R Programming Lecture Notes
Author:
shrikant
Last Updated:
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Abstract:
R Programming Lecture Notes
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R Programming Lecture Notes
\begin
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\section{R programming (6900) Basics of NGS and Micro-array analysis}
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\url{https://www.youtube.com/watch?v=v1cTNhiZ2_c}
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\subsection{Survival Plots: Kaplan Meier Analysis}
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The first thing to do is to use function Surv() to build the standard survival object. The variable t1 records the time to death or the censored time. A plus sign after the time in the print out indicates censoring. The formula instructs the survfit() function to fit a model with intercept only.
\subsection{What is censoring? Understanding your data is extremely important}
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Complete data means that the value of each sample unit is observed or known. For example, if we had to compute the average test score for a sample of ten students, complete data would consist of the known score for each student. Likewise in the case of life data analysis, our data set (if complete) would be composed of the times-to-failure of all units in our sample. For example, if we tested five units and they all failed (and their times-to-failure were recorded), we would then have complete information as to the time of each failure in the sample.
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The most common case of censoring is what is referred to as right censored data, or suspended data. In the case of life data, these data sets are composed of units that did not fail. For example, if we tested five units and only three had failed by the end of the test, we would have right censored data (or suspension data) for the two units that did not failed. The term right censored implies that the event of interest (i.e., the time-to-failure) is to the right of our data point. In other words, if the units were to keep on operating, the failure would occur at some time after our data point (or to the right on the time scale).
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The second type of censoring is commonly called interval censored data. Interval censored data reflects uncertainty as to the exact times the units failed within an interval. This type of data frequently comes from tests or situations where the objects of interest are not constantly monitored. For example, if we are running a test on five units and inspecting them every 100 hours, we only know that a unit failed or did not fail between inspections. Specifically, if we inspect a certain unit at 100 hours and find it operating, and then perform another inspection at 200 hours to find that the unit is no longer operating, then the only information we have is that the unit failed at some point in the interval between 100 and 200 hours. This type of censored data is also called inspection data by some authors.
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The third type of censoring is similar to the interval censoring and is called left censored data. In left censored data, a failure time is only known to be before a certain time. For instance, we may know that a certain unit failed sometime before 100 hours but not exactly when. In other words, it could have failed any time between 0 and 100 hours. This is identical to interval censored data in which the starting time for the interval is zero.
\subsection{Using Intercepts?}
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\subsection{R exercise on KM plot}
\subsection{Microarray Technique:}
A DNA microarray (biochip) is a collection of microscopic DNA spots attached to a solid surface. Scientists use DNA microarrays to measure the expression levels of large numbers of genes simultaneously or to genotype multiple regions of a genome.
General technique of performing Microarray
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One-channel vs two-channel microarrays:
Two-color microarrays or two-channel microarrays are typically hybridized with cDNA prepared from two samples to be compared (e.g. diseased tissue versus healthy tissue) and that are labeled with two different fluorophores.
Fluorescent dyes commonly used for cDNA labeling include Cy3, which has a fluorescence emission wavelength of 570 nm (corresponding to the green part of the light spectrum), and Cy5 with a fluorescence emission wavelength of 670 nm (corresponding to the red part of the light spectrum). The two Cy-labeled cDNA samples are mixed and hybridized to a single microarray that is then scanned in a microarray scanner to visualize fluorescence of the two fluorophores after excitation with a laser beam of a defined wavelength
Data analysis:
1. Image analysis
2. Data processing: background subtraction (based on global or local background), determination of spot intensities and intensity ratios, visualisation of data (e.g. see MA plot), and log-transformation of ratios, global or local normalization of intensity ratios, and segmentation into different copy number regions using step detection algorithms.
3. Class discovery analysis: This analytic approach, sometimes called unsupervised classification or knowledge discovery, tries to identify whether microarrays (objects, patients, mice, etc.) or genes cluster together in groups. Identifying naturally existing groups of objects.
4. Class prediction analysis: This approach, called supervised classification, establishes the basis for developing a predictive model into which future unknown test objects can be input in order to predict the most likely class membership of the test objects.
5. Hypothesis-driven statistical analysis: Identification of statistically significant changes in gene expression are commonly identified using the t-test, ANOVA, Bayesian method[29] Mann–Whitney test methods tailored to microarray data sets, which take into account multiple comparisons.
