Draws TCR/BCR repertoire figures - V-J chord/circos, CDR3 spectratype, clonal-space stratification, clonal tracking across timepoints, rarefaction/extrapolation curves, overlap heatmaps, and clonotype-similarity networks - and encodes how to read them. Use when choosing between a raw Shannon bar and a rarefaction curve for a diversity comparison; deciding a depth-robust overlap metric (Morisita-Horn) vs a set metric (Jaccard) for a heatmap; setting the distance threshold that defines a clonotype-similarity network; interpreting a Gaussian vs skewed spectratype as polyclonal vs clonally expanded; or laying out clonal-space and clone-tracking plots. Covers VDJtools PlotFancyVJUsage/RarefactionPlot, R circlize and iNEXT, and matplotlib/seaborn recipes.
Standardmäßig ist der Prompt ausgewählt, der zuerst die Quelle prüft. Sie können zu einem direkten Befehl wechseln oder eine lokale Kopie herunterladen.
Quelldateien prüfen
Lesen Sie SKILL.md und alle von SkillsMP angezeigten Begleitdateien, bevor Sie sich für eine Installation entscheiden.
Mit Codex oder Claude installieren Kopieren Sie diesen Prompt, fügen Sie ihn in Codex, Claude oder einen anderen Assistant ein und lassen Sie die Skill-Seite prüfen und installieren.
Ein direkter Befehl überspringt den Prüf-Prompt. Prüfen Sie die Quelle, bevor Sie ihn ausführen.
Draws TCR/BCR repertoire figures - V-J chord/circos, CDR3 spectratype, clonal-space stratification, clonal tracking across timepoints, rarefaction/extrapolation curves, overlap heatmaps, and clonotype-similarity networks - and encodes how to read them. Use when choosing between a raw Shannon bar and a rarefaction curve for a diversity comparison; deciding a depth-robust overlap metric (Morisita-Horn) vs a set metric (Jaccard) for a heatmap; setting the distance threshold that defines a clonotype-similarity network; interpreting a Gaussian vs skewed spectratype as polyclonal vs clonally expanded; or laying out clonal-space and clone-tracking plots. Covers VDJtools PlotFancyVJUsage/RarefactionPlot, R circlize and iNEXT, and matplotlib/seaborn recipes.
Before using code patterns, verify installed versions match. If versions differ:
Python: pip show <package> then help(module.function) to check signatures
R: packageVersion('<pkg>') then ?function_name to verify parameters
CLI: <tool> --version then <tool> --help to confirm flags
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
Note: matplotlib 3.9 removed plt.cm.get_cmap(name, N); use plt.get_cmap(name) and sample it, or matplotlib.colormaps[name].resampled(N). seaborn 0.13 deprecated bare palette= without hue=; assign hue= and legend=False.
Repertoire Visualization
"Plot my TCR/BCR repertoire" -> Render V-J usage, CDR3-length spectratype, clonal-space, clonal tracking, diversity/rarefaction, overlap, and similarity-network figures, and read each one correctly.
CLI: vdjtools PlotFancyVJUsage, vdjtools RarefactionPlot (delegates plotting to R)
Python: matplotlib/seaborn for bespoke figures; numpy multinomial resampling for rarefaction; networkx + rapidfuzz for similarity networks
The governing principle: every figure inherits two upstream choices
A repertoire figure is only as valid as the numbers behind it, and those numbers depend on two decisions made before any plot is drawn:
The clonotype definition. A clonotype = CDR3 (nucleotide OR amino acid) + optionally V and J. Amino-acid CDR3 collapses convergent recombination (many nt rearrangements -> one aa clonotype), inflating apparent sharing and deflating richness; nt CDR3 is more conservative (Venturi et al. 2006 PNAS 103:18691). Every count on every axis shifts with this choice, so it must be stated in the figure and held constant across all samples in a comparison.
The sequencing depth. Richness, Shannon, clonality, and set-overlap (Jaccard, shared-clonotype counts) are strictly increasing functions of reads sequenced - the rare-clone tail never saturates. Diversity, rarefaction, and overlap figures are comparable across samples ONLY after depth normalization: downsample all samples to a common depth, or read rarefaction curves at a shared x. Comparing raw values across libraries of unequal depth measures sequencing effort, not biology (Chao et al. 2014 Ecol Monogr 84:45; Greiff et al. 2015 Genome Med 7:49). This caveat governs the diversity, rarefaction, and overlap recipes below.
