";s:4:"text";s:5004:"In the previous section, I explored the bacteria that were present in the samples and in the positive and negative sequencing controls. alphadiv.normal <- read.table ( "data/alpha_div_normalised.txt" , sep = " \t " , header = T) # Split into PD and Simpson metrics PD.normal <- alphadiv.normal[, 1 : 2 ] simp.normal <- alphadiv.normal[, - 2 ] names (PD.normal) <- c ( "SampleID" , "PD_whole_tree" ) names (simp.normal) <- c ( "SampleID" , … Finally, the parameter of Fisher’s log-series can be used as a diversity index: R> alpha <- fisher.alpha… In this case, I needed to do a paired test so as to not violate the independent samples assumption.In my differential abundance analysis (explained in detail in a later section) I selected at random a set of fluid and rinse pairs from the same ear of the same child, with each child contributing only one pair.Again, these samples are from within the same children and I am using pairs which I previously selected.Now I have determined the p-values for the differences between groups, I wanted to present the data as boxplots.Note that I could not manage to put the p-values on the plot (not six different ones at least) so for the final figure in the paper I added the p-values using I used ggplot2 to create two faceted plots.
The underlying data used for calculations is an operational taxonomic unit (OTU) abundance table. The vertical The relation between the number of species and the number of individuals in a random sample of animal population. 1). Now I move on to alpha diversity, which is a measure of the diversity I chose to calculate the diversity within samples using two different metrics; one phylogenetic metric (i.e. This parameter ((Deprecated). Hurlbert, S.H. The nonconcept of species diversity: a critique and … I have run Qiime and have text files of the rarified values (alpha_div_collated files) for the shannon index, PD, observed and chao1 (attached here.) (1971). Common alpha diversity statistics include: Shannon: How difficult it is to predict the identity of a randomly chosen individual. I then arranged these plots next to each other.# Create a metagenomeSeq object for the raw OTU table# OTU taxonomy: taken from OTU table and separated by tabs# Export normalised and logged matrix for use in alpha diversity#write.table(alphanorm, "alphadata_normalised.txt", sep = "\t")# First convert back to biom format (the table was JSON format before)# Test for difference in Simp value (non-parametric)# Read in the list of pairs that I previously chose# Check that the fluids and rinses are in the same order (so the test knows which are paired)# Test for differences between groups using non-parametric paired test# Subset data to fluids and NPS only (not controls)# Split into fluids and NPS (this set of fluids is different to the set used in the fluid/rinse comparison)# Test for differences between groups with non-parametric paired test# Remove unnecessary data that came from mapping file# Specify case group (different to NPS in fluid/NPS comparison)# Specify that this comparison uses a separate set of fluids# Add a variable to show what goes in each facet: we want "case" with "control", "fluid" with "rinse" and "fluid2" with "NPS"# Labelling the axis ticks with the number of samples included The -r flag is the number of permutations to determine a p-value for the m 2 value (i.e. ; Simpson: The probability that two randomly chosen individuals are the same species. No longer supported. The simplest measure of species richness is just the number of species recorded per site. I have a 3 column matrix; plots are made by points based on column 1 and column 2 values, but colored based on column 2 (6 different groups). (1971). For example, if we compare sample A with 100,000 reads to sample B with 10,000 reads, the diversity in sample A will probably be greater just because we have a greater sampling depth. Now I move on to alpha diversity, which is a measure of the diversity I chose to calculate the diversity within samples using two different metrics; one phylogenetic metric (i.e. However, we need to account for variations in sequencing depth so I cannot use the raw counts here. For example, if we compare sample A with 100,000 reads to sample B with 10,000 reads, the diversity in sample A will probably be greater just because we have a greater sampling depth. Select an alpha diversity measure such as Shannon, Fisher, Coverage, GiniSimpson, and InverseSimpson, Default: 'Shannon'The "TreatmentGroup" or similar grouping or column from your metadata to denote sample groups, Default: 'TreatmentGroup'DEPRECATED. takes into account the relatedness of the bacteria) and one non-phylogenetic metric (doesn’t take this into account). I then compared the alpha diversity value between groups of samples.I began with the OTU table that contains only samples above 1499 reads. Each plot contained three facets (the three comparisons) with one plot for PD, one for inverse Simp. ";s:7:"keyword";s:26:"alpha diversity plots in r";s:5:"links";s:5185:"Love Alone Iu,
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