年04月12日
論文定稿截止日期：108年04月19日
會議時間與舉辦地點 會議時間：中華民國108年5月17日(星期五) 會議地點：中臺科技大學 勤學樓 B1 國際會議廳 聯絡人：中臺科技大學資訊管理系 謝嘉芳小姐 電話：(04)22391647轉7701 E-mail:106995@ctust.edu.tw |
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