Choosing the figure and reading it
Figure
What it reveals
How to compare correctly
V-J chord/circos
Combinatorial V-J pairing bias within one sample
Descriptive per sample; never compare raw usage across primer sets/platforms (primer bias masquerades as biology)
CDR3 spectratype
Clonal structure: Gaussian length distribution = polyclonal/naive, spikes = expansion
Weight by frequency to see expansions; by unique clonotypes to see underlying diversity
Clonal-space / proportion
Fraction of repertoire held by rare vs expanded clones
Bin by clone frequency (strata), not raw count; robust to depth if frequency-based
Clonal tracking
Expansion/contraction/persistence of clones over time
Downsample timepoints to common depth first; an "absent" clone is often a sampling zero
Rarefaction/extrapolation
Diversity comparison done right (interpolate to common depth)
Read all curves at a shared x; extrapolate at most 2-3x observed depth
Overlap heatmap
Pairwise repertoire similarity
Use Morisita-Horn (depth-robust) on depth-normalized samples; Jaccard is depth-biased
Similarity network
Clusters of related CDR3s (candidate specificity groups)
Structure depends entirely on the distance threshold; state it and test sensitivity
V-J usage: chord/circos and heatmap
The chord/circos shows which V and J segments pair, weighted by clonotype count, within one sample.
VDJtools route (delegates plotting to R; requires RInstall once):
CDR3 spectratype: length distribution reveals clonal structure
The spectratype is a histogram of CDR3 length. A roughly Gaussian, bell-shaped distribution indicates a diverse, polyclonal (naive-like) repertoire; skew or discrete spikes at particular lengths indicate clonal expansion(s). The classic immunoscope spectratype (and VDJtools CalcSpectratype) bins nucleotide length, where in-frame clones sit 3 nt apart and the periodicity is part of the readout; amino-acid length is a common simplification - state which is plotted. Weighting by read/UMI frequency shows expansions; weighting by unique clonotypes shows the underlying diversity - the two views can look opposite, so label which is plotted.
Clonal-space / proportion: how much repertoire the big clones hold
Bin clones into frequency strata (Rare / Small / Medium / Large / Hyperexpanded) and stack their summed frequency. Because strata are defined on frequency, this view is comparatively depth-robust and shows clonal-space homeostasis at a glance. A treemap of top clones is an alternative when individual dominant clones matter.
import numpy as np
defplot_clonal_space(clone_df, sample_col='sample'):
# Frequency-based strata (immunarch homeo convention); frequency makes this depth-robust
edges = [0, 1e-4, 1e-3, 1e-2, 1e-1, 1.0]
labels = ['Rare', 'Small', 'Medium', 'Large', 'Hyperexpanded']
clone_df = clone_df.copy()
clone_df['stratum'] = pd.cut(clone_df['frequency'], bins=edges, labels=labels)
space = clone_df.groupby([sample_col, 'stratum'], observed=True)['frequency'].sum().unstack(fill_value=0)
fig, ax = plt.subplots(figsize=(8, 5))
space[labels].plot(kind='bar', stacked=True, ax=ax, colormap='viridis')
ax.set_ylabel('Fraction of repertoire')
return fig
Clonal tracking across timepoints
Track individual clone frequencies over an ordered sample set (vaccination, infection, therapy). A line plot of the top clones, or an alluvial/stream for the same data, shows expansion, contraction, and persistence. Downsample timepoints to a common depth before declaring contraction - a clone scored absent at one timepoint is frequently below the sampling floor, not truly gone.
defplot_clone_tracking(clone_df, top_n=10, clone_col='cdr3_aa', time_col='timepoint'):
top = clone_df.groupby(clone_col)['frequency'].sum().nlargest(top_n).index
fig, ax = plt.subplots(figsize=(9, 5))
for clone in top:
d = clone_df[clone_df[clone_col] == clone].sort_values(time_col)
ax.plot(d[time_col], d['frequency'], marker='o', label=clone[:12])
ax.set_xlabel('Timepoint'); ax.set_ylabel('Clone frequency')
ax.legend(bbox_to_anchor=(1.02, 1), loc='upper left', fontsize=7)
return fig
Rarefaction/extrapolation: the correct way to compare diversity
A bare bar of Shannon (or observed richness) across samples of unequal depth is misleading - it plots depth as much as biology. The defensible comparison is a rarefaction/extrapolation curve: interpolate each sample down to (and extrapolate modestly above) a shared depth, then read diversity at a common x. iNEXT computes this for Hill numbers q=0/1/2 with confidence intervals (Hsieh et al. 2016 Methods Ecol Evol 7:1451; Chao et al. 2014 Ecol Monogr 84:45).
