Questions tagged [pca]
Principal Components Analysis. A statistical method used to reduce the dimensionality of a dataset while keeping as much variance in the first principal components as possible. It can be used to visualise samples with many variables in 2-D or 3-D, thus allowing for a visual non-supervised grouping of points.
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Predict population based on PC coordinates
Utilizing a reference panel, I want to assign most likely population label to each individual in the study. Following are the files I have:
Reference panel population labels:
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How to perform PCA on proteomic data set
I'm trying to perform Principal Component Analysis using R on a proteomics dataset.
As the dataset contains a lot missing values I tried different approaches.
I ran PCA using ...
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Question about umap using different numbers of pca components as initialization
I am new to the scRNA-seq field and I have been doing some experiments of visualization of UMAP using different numbers of PCA components for initialization. The process involves projecting scRNA-seq ...
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scRNA: What are good dimensionality reduction/clustering parameters to get biologically plausible groupings?
I've got a moderately large set of PBMCs, over 1M cells. That means I can't easily do a grid search of dimensionality reduction/clustering parameters/methods. Some examples results I'm getting with ...
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Test for differences between groups of samples
Sorry if the answer to this should be obvious.
I have RNA-expression results from 24 samples which can be divided into 6 groups, (wildtype and two different mutants at two different ages) with a total ...
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Project PLINK eigenvector from one group on top of another group
I have 1 .bcf file called with individuals from two groups, one which is my samples, another which is a reference panel. Due to my samples having a limited amount ...
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What is the best approach to do a PCA analysis to distinguish between two groups?
I have two samples A and B and I do have an RNAseq for both of them. Inside each file (for A and B) I do have a set of FPKMs that give me a fold change for each gene (sample vs control). It looks ...
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Using Multi-Dimensional Scaling (MDS) to produce a vector in order to account for patient bias when constructing DGE lists from RNA-seq datasets in R?
I am currently working on my PhD and as part of my thesis, I intend to analyse gene expression within multiple sclerosis (MS) lesions by looking at RNA-seq datasets on Gene Expression Omnibus (https://...
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PCA of methylation-values normalized by coverage
I have started analyzing methylation (EM-seq) data for the first time ~0.8M positions.
In this data set I have 27 samples of 20 patients. I want to perform a PCA of the dataset to check for possible ...
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Should be RT-qPCR values standardized before PCA analysis?
I come from this question of gene exression analysis: Should PCA be standardized for gene expression?
My experiment is based in 151 x 51 (individuals/samples x genes), in which, patients are subjected ...
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Clustering individuals by gene presence/absence
I have a binary matrix with individuals as row names, and gene name as column name.
So, if the gene is present in an individual, we have 1, otherwise 0.
I would like to cluster individuals based on ...
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WCGNA - Relate modules with Y features when the % of variance explained of each eigengen is low
I'm doing a WCGNA analysis (signed network) on microbiome 16S data.
I have transformed counts to centeres log-ratio transformed data (CLR) to address the compositional characteristics of the data and ...
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PCA analysis of samples in a phylogenetic tree
I have a phylogenetic tree.
Each branch ends have samples (s1, s2, ~ s16).
What I want to do is, I want to make PCA analysis plot for each sample.
First I thought each sample has each lineage (...
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Is this RNA seq data good based on the MDS plot
I am analyzing some RNA sequencing data from a collaborator where the effect of some ligands on the transcriptome is being looked at. I am using DESeq2 for my analysis. I am looking to compare each of ...
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Help with performing PCA analysis on data from 3 different datasets of normalized counts data for RNA-seq experiment
So, I have limited knowledge of R but I need to do a PCA analysis of 3 different datasets of gene expression as a result of combined growth or mono-culture growth.
The 3 different datasets I performed ...
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PCA plot in R coloured by sample type
I'm a biologist, not a programmer so please be gentle.
So I have a dataset that looks like
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Is SNPRelate the goto package for PCA of SNP data in R?
I'm trying to evaluate a method that I'm not familiar with. They used SNPRelate. I'm wondering if this is the best / only choice... what other tools would you recommend?
Many thanks,
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Explanation for RNA-seq samples not clustering in PCA as expected
A colleague is analysing RNA-seq data - the study design is 2 treatments, 3 replicates, 3 tissues. In their PCA plot the samples clustered neatly by tissue. Except for two samples - two tissue samples ...
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Biology behind PCA analysis based on SNP
What is the biology behind SNP based PCA analysis to study population structure? I am reading some articles where they used PCA analysis to compare isolates of drosophila that is collected from a ...
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PCA analysis and nucleotide diversity pi
I have 25 individuals' whole-genome sequence (collected from 5 different locations) and after doing a variant calling analysis, I found 2734 SNP+INDELS marker (SNPs + INDELS = 2734 ). Then I plotted ...
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What is a sensible number of gene/observations to explain PCA variance?
I am working with a set of RNASeq dataset. I have about 4000 observations (genes) on 20 samples and plotting a PCA I found the clustering doesn't vary much when I use different number of genes, but ...