6. Network-based methods: Statistical methods that take the underlying structure of gene networks into account, representing either associative or causative interactions or dependencies among gene products.
\subsection{R exercise on Microarray analysis}
Please refer to the GitHub account for all the in-class code repositories for this analysis. The link is as follows:
https://github.com/spawar2/Class-Exercise-1
\subsection{RNA sequencing concepts:}
Why Is RNA-Seq “Better” Than Microarrays?
There are several advantages RNA-seq has over microarrays:
With RNA-seq you can interrogate more than just differential gene expression. Although there are microarrays available for exon-level and microRNA analysis, most users are still interested in basic, probably 3’ biased, differential gene expression. With RNA-seq you can look at coding and non-coding RNA, at splicing and allele specific expression, and possibly soon at full-length cDNA sequences, eliminating the need to infer or assemble isoforms.
Microarrays are also biased, as we have to decide what content to place on the array. Since RNA-seq does not use probes or primers, the data suffer from much lower biases (although I do not mean to say RNA-seq has none).
RNA-seq provides digital data in the form of aligned read-counts, resulting in a very wide dynamic range, improving the sensitivity of detection for rare transcripts.
It is also very cost-competitive to microarrays, as today, between 6-30 samples can be multiplexed in a single Illumina sequencing lane.
Lastly, you can reanalyze an RNA-seq dataset as more information about the transcriptome becomes available. If a paper is published showing an interesting splice-variant in a similar system to the one you work on, then you might want to go back and look at that splicing in your samples; and you’d already have the data to do so.
How Does RNA-Seq Work?
There are many methods for performing an RNA-seq experiment. In fact, the techniques are evolving so rapidly it can be difficult to decide which one to use. A basic choice is between 1) random-primed cDNA synthesis from double-stranded cDNA or 2) RNA-ligation methods. Most people use the first method and then need to make a further choice between a strand-specific protocol and one that is not. The method used most in my lab is Illumina’s TruSeq RNA-seq, which is a random-primed cDNA synthesis non-strand-specific protocol.
Following is the process of generating RNA seq data:
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Once you have a sequencing library, it is sequenced to a specified depth, which is dependent on what you want to do with the data. These reads are aligned to the genome or transcriptome and are counted to determine differential gene expression or further analyzed to determine splicing and isoform expression. Most people are sequencing RNA using paired-end 50-100bp methods. The exception is microRNA sequencing, as this only requires single-end 36bp sequencing in most cases.
Library preparation:
RNA Isolation: RNA is isolated from tissue and mixed with deoxyribonuclease (DNase). DNase reduces the amount of genomic DNA. The amount of RNA degradation is checked with gel and capillary electrophoresis and is used to assign an RNA integrity number to the sample. This RNA quality and the total amount of starting RNA are taken into consideration during the subsequent library preparation, sequencing, and analysis steps.
RNA selection/depletion: To analyze signals of interest, the isolated RNA can either be kept as is, filtered for RNA with 3' polyadenylated (poly(A)) tails to include only mRNA, depleted of ribosomal RNA (rRNA), and/or filtered for RNA that binds specific sequences.
cDNA synthesis: DNA sequencing technology is more mature, so the RNA is reverse transcribed to cDNA. Reverse transcription results in loss of strandedness, which can be avoided with chemical labelling. Fragmentation and size selection are performed to purify sequences that are the appropriate length for the sequencing machine. The RNA, cDNA, or both are fragmented with enzymes, sonication, or nebulizers. Fragmentation of the RNA reduces 5' bias of randomly primed-reverse transcription and the influence of primer binding sites, with the downside that the 5' and 3' ends are converted to DNA less efficiently. Fragmentation is followed by size selection, where either small sequences are removed or a tight range of sequence lengths are selected. Because small RNAs like miRNAs are lost, these are analyzed independently. The cDNA for each experiment can be indexed with a hexamer or octamer barcode, so that these experiments can be pooled into a single lane for multiplexed sequencing.
Following is the workflow for analyzing RNA seq data from raw files:
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Transcriptome assembly:
De novo: This approach does not require a reference genome to reconstruct the transcriptome, and is typically used if the genome is unknown, incomplete, or substantially altered compared to the reference
Genome guided: This approach relies on the same methods used for DNA alignment, with the additional complexity of aligning reads that cover non-continuous portions of the reference genome.
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