R route (gold standard, gives CIs):
library(iNEXT)# count_list: named list of per-sample integer clonotype-count vectors
out <- iNEXT(count_list, q =c(0,1,2), datatype ='abundance')
ggiNEXT(out, type =1)# diversity vs sample size, read at a common x
Python resampling route (interpolation by multinomial subsampling) when iNEXT is unavailable:
defrarefaction_curve(counts, depths, reps=20, rng=None):
# Interpolate observed richness by drawing 'm' reads without-replacement-like via multinomial
rng = rng or np.random.default_rng(0)
counts = np.asarray(counts, dtype=float)
p = counts / counts.sum()
total = int(counts.sum())
richness = []
for m in depths:
if m > total: # interpolation only; do not extrapolate past observed depth here
richness.append(np.nan); continue
obs = [np.count_nonzero(rng.multinomial(m, p)) for _ inrange(reps)]
richness.append(np.mean(obs))
return richness
Overlap heatmap: pick a depth-robust metric
An N-by-N similarity heatmap summarizes pairwise repertoire overlap. The metric choice is the decision: Morisita-Horn is abundance-weighted and near-invariant to sample size, so it is the default across unequal depths; Jaccard (presence/absence) is dominated by the shallower sample's depth and should not be compared across unequal-depth pairs. Compute the matrix in vdjtools-analysis on depth-normalized samples, then render it here. Label the metric in the title.
defplot_overlap_heatmap(overlap_matrix, metric='Morisita-Horn'):
fig, ax = plt.subplots(figsize=(7, 6))
sns.heatmap(overlap_matrix, annot=True, fmt='.2f', cmap='YlOrRd', vmin=0, vmax=1, square=True, ax=ax)
ax.set_title(f'Repertoire overlap ({metric})') # state the metric; Jaccard is depth-biasedreturn fig
Clonotype-similarity network
Nodes are CDR3s, edges connect sequences within a chosen distance, and clusters are candidate specificity groups. The network structure depends entirely on the threshold: too loose chains distinct clones together, too tight fragments real groups. State the threshold and metric, and test sensitivity before interpreting clusters. Same-length CDR3s with Hamming distance is the conservative default; Levenshtein allows indels.
import networkx as nx
from rapidfuzz.distance import Levenshtein
defbuild_similarity_network(clone_df, max_norm_dist=0.15, clone_col='cdr3_aa'):
# normalized_similarity in [0,1]; edge when 1 - similarity <= threshold. Structure is threshold-dependent.
clones = clone_df[clone_col].unique()
g = nx.Graph()
g.add_nodes_from(clones)
for i, a inenumerate(clones):
for b in clones[i + 1:]:
if1 - Levenshtein.normalized_similarity(a, b) <= max_norm_dist:
g.add_edge(a, b)
return g
Common Errors
Symptom
Cause
Fix
Diversity "differs" between groups but tracks library size
Bare Shannon/richness bar across unequal depth
Downsample to common depth or plot rarefaction curves read at a shared x (iNEXT/RarefactionPlot)
Overlap heatmap shows huge differences driven by one shallow sample
Jaccard/shared-count on raw, unequal-depth counts
Use Morisita-Horn on depth-normalized samples; label the metric
Network clusters change completely on re-run with a new cutoff
Shugay M, et al. VDJtools: unifying post-analysis of T cell receptor repertoires. PLoS Comput Biol 2015; 11(11):e1004503. (PlotFancyVJUsage, RarefactionPlot, overlap metrics.)
Chao A, Gotelli NJ, Hsieh TC, Sander EL, Ma KH, Colwell RK, Ellison AM. Rarefaction and extrapolation with Hill numbers. Ecol Monogr 2014; 84(1):45-67. (Interpolation/extrapolation framework.)
Hsieh TC, Ma KH, Chao A. iNEXT: an R package for rarefaction and extrapolation of species diversity (Hill numbers). Methods Ecol Evol 2016; 7(12):1451-1456. (Rarefaction/extrapolation curves with CIs.)
Greiff V, et al. A bioinformatic framework for immune repertoire diversity profiling. Genome Med 2015; 7:49. (Hill-number diversity profiling of repertoires.)
Venturi V, et al. Sharing of T cell receptors in antigen-specific responses is driven by convergent recombination. PNAS 2006; 103(49):18691-18696. (aa-clonotype sharing inflated by convergence.)
Chao A. Nonparametric estimation of the number of classes in a population. Scand J Stat 1984; 11:265-270. (Chao1 richness estimator.)