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Performing PCA for the samples and for the genes
I have 10 samples from a RNAseq experiment (5 control, 5 disease), I have performed a cluster analysis for the samples and for the genes (4000 genes aprox) to see how they cluster (to see which ...
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data visualization RNAseq : scaling data for PCA and cluster dendogram
I have count data from a RNAseq experiment (2 samples are from normal cells and 3 samples are cells with a disease), and the data is already standardized by trimmed mean of M values (TMM).
I want to ...
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the variation between treatments is less than the variation between replicates in RNA-seq data
I have a set of RNA-seq samples from targeting different proteins in a complex with siRNAs. However, the ...
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RNAseq biological replicates not clustering in PCA plots
I have RNAseq data from 4 samples with 3 biological replicates per sample. I am currently trying to do the differential expression analysis with DESeq2 but the biological replicates will not cluster ...
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about rescaled data (median) [duplicate]
When we have a rescaled data with median (divided by median value)(e.g.,metabolome) and perform natural log transformation, do we need to use autoscaling again to perform PCA or to compute euclidian ...
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How to identify latent variables in single-cell RNA-Seq data
I have a single-cell RNASeq sample, in which I'd like to identify latent variables (e.g. response to stress) that I think might be affecting the clustering.
The approach I was planning to use is to ...
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K means clustering, would PCA be a better option?
I have the data below. I need to use a clustering method to classify them and into categories of "Heterozygotote, Allele 1, Allele 2 and No Call. The values in RFU1 and RFU2 are used to determine the ...
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Pre-filtering genes for Principal Component Analysis
I have a raw counts data-set of 20,502 genes and 137 samples. I want to find out Principal Components which best explain variation between samples in different stages of tumor.
I am new to Machine ...
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Illustrate a 3D visualisation of the three main PCs using plot3d() package in R?
I have a dataset (view/download) here. I need to run PCA on this data set and need to illustrate a 3D visualisation of the three main PCs using plot3d() package.
I have looked for more information ...
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Selecting genes with more contribution from PCA
I have RNA-seq data in response to treatment vs non response; By machine learning I selected three principle components likely can predict the response based on the gene expression. Now I have ...
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Interpreting this PCA plot for RNA-seq
I have RNA-seq from two sequencing batches; Lab technician says that he has run the RNA expression quantification two times in bathes 1 and 2 for example ...
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PCA on large sparse matrix of single cell RNA-seq
I received a large sc-RNA-seq data matrix, as of the nature of sc-RNA-seq compared to bulk-RNA-seq the data matrix is very sparse.
Moreover, due to the fact that this is single cell data the cell ...
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What does PCA mean on GWAS
I understand what GWAS is and I'm able to perform certain tests with the p-values, etc. But what I am having a hard time wrapping my head around is what PCA on GWAS means.
So let's say I have 100,000 ...
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Understanding PCHeatmap outputs
I am currently trying to understand the purpose of these PCHeatmaps - part of the seurat package in R:
All the online documentation I have searched for has only ...
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PCA plot shows big difference but not many differentially expressed genes are found
I got a PCA plot of bulk RNA-seq experiment that looks the following way:
It was generated by the following code:
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3D PCA group labelling
I would like to make a 3D PCA but not sure how to label group wise which i can do for 2D PCA
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Using external list of PCs for clustering
I am going to use principal components (PCs) comes from calcPCA function in URD program for clustering my cells in Seurat; So
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Error in annotating row names as data points in a PCA plot
I made a PCA plot and was trying to plot the eigenvenctors in R so that each data point is actually the sample name. All the samples are in column 1. The following are the R commands I am using:
<...
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Should PCA be standardized for gene expression?
This is a theory/good practice question more than a technical one. If samples are being plotted on a PCA projection of gene expression data, I'm wondering whether it is standard (and if so, why) to ...
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How to interpret PCA output statistically and biologically?
How can I interpret the PCA results statistically for biological data?
I have used FactoMineR and factoextra libraries for PCA
Scripts used:
library(FactoMineR)
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How are Principal Component analyses and Admixture analyses from a genetic alignment different?
How are Principal Component analyses and Admixture analyses from a genetic alignment different?
My understanding is that a PCA will take raw genetic differences across the entire alignment and plot ...
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PCA on genotype matrix with multiple alleles
Consider an m x n genotype matrix of m haploid samples and n SNPs where each value is an allele encoded by an integer (0,1,2,3).
Is there a good/standard way to encode the alleles in order to ...
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Hierarchial PCA Clustering with duplicated row names
I have a matrix object in R of 1500 rows and 20 columns. Based on my own algorithm of analysis, I know there are roughly 7 clusters. In my matrix object, each row is one data point, and is labeled to ...
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Perplexity is too large
I am trying to run {Rtsne}. On pca dataset prepared by using dudi.pca from {ade4}.
For pca calculation I selected 40 components ...
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Filter out PCA outliers automatically
I am new to bioinformatics and PCA. What I am trying to do is to remove bad cells from a dataset that was obtained with scRNA-seq for ...